Condition based Predictive Maintenance with M2M Big data Analytics

Manufacturers and operators employ array of maintenance strategies, all of which can be broadly categorized as below:

  • Corrective Maintenance
  • Preventive Maintenance
  • Predictive Maintenance

Corrective maintenance is the classic Run-to-Failure reactive maintenance that has no special maintenance plan in place. The machine is assumed to be fit unless proven otherwise.

  • Cons:
    • High risk of collateral damage and secondary failure
    • High production downtime
    • Overtime labor and high cost of spare parts
  • Pros:
    • Machines are not over-maintained
    • No overhead of condition monitoring or planning costs

Preventive maintenance (PM) is the popular periodic maintenance strategy that is actively employed by all manufacturers and operators in the industry today. An optimal breakdown window is pre-calculated (at the time of component design or installation, based on a wide range of models describing the degradation process of equipment, cost structure and admissible maintenance etc.), and a preventive maintenance schedule is laid out. Maintenance is carried-out on those periodic intervals, assuming that the machine is going to break otherwise.

  • Cons:
    • Calendar-based maintenance: Machines are repaired when there are no faults
    • There will still be unscheduled breakdowns
  • Pros:
    • Fewer catastrophic failures and lesser collateral damage
    • Greater control over spare-parts and inventory
    • Maintenance is performed in controlled manner, with a rough estimate of costs well-known ahead of time

Predictive Maintenance (PdM) is an alternative to the above two that employs predictive analytics over real-time data collected (streamed) from parts of the machine to a centralized processor that detects variations in the functional parameters and detects anomalies that can potentially lead to breakdowns. The real-time nature of the analytics helps identify the functional breakdowns long before they happen but soon after their potential cause arises.

  • Pros:
    • Unexpected breakdown is reduced or even completely eliminated
    • Parts are ordered when needed and maintenance performed when convenient
    • Equipment life and there by its utilization is maximized
  • Cons:
    • Investment costs for implementing the condition-based monitoring (CBM) system
    • Additional skills might be required to effectively use the CBM system effectively

Predictive maintenance, also known as Condition Based Maintenance (CBM) differs from preventive maintenance by basing maintenance need on the actual condition of the machine rather than on some preset schedule or assumptions.

For example, a typical preventive maintenance strategy demands automobile operators to change the engine oil after every 3,000 to 5,000 Miles traveled. No concern is given to the actual condition of vehicle or performance capability of the oil. If on the other hand, the operator has some way of knowing or somehow measuring the actual condition of the vehicle and the oil lubrication properties, he/she gains the potential to extend the vehicle usage and postpone oil change until the vehicle has traveled 10,000 Miles, or perhaps pre-pone the oil change in case of any abnