Predictive maintenance is one of the most demanded applications of predictive analytics with unarguable benefits including tremendous amount of cost savings. This playbook aims at providing a reference for predictive maintenance solutions with the emphasis on major use cases. It is prepared to give the reader an understanding of the most common business scenarios of predictive maintenance, challenges of qualifying business problems for such solutions, data required to solve these business problems, predictive modeling techniques to build solutions using such data and best practices with sample solution architectures. It also describes the specifics of the predictive models developed such as feature engineering, model development and performance evaluation. In essence, this playbook brings together the business and analytical guidelines needed for a successful development and deployment of predictive maintenance solutions. These guidelines are prepared to help the audience create an initial solution using Cortana Intelligence Suite and specifically Azure Machine Learning as a starting point in their long-term predictive maintenance strategy. The documentation regarding Cortana Intelligence Suite and Azure Machine Learning can be found in Cortana Analytics and Azure Machine Learning pages.
For a technical guide to implementing this Solution Template, see Technical guide to the Cortana Intelligence Solution Template for predictive maintenance. To download a diagram that provides an architectural overview of this template, see Architecture of the Cortana Intelligence Solution Template for predictive maintenance.
Playbook overview and target audience
This playbook is organized to benefit both technical and non-technical audience with varying backgrounds and interests in predictive maintenance space. The playbook covers both high-level aspects of the different types of predictive maintenance solutions and details of how to implement them. The content is balanced to cater both to the audience who are only interested in understanding the solution space and the type of applications as well as those who are looking to implement these solutions and are hence interested in the technical details.
Majority of the content in this playbook does not assume prior data science knowledge or expertise. However, some parts of the playbook will require somewhat familiarity with data science concepts to be able to follow implementation details. Introductory level data science skills are required to fully benefit from the material in those sections.
The first half of the playbook covers an introduction to predictive maintenance applications, how to qualify a predictive maintenance solution, a collection of common use cases with the details of the business problem, the data surrounding these use cases and the business benefits of implementing these predictive maintenance solutions. These sections don’t require any technical knowledge in the predictive analytics domain.
In the second half of the playbook, we cover the types of predictive modeling techniques for predictive maintenance applications and how to implement these models through examples from the use cases outlined in the first half of the playbook. This is illustrated by going through the steps of data preprocessing such as data labeling and feature engineering, model selection, training/testing and performance evaluation best practices. These sections are suitable for technical audience.
Predictive maintenance in IoT
The impact of unscheduled equipment downtime can be extremely destructive for businesses. It is critical to keep field equipment running in order to maximize utilization and performance and by minimizing costly, unscheduled downtime. Simply, waiting for the failure to occur is not affordable in today’s business operations scene. To remain competitive, companies look for new ways to maximize asset performance by making use of the data collected from various channels. One important way to analyze such information is to utilize predictive analytic techniques that use historical patterns to predict future outcomes. One of the most popular of these solutions is called Predictive Maintenance which can generally be defined as but not limited to predicting possibility of failure of an asset in the near future so that the assets can be monitored to proactively identify failures and take action before the failures occur. These solutions detect failure patterns to determine assets that are at the greatest risk of failure. This early identification of issues helps deploy limited maintenance resources in a more cost-effective way and enhance quality and supply chain processes.
With the rise of the Internet of Things (IoT) applications, predictive maintenance has been gaining increasing attention in the industry as the data collection and processing technologies has matured enough to generate, transmit, store and analyze all kinds of data in batches or in real-time. Such technologies enable easy development and deployment of end-to-end solutions with advanced analytics solutions, with predictive maintenance solutions providing arguably the largest benefit.
Business problems in the predictive maintenance domain range from high operational risk due to unexpected failures and limited insight into the root cause of problems in complex business environments. The majority of these problems can be categorized to fall under the following business questions:
- What is the probability that a piece of equipment fails in the near future?
- What is the remaining useful life of the equipment?
- What are the causes of failures and what maintenance actions should be performed to fix these issues?
By utilizing predictive maintenance to answer these questions, businesses can:
- Reduce operational risk and increase rate of return on assets by spotting failures before they occurred
- Reduce unnecessary time-based maintenance operations and control cost of maintenance
- Improve overall brand image, eliminate bad publicity and resulting lost sales from customer attrition.
- Lower inventory costs by reducing inventory levels by predicting the reorder point
- Discover patterns connected to various maintenance problems
Predictive maintenance solutions can provide businesses with key performance indicators such as health scores to monitor real-time asset condition, an estimate of the remaining lifespan of assets, recommendation for proactive maintenance activities and estimated order dates for replacement of parts.
Qualification criteria for predictive maintenance
It is important to emphasize that not all use cases or business problems can be effectively solved by predictive maintenance. Important qualification criteria include whether the problem is predictive in nature, that a clear path of action exists in order to prevent failures when they are detected beforehand and most importantly, data with sufficient quality to support the use case is available. Here, we focus on the data requirements for building a successful predictive maintenance solution.
When building predictive models, we use historical data to train the model which can then recognize hidden patterns and further identify these patterns in the future data. These models are trained with examples described by their features and the target of prediction. The trained model is expected to make predictions on the target by only looking at the features of the new examples. It is crucial that the model capture the relationship between features and the target of prediction. In order to train an effective machine learning model, we need training data which includes features that actually have predictive power towards the target of prediction meaning the data should be relevant to the prediction goal to expect accurate predictions.
For example, if the target is to predict failures of train wheels, the training data should contain wheel-related features (e.g. telemetry reflecting the health status of wheels, the mileage, car load, etc.). However, if the target is to predict train engine failures, we probably need another set of training data that has engine-related features. Before building predictive models, we expect the business expert to understand the data relevancy requirement and provide the domain knowledge that is needed to select relevant subsets of data for the analysis.
There are three essential data sources we look for when qualifying a business problem to be suitable for a predictive maintenance solution:
- Failure History: Typically, in predictive maintenance applications, failure events are very rare. However, when building predictive models that predict failures, the algorithm needs to learn the normal operation pattern as well as the failure pattern through the training process. Hence, it is essential that the training data contains sufficient number of examples in both categories in order to learn these two different patterns. For that reason, we require that data has sufficient number of failure events. Failure events can be found in maintenance records and parts replacement history or anomalies in the training data can also be used as failures as identified by the domain experts.
- Maintenance/Repair History: An essential source of data for predictive maintenance solutions is the detailed maintenance history of the asset containing information about the components replaced, preventive maintenance activates performed, etc. It is extremely important to capture these events as these affect the degradation patterns and absence of this information causes misleading results.
- Machine Conditions: In order to predict how many more days (hours, miles, transactions, etc.) a machine lasts before it fails, we assume the machine’s health status degrades over time during its operation. Therefore, we expect the data to contain time-varying features that capture this aging pattern and any anomalies that leads to degradation. In IoT applications, the telemetry data from different sensors represent one good example. In order to predict if a machine is going to fail within a time frame, ideally the data should capture degrading trend during this time frame before the actual failure event.
Additionally, we require data that is directly related to the operating conditions of the target asset of prediction. The decision of target is based on both business needs and data availability. Taking the train wheel failure prediction as an example, we may predict "if the wheel is going to have a failure" or "if the whole train is going have a failure". The first one targets a more specific component whereas the second one targets failure of the train. The second one is a more general question that requires a lot more dispersed data elements than the first one, making it harder to build a model. Conversely, trying to predict wheel failures just by looking at the high-level train condition data may not be feasible as it does not contain information at the component level. In general, it is more sensible to predict specific failure events than more general ones.
One common question that is usually asked about failure history data is "How many failure events are required to train a model and how many is considered as "enough"? There is no clear answer to that question as in many predictive analytics scenarios, it is usually the quality of the data that dictates what is acceptable. If the dataset does not include features that are relevant to failure prediction, then even if there are many failure events, building a good model may not be possible. However, the rule of thumb is that the more the failure events the better the model is and a rough estimate of how many failure examples are required is a very context and data-dependent measure. This issue is discussed in the section for handling imbalanced datasets where we propose methods to cope with the problem of not having enough failures.
Sample use cases
This section focuses on a collection of predictive maintenance use cases from several industries such as Aerospace, Utilities and Transportation. Each subsection drills into the use-cases collected from these areas and discusses a business problem, the data surrounding the business problem and the benefits of a predictive maintenance solution.
Use Case 1: Flight delay and cancellations
Business problem and data sources
One of the major business problems that airlines face is the significant costs that are associated with flights being delayed due to mechanical problems. If the mechanical failures cannot be repaired, flights may even be canceled. This is extremely costly as delays create problems in scheduling and operations, causes bad reputation and customer dissatisfaction along with many other problems. Airlines are particularly interested in predicting such mechanical failures in advance so that they can reduce flight delays or cancellations. The goal of the predictive maintenance solution for these cases is to predict the probability of an aircraft being delayed or canceled, based on relevant data sources such as maintenance history and flight route information. The two major data sources for this use case are the flight legs and page logs. Flight leg data includes data about the flight route details such as the date and time of departure and arrival, departure and arrival airports, etc. Page log data includes a series of error and maintenance codes that are recorded by the maintenance personnel.
Business value of the predictive model
Using the available historical data, a predictive model was built using a multi-classification algorithm to predict the type of mechanical issue which results in a delay or cancellation of a flight within the next 24 hours. By making this prediction, necessary maintenance actions can be taken to mitigate the risk while an aircraft is being serviced and thus prevent possible delays or cancellations. Using Azure Machine Learning web service, the predictive models can seamlessly and easily be integrated into airlines’ existing operating platforms.
Use Case 2: Aircraft component failure
Business problem and data sources
Aircraft engines are very sensitive and expensive pieces of equipment and engine part replacements are among the most common maintenance tasks in the airline industry. Maintenance solutions for airlines require careful management of component stock availability, delivery and planning. Being able to gather intelligence on component reliability leads to substantial reduction on investment costs. The major data source for this use case is telemetry data collected from a number of sensors in the aircraft providing information on the condition of the aircraft. Maintenance records were also used to identify when component failures occurred and replacements were made.
Business value of the predictive model
A multi-class classification model was built that predicts the probability of a failure due to a certain component within the next month. By employing these solutions, airlines can reduce component repair costs, improve component stock availability, reduce inventory levels of related assets and improve maintenance planning.
Use Case 1: ATM cash dispense failure
Business problem and data sources
Executives in asset intensive industries often state that primary operational risk to their businesses is unexpected failures of their assets. As an example, failure of machinery such as ATMs in banking industry is a very common problem that occurs frequently. These types of problems make predictive maintenance solutions very desirable for operators of such machinery. In this use-case, prediction problem is to calculate the probability that an ATM cash withdrawal transaction gets interrupted due to a failure in the cash dispenser such as a paper jam or a part failure. Major data sources for this case are sensor readings that collect measurements while cash notes are being dispensed and also maintenance records collected over time. Sensor data included sensor readings per each transaction completed and also sensor readings per each note dispensed. The sensor readings provided measurements such as gaps between notes, thickness, note arrival distance etc. Maintenance data included error codes and repair information. These were used to identify failure cases.
Business value of the predictive model
Two predictive models were built to predict failures in the cash withdrawal transactions and failures in the individual notes dispensed during a transaction. By being able to predict transaction failures beforehand, ATMs can be serviced proactively to prevent failures from occurring. Also, with note failure prediction, if a transaction is likely to fail before it is complete due to a note dispense failure, it may be best to stop the process and warn the customer for incomplete transaction rather than waiting for the maintenance service to arrive after the error occurs which may lead to larger customer dissatisfaction.
Use Case 2: Wind turbine failures
Business problem and data sources
With the raise of environmental awareness, wind turbines have become one of the major sources of energy generation and they usually cost millions of dollars. One of the key components of wind turbines is the generator motor which is equipped with many sensors that helps to monitor turbine conditions and status. The sensor readings contain valuable information which can be used to build a predictive model to predict critical Key Performance Indicators (KPIs) such as mean time to failure for components of the wind turbine. Data for this use case comes from multiple wind turbines that are located in three different farm locations. Measurements from close to a hundred sensors from each turbine were recorded every 10 seconds for one year. These readings include measurements such as temperature, generator speed, turbine power and generator winding.
Business value of the predictive model
Predictive models were built to estimate remaining useful life for generators and temperature sensors. By predicting the probability of failure, maintenance technicians can focus on suspicious turbines that are likely to fail soon to complement time-based maintenance regimes. Additionally, predictive models bring insight to the level of contribution for different factors to the probability of a failure which helps business to have a better understanding of the root cause of the problems.
Use Case 3: Circuit breaker failures
Business problem and data sources
Electricity and gas operations that include generation, distribution and sale of electrical energy require significant amount of maintenance to ensure power lines are operational at all times to guarantee delivery of energy to households. Failure of such operations is critical as almost every entity is effected by power problems in the regions that they occur. Circuit breakers are critical for such operations as they are a piece of equipment that cut electrical current in case of problems and short circuits to prevent any damage to power lines from happening. The business problem for this use case is to predict circuit breaker failures given maintenance logs, command history and technical specifications.
Three major data sources for this case are maintenance logs that include corrective, preventive and systematic actions, operational data that includes automatic and manual commands send to circuit breakers such as for open and close actions and technical specification data about the properties of each circuit breaker such as year made, location, model, etc.
Business value of the predictive model
Predictive maintenance solutions help reduce repair costs and increase the lifecycle of equipment such as circuit breakers. These models also help improve the quality of the power network since models provide warnings ahead of time that reduce unexpected failures which lead to fewer interruptions to the service.
Use Case 4: Elevator door failures
Business problem and data sources
Most large elevator companies typically have millions of elevators running around the world. To gain a competitive edge, they focus on reliability which is what matters most to their customers. Drawing on the potential of the Internet of Things, by connecting their elevators to the cloud and gathering data from elevator sensors and systems, they are able to transform data into valuable business intelligence which vastly improves operations by offering predictive and preemptive maintenance that is not something that is available to the competitors yet. The business requirement for this case is to provide a knowledge base predictive application that predicts the potential causes of door failures. The required data for this implementation consists of three parts which are elevator static features (e.g. identifiers, contract maintenance frequency, building type, etc.), usage information (e.g. number of door cycles, average door close time, etc.) and failure history (i.e. historical failure records and their causes).
A multiclass logistic regression model was built with Azure Machine Learning to solve the prediction problem, with the integrated static features and usage data as features, and the causes of historical failure records as class labels. This predictive model is consumed by an app on a mobile device which is used by field technicians to help improve working efficiency. When a technician goes on site to repair an elevator, he/she can refer to this app for recommended causes and best courses of maintenance actions to fix the elevator doors as fast as possible.
Transportation and logistics
Use Case 1: Brake disc failures
Business problem and data sources
Typical maintenance policies for vehicles include corrective and preventive maintenance. Corrective maintenance implies that the vehicle is repaired after a failure has occurred which can cause a severe inconvenience to the driver as a result of an unexpected malfunction and the time wasted on a visit to mechanic. Most vehicles are also subject to a preventive maintenance policy, which requires performing certain inspections at a schedule which does not take into account the actual condition of the car subsystems. None of these approaches are successful in fully eliminating problems. The specific use case here is brake disc failure prediction based on data collected through sensors installed in the tire system of a car which keeps track of historical driving patterns and other conditions that the car is exposed to. The most important data source for this case is the sensor data that measure, for instance, accelerations, braking patterns, driving distances, velocity, etc. This information, coupled with other static information such as car features, help build a good set of predictors that can be used in a predictive model. Another set of essential information is the failure data which is inferred from the part order database (used to keep the spare part order dates and quantities as cars are being serviced in the dealerships).
Business value of the predictive model
The business value of a predictive approach here is substantial. A predictive maintenance system can schedule a visit to the dealer based on a predictive model. The model can be based on sensory information that is representing the current condition of the car and the driving history. This approach can minimize the risk of unexpected failures, which may as well occur before the next periodic maintenance. It can also reduce the amount of unnecessary preventive maintenance. Driver can proactively be informed that a change of parts might be necessary in a few weeks and supply the dealer with that information. The dealer could then prepare an individual maintenance package for the driver in advance.
Use Case 2: Subway train door failures
Business problem and data sources
One of the major reasons of delays and problems on subway operations is door failures of train cars. Predicting if a train car may have a door failure, or being able to forecast the number of days till the next door failure, is extremely important foresight. It provides the opportunity to optimize train door servicing and reduce the train's down time.
Three sources of data in this use-case are
- train event data, which is the historical records of train events,
- maintenance data such as maintenance types, work order types, and priority codes,
- records of failures.
Business value of the predictive model
Two models were built to predict next day failure probability using binary classification and days till failure using regression. Similar to the earlier cases, the models create tremendous opportunity to improve quality of service and increase customer satisfaction by complementing the regular maintenance regimes.
The common data elements for predictive maintenance problems can be summarized as follows:
- Failure history: The failure history of a machine or component within the machine.
- Maintenance history: The repair history of a machine, e.g. error codes, previous maintenance activities or component replacements.
- Machine conditions and usage: The operating conditions of a machine e.g. data collected from sensors.
- Machine features: The features of a machine, e.g. engine size, make and model, location.
- Operator features: The features of the operator, e.g. gender, past experience.
It is possible and usually the case that failure history is contained in maintenance history such as in the form of special error codes or order dates for spare parts. In those cases, failures can be extracted from the maintenance data. Additionally, different business domains may have a variety of other data sources that influence failure patterns which are not listed here exhaustively. These should be identified by consulting the domain experts when building predictive models.
Some examples of above data elements from use cases are:
Failure history: fight delay dates, aircraft component failure dates and types, ATM cash withdrawal transaction failures, train/elevator door failures, brake disk replacement order dates, wind turbine failure dates and circuit breaker command failures.
Maintenance history: Flight error logs, ATM transaction error logs, train maintenance records including maintenance type, short description etc. and circuit breaker maintenance records.
Machine conditions and usage: Flight routes and times, sensor data collected from aircraft engines, sensor readings from ATM transactions, train events data, sensor readings from wind turbines, elevators and connected cars.
Machine features: Circuit breaker technical specifications such as voltage levels, geolocation or car features such as make, model, engine size, tire types, production facility etc.
Given the above data sources, the two main data types we observe in predictive maintenance domain are temporal data and static data. Failure history, machine conditions, repair history, usage history almost always come with time-stamps indicating the time of collection for each piece of data. Machine features and operator features in general are static since they usually describe the technical specifications of machines or operator’s properties. It is possible for these features to change over time and if so they should be treated as time stamped data sources.
Merging data sources
Before getting into any type of feature engineering or labeling process, we need to first prepare our data in the form required to create features from. The ultimate goal is to generate a record for each time unit for each asset with its features and labels to be fed into the machine learning algorithm. In order to prepare that clean final data set, some pre-processing steps should be taken. First step is to divide the duration of data collection into time units where each record belongs to a time unit for an asset. Data collection can also be divided into other units such as actions, however for simplicity we use time units for the rest of the explanations.
The measurement unit for time can be in seconds, minutes, hours, days, months, cycles, miles or transactions depending on the efficiency of data preparation and the changes observed in the conditions of the asset from a time unit to the other or other factors specific to the domain. In other words, the time unit does not have to be the same as the frequency of data collection as in many cases data may not show any difference from one unit to the other. For example, if temperature values were being collected every 10 seconds, picking a time unit of 10 seconds for the whole analysis inflates the number of examples without providing any additional information. Better strategy would be to use average over an hour as an example.
Example generic data schemas for the possible data sources explained in the earlier section are:
Maintenance records: These are the records of maintenance actions performed. The raw maintenance data usually comes with an Asset ID and time stamp with information about what maintenance activities have been performed at that time. In case of such raw data, maintenance activities need to be translated into categorical columns with each category corresponding to a maintenance action type. The basic data schema for maintenance records would include asset ID, time and maintenance action columns.
Failure records: These are the records that belong to the target of prediction which we call failures or failure reason. These can be specific error codes or events of failures defined by specific business condition. In some cases, data includes multiple error codes some of which correspond to failures of interest. Not all errors are target of prediction so other errors are usually used to construct features that may correlate with failures. The basic data schema for failure records would include asset ID, time and failure or failure reason columns if reason is available.
Machine conditions: These are preferably real-time monitoring data about the operating conditions of the data. For example, for door failures, door opening and closing times are good indicators about the current condition of doors. The basic data schema for machine conditions would include asset ID, time and condition value columns.
Machine and operator data: Machine and operator data can be merged into one schema to identify which asset was operated by which operator along with asset and operator properties. For example, a car is usually owned by a driver with attributes such as age, driving experience etc. If this data changes over time, this data should also include a time column and should be treated as time varying data for feature generation. The basic data schema for machine conditions would include asset ID, asset features, operator ID and operator features.
The final table before labeling and feature generation can be generated by left joining machine conditions table with failure records on Asset ID and time fields. This table can then be joined with maintenance records on Asset ID and Time fields and finally with machine and operator features on Asset ID. The first left join leaves null values for failure column when machine is in normal operation, these can be imputed by an indicator value for normal operation. This failure column is used to create labels for the predictive model.
The first step in modeling is feature engineering. The idea of feature generation is to conceptually describe and abstract a machine’s health condition at a given time using historical data that was collected up to that point in time. In the next section, we provide an overview of the type of techniques that can be used for predictive maintenance and how the labeling is done for each technique. The exact technique that should be used depends on the data and business problem. However, the feature engineering methods described below can be used as baseline for creating features. Below, we discuss lag features that should be constructed from data sources that come with time-stamps and also static features created from static data sources and provide examples from the use cases.
As mentioned earlier, in predictive maintenance, historical data usually comes with timestamps indicating the time of collection for each piece of data. There are many ways of creating features from the data that comes with timestamped data. In this section, we discuss some of these methods used for predictive maintenance. However, we are not limited by these methods alone. Since feature engineering is considered to be one of the most creative areas of predictive modeling, there could be many other ways to create features. Here, we provide some general techniques.
For each record of an asset, we pick a rolling window of size "W" which is the number of units of time that we would like to compute historical aggregates for. We then compute rolling aggregate features using the W periods before the date of that record. Some example rolling aggregates can be rolling counts, means, standard deviations, outliers based on standard deviations, CUSUM measures, minimum and maximum values for the window. Another interesting technique is to capture trend changes, spikes and level changes using algorithms that detect anomalies in data using anomaly detection algorithms.
For demonstration, see Figure 1 where we represent sensor values recorded for an asset for each unit of time with the blue lines and mark the rolling average feature calculation for W=3 for the records at t1 and t2 which are indicated by orange and green groupings respectively.
Figure 1. Rolling aggregate features
As examples, for aircraft component failure, sensor values from last week, last three days and last day were used to create rolling means, standard deviation and sum features. Similarly, for ATM failures, both raw sensor values and rolling means, median, range, standard deviations, number of outliers beyond three standard deviations, upper and lower CUMSUM features were used.
For flight delay prediction, counts of error codes from last week were used to create features. For train door failures, counts of the events on the last day, counts of events over the previous 2 weeks and variance of counts of events of the previous 15 days were used to create lag features. Same counting was used for maintenance-related events.
Additionally, by picking a W that is very large (ex. years), it is possible to look at the whole history of an asset such as counting all maintenance records, failures etc. up until the time of the record. This method was used for counting circuit breaker failures for the last three years. Also for train failures, all maintenance events were counted to create a feature to capture the long-term maintenance effects.
For each labeled record of an asset, we pick a window of size "W-k" where k is the number or windows of size "W" that we want to create lag features for. "k" can be picked as a large number to capture long-term degradation patterns or a small number to capture short-term effects. We then use k tumbling windows W-k , W-(k-1), …, W-2 , W-1 to create aggregate features for the periods before the record date and time (see Figure 2). These are also rolling windows at the record level for a time unit which is not captured in Figure 2 but the idea is the same as in Figure 1 where t2 is also used to demonstrate the rolling effect.
Figure 2. Tumbling aggregate features
As an example, for wind turbines, W=1 and k=3 months were used to create lag features for each of the last 3 months using top and bottom outliers.
These are technical specifications of the equipment such as manufacture date, model number, location, etc. While lag features are mostly numeric in nature, static features usually become categorical variables in the models. As an example, circuit breaker properties such as voltage, current and power specifications along with transformer types, power sources etc. were used. For brake disc failures, the type of tire wheels such as if they are alloy or steel were used as some of the static features.
During feature generation, some other important steps such as handling missing values and normalization should be performed. There are numerous methods of missing value imputation and also data normalization which is not discussed here. However, it is beneficial to try different methods to see if an increase in prediction performance is possible.
The final feature table after feature engineering steps discussed in the earlier section should resemble the following example data schema when time unit is a day:
|Asset ID||Time||Feature Columns||Label|
Predictive Maintenance is a very rich domain often employing business questions which may be approached from many different angles of the predictive modeling perspective. In the next sections, we provide main techniques that are used to model different business questions that can be answered with predictive maintenance solutions. Although there are similarities, each model has its own way of constructing labels which are described in detail. As an accompanying resource, you can refer to the predictive maintenance template that is included in the sample experiments provided within Azure Machine Learning. The links to the online material for this template are provided in the resources section. You can see how some of the feature engineering techniques discussed above and the modeling technique that is described in the next sections are applied to predict aircraft engine failures using Azure Machine Learning.
Binary classification for predictive maintenance
Binary Classification for predictive maintenance is used to predict the probability that equipment fails within a future time period. The time period is determined by and based on business rules and the data at hand. Some common time periods are minimum lead time required to purchase spare parts to replace likely to damage components or time required to deploy maintenance resources to perform maintenance routines to fix the problem that is likely to occur within that time period. We call this future horizon period "X".
In order to use binary classification, we need to identify two types of examples which we call positive and negative. Each example is a record that belongs to a time unit for an asset conceptually describing and abstracting its operating conditions up to that time unit through feature engineering using historical and other data sources described earlier. In the context of binary classification for predictive maintenance, positive type denotes failures (label 1) and negative type denotes normal operations (label = 0) where labels are of type categorical. The goal is to find a model that identifies each new example as likely to fail or operate normally within the next X units of time.
In order to create a predictive model to answer the question "What is the probability that the asset fails in the next X units of time?", labeling is done by taking X records prior to the failure of an asset and labeling them as "about to fail" (label = 1) while labeling all other records as "normal" (label =0). In this method, labels are categorical variables (see Figure 3).
Figure 3. Labeling for binary classification
For flight delays and cancellations, X is picked as one day to predict delays in the next 24 hours. All flights that are within 24 hours before failures were labeled as 1s. For ATM cash dispense failures, two binary classification models were built to predict the failure probability of a transaction in the next 10 minutes and also to predict the probability of failure in the next 100 notes dispensed. All transactions that happened within the last 10 minutes of the failure are labeled as 1 for the first model. And all notes dispensed within the last 100 notes of a failure were labeled as 1 for the second model. For circuit breaker failures, the task is to predict the probability that the next circuit breaker command fails in which case X is chosen to be one future command. For train door failures, the binary classification model was built to predict failures within the next 7 days. For wind turbine failures, X was chosen as 3 months.
Wind turbine and train door cases are also used for regression analysis to predict remaining useful life using the same data but by utilizing a different labeling strategy which is explained in the next section.
Regression for predictive maintenance
Regression models in predictive maintenance are used to compute the remaining useful life (RUL) of an asset which is defined as the amount of time that the asset is operational before the next failure occurs. Same as binary classification, each example is a record that belongs to a time unit "Y" for an asset. However, in the context of regression, the goal is to find a model that calculates the remaining useful life of each new example as a continuous number which is the period of time remaining before the failure. We call this time period some multiple of Y. Each example also has a remaining useful life which can be calculated by measuring the amount of time remaining for that example before the next failure.
Given the question "What is the remaining useful life of the equipment? ", labels for the regression model can be constructed by taking each record prior to the failure and labeling them by calculating how many units of time remain before the next failure. In this method, labels are continuous variables (See Figure 4).
Figure 4. Labeling for regression
Different than binary classification, for regression, assets without any failures in the data cannot be used for modeling as labeling is done in reference to a failure point and its calculation is not possible without knowing how long the asset survived before failure. This issue is best addressed by another statistical technique called Survival Analysis. We are not going to discuss Survival Analysis in this playbook because of the potential complications that may arise when applying the technique to predictive maintenance use cases that involve time-varying data with frequent intervals.
Multi-class classification for predictive maintenance
Multi-class classification for predictive maintenance can be used to
predict two future outcomes. The first one is to assign an asset to one of
the multiple possible periods of time to give a range of time to failure
for each asset. The second one is to identify the likelihood of failure in a
future period due to one of the multiple root causes. That allows
maintenance personnel who are equipped with this knowledge to
handle the problems in advance. Another multi-class modeling
technique focuses on determining the most likely root
cause of a given a failure. This allows
recommendations to be given for the top maintenance actions
to be taken in order to fix a failure.
By having a ranked list of root causes and associated repair actions,
technicians can be more effective in taking their first repair actions after failures.
Given the two questions which are "What is the probability that an asset fails in the next "aZ" units of time where "a" is the number of periods" and "What is the probability that the asset fails in the next X units of time due to problem "Pi" where "i" is the number of possible root causes, labeling is done in the following way for these to techniques.
For the first question, labeling is done by taking aZ records prior to the failure of an asset and labeling them using buckets of time (3Z, 2Z, Z) as their labels while labeling all other records as "normal" (label =0). In this method, label is categorical variable (See Figure 5).
Figure 5. Labeling for multiclass classification for failure time prediction
For the second question, labeling is done by taking X records prior to the failure of an asset and labeling them as "about to fail due to problem Pi" (label = Pi) while labeling all other records as "normal" (label =0). In this method, labels are categorical variables (See Figure 6).
Figure 6. Labeling for multiclass classification for root cause prediction
The model assigns a failure probability due to each Pi as well as the probability of no failure. These probabilities can be ordered by magnitude to allow prediction of the problems that are most likely to occur in the future. Aircraft component failure use case was structured as a multiclass classification problem. This enables the prediction of the probabilities of failure due to two different pressure valve components occurring within the next month.
For recommending maintenance actions after failures, labeling does not require a future horizon to be picked. This is because the model is not predicting failure in the future but it is just predicting the most likely root cause once the failure has already happened. Elevator door failures fall into the third case where the goal is to predict the cause of the failure given historical data on operating conditions. This model is then used to predict the most likely root causes after a failure has occurred. One key benefit of this model is that it helps inexperienced technicians to easily diagnose and fix problems that would otherwise need years’ worth of experience.
Training, validation and testing methods in predictive maintenance
In predictive maintenance, similar to any other solution space containing timestamped data, the typical training and testing routine needs to take account the time varying aspects to better generalize on unseen future data.
Many machine learning algorithms depend on a number of hyperparameters that can change model performance significantly. The optimal values of these hyperparameters are not computed automatically when training the model, but should be specified by data scientist. There are several ways of finding good values of hyperparameters. The most common one is "k-fold cross-validation" which splits the examples randomly into "k" folds. For each set of hyperparameters values, learning algorithm is run k times. At each iteration, the examples in the current fold are used as a validation set, the rest of the examples are used as a training set. The algorithm trains over training examples and the performance metrics are computed over validation examples. At the end of this loop for each set of hyperparameter values, we compute average of the k performance metric values and choose hyperparameter values that have the best average performance.
As mentioned before, in predictive maintenance problems, data is recorded as a time series of events that come from several data sources. These records can be ordered according to the time of labeling a record or an example. Hence, if we split the dataset randomly into training and validation set, some of the training examples are later in time than some of validation examples. This results in estimating future performance of hyperparameter values based on the data that arrived before model was trained. These estimations might be overly optimistic, especially if time-series are not stationary and change their behavior over time. As a result, chosen hyperparameter values might be sub-optimal.
A better way of finding good values of hyperparameters is to split the examples into training and validation set in a time-dependent way, such that all validation examples are later in time than all training examples. Then, for each set of values of hyperparameters we train the algorithm over training set, measure model’s performance over the same validation set and choose hyperparameter values that show the best performance. When time-series data is not stationary and evolves over time, the hyperparameter values chosen by train/validation split lead to a better future "model's performance than with the values chosen randomly by cross-validation.
The final model is generated by training a learning algorithm over entire data using the best hyperparameter values that are found by using training/validation split or cross-validation.
Testing for model performance
After building a model we need to estimate its future performance on new data. The simplest estimate could be the performance of the model over the training data. But this estimate is overly optimistic, because the model is tailored to the data that is used to estimate performance. A better estimate could be a performance metric of hyperparameter values computed over the validation set or an average performance metric computed from cross-validation. But for the same reasons as previously stated, these estimations are still overly optimistic. We need more realistic approaches for measuring model performance.
One way is to split the data randomly into training, validation and test sets. The training and validation sets are used to select values of hyperparameters and train the model with them. The performance of the model is measured over the test set.
Another way which is relevant to predictive maintenance, is to split the examples into training, validation and test sets in a time-dependent way, such that all test examples are later in time than all training and validation examples. After the split, model generation and performance measurement are done the same as described earlier.
When time-series are stationary and easy to predict both approaches generate similar estimations of future performance. But when time-series are non-stationary and/or hard to predict, the second approach will generate more realistic estimates of future performance than the first one.
As a best practice, in this section we take a closer look at how to implement time-dependent split. We describe a time-dependent two-way split between training and test sets, however exactly the same logic should be applied for time-dependent split for training and validation sets.
Suppose we have a stream of timestamped events such as measurements from various sensors. Features of training and test examples, as well as their labels, are defined over timeframes that contain multiple events. For example, for binary classification, as described in Feature Engineering and Modeling Techniques sections, features are created based on the past events and labels are created based on future events within "X" units of time in the future. Thus, the labeling timeframe of an example comes later then the timeframe of its features. For time- dependent split, we pick a point in time at which we train a model with tuned hyperparameters by using historical data up to that point. To prevent leakage of future labels that are beyond the training cut-off into training data, we choose the latest timeframe to label training examples to be X units before the training cut-off date. In Figure 7, each solid circle represents a row in the final feature data set for which the features and labels are computed according to the method described above. Given that, the figure shows the records that should go into training and testing sets when implementing time-dependent split for X=2 and W=3:
Figure 7. Time-dependent split for binary classification
The green squares represent the records belonging to the time units that can be used for training. As explained earlier, each training example in the final feature table is generated by looking at past 3 periods for feature generation and 2 future periods for labeling before the training day cut-off. We do not use examples in the training set when any part of the 2 future periods for that example is beyond the training cut-off since we assume that we do not have visibility beyond the training cut-off. Due to that constraint, black examples represent the records of the final labeled dataset that should not be used in the training data set. These records won’t be used in testing data either since they are before the training cut-off and their labeling timeframes partially depend on the training timeframe which should not be the case as we would like to completely separate labeling timeframes for training and testing to prevent label information leakage.
This technique allows for overlap in the data used for feature generation between training and testing examples that are close to the training cut-off. Depending on data availability, an even more severe separation can be accomplished by not using any of the examples in the test set that are within W time units of the training cut-off.
From our work, we found that regression models used for predicting remaining useful life are more severely affected by the leakage problem and using a random split leads to extreme overfitting. Similarly, in regression problems, the split should be such that records belonging to assets with failures before training cut off should be used for the training set and assets that have failures after the cut-off should be used for testing set.
As a general method, another important best practice for splitting data for training and testing is to use a split by asset ID so that none of the assets that were used in training are used for testing since the idea of testing is to make sure that when a new asset is used to make predictions on, the model provides realistic results.
Handling imbalanced data
In classification problems, if there are more examples of one class than of the others, the data is said to be imbalanced. Ideally, we would like to have enough representatives of each class in the training data to be able to differentiate between different classes. If one class is less than 10% of the data, we can say that the data is imbalanced and we call the underrepresented dataset minority class. Drastically, in many cases we find imbalanced datasets where one class is severely underrepresented compared to others for example by only constituting 0.001% of the data points. Class imbalance is a problem in many domains including fraud detection, network intrusion and predictive maintenance where failures are usually rare occurrences in the lifetime of the assets which make up the minority class examples.
In case of class imbalance, performance of most standard learning algorithms is compromised as they aim to minimize the overall error rate. For example, for a data set with 99% negative class examples and 1% positive class examples, we can get 99% accuracy by simply labeling all instances as negative. However, this misclassifies all positive examples so the algorithm is not a useful one although the accuracy metric is very high. Consequently, conventional evaluation metrics such as overall accuracy on error rate, are not sufficient in case of imbalanced learning. Other metrics, such as precision, recall, F1 scores and cost adjusted ROC curves are used for evaluations in case of imbalanced datasets which is discussed in the Evaluation Metrics section.
However, there are some methods that help remedy class imbalance problem. The two major ones are sampling techniques and cost sensitive learning.
The use of sampling methods in imbalanced learning consists of modification of the dataset by some mechanisms in order to provide a balanced dataset. Although there are a lot of different sampling techniques, most straight forward ones are random oversampling and under sampling.
Simply stated, random oversampling is selecting a random sample from minority class, replicating these examples and adding them to training data set. This increases the number of total examples in minority class and eventually balance the number of examples of different classes. One danger of oversampling is that multiple instances of certain examples can cause the classifier to become too specific leading to overfitting. This would result in high training accuracy but performance on the unseen testing data may be very poor. Conversely, random under sampling is selecting a random sample from majority class and removing those examples from training data set. However, removing examples from majority class may cause the classifier to miss important concepts pertaining to the majority class. Hybrid sampling where minority class is oversampled and majority class is under sampled at the same time is another viable approach. There are many other more sophisticated sampling techniques are available and effective sampling methods for class imbalance is a popular research area receiving constant attention and contributions from many channels. Use of different techniques to decide on the most effective ones is usually left to the data scientist to research and experiment and are highly dependent on the data properties. Additionally, it is important to make sure that sampling methods are applied only to the training set but not the test set.
Cost sensitive learning
In predictive maintenance, failures which constitute the minority class are of more interest than normal examples and thus the focus is on the performance of the algorithm on failures is usually the focus. This is commonly referred as unequal loss or asymmetric costs of misclassifying elements of different classes wherein incorrectly predicting a positive as negative can cost more than vice versa. The desired classifier should be able to give high prediction accuracy over the minority class, without severely compromising on the accuracy for the majority class.
There are several ways this can be achieved. By assigning a high cost to misclassification of the minority class, and trying to minimize the overall cost, the problem of unequal loses can be effectively dealt with. Some machine learning algorithms use this idea inherently such as SVMs (Support Vector Machines) where cost of positive and negative examples can be incorporated during training time. Similarly, boosting methods are used and usually show good performance in case of imbalanced data such as boosted decision tree algorithms.
As mentioned earlier, class imbalance causes poor performance as algorithms tend to classify majority class examples better in expense of minority class cases as the total misclassification error is much improved when majority class is labeled correctly. This causes low recall rates and becomes a larger problem when the cost of false alarms to the business is very high. Accuracy is the most popular metric used for describing a classifier’s performance. However as explained above accuracy is ineffective and do not reflect the real performance of a classifier’s functionality as it is very sensitive to data distributions. Instead, other evaluation metrics are used to assess imbalanced learning problems. In those cases, precision, recall and F1 scores should be the initial metrics to look at when evaluating predictive maintenance model performance. In predictive maintenance, recall rates denote how many of the failures in the test set were correctly identified by the model. Higher recall rates mean the model is successful in catching the true failures. Precision metric relates to the rate of false alarms where lower precision rates correspond to higher false alarms. F1 score considers both precision and recall rates with best value being 1 and worst being 0.
Moreover, for binary classification, decile tables and lift charts are very informative when evaluating performance. They focus only on the positive class (failures) and provide a more complex picture of the algorithm performance than what is seen by looking at just a fixed operating point on the ROC (Receiver Operating Characteristic) curve. Decile tables are obtained by ordering the test examples according to their predicted probabilities of failures computed by the model before thresholding to decide on the final label. The ordered samples are then grouped in deciles (i.e. the 10% samples with largest probability and then 20%, 30% and so on). By computing the ratio between true positive rate of each decile and its random baseline (i.e. 0.1, 0.2 ..) one can estimate how the algorithm performance changes at each decile. Lift charts are used to plot decile values by plotting decile true positive rate versus random true positive rate pairs for all deciles. Usually, the first deciles are the focus of the results since here we see the largest gains. First deciles can also be seen as representative for "at risk" when used for predictive maintenance.
Sample solution architecture
When deploying a predictive maintenance solution, we are interested in an end to end solution that provides a continuous cycle of data ingestion, data storage for model training, feature generation, prediction and visualization of the results along with an alert generating mechanism such as an asset monitoring dashboard. We want a data pipeline that provides future insights to the user in a continuous automated manner. An example predictive maintenance architecture for such an IoT data pipeline is illustrated in Figure 8 below. In the architecture, real-time telemetry is collected into an Event Hub which stores streaming data. This data is ingested by stream analytics for real-time processing of data such as feature generation. The features are then used to call the predictive model web service and results are displayed on the dashboard. At the same time, ingested data is also stored in an historical database and merged with external data sources such as on-premise data bases to create training examples for modeling. Same data warehouses can be used for batch scoring of the examples and storing of the results which can again be used to provide predictive reports on the dashboard.
Figure 8. Example solution architecture for predictive maintenance
For more information about each of the components of the architecture please refer to Azure documentation.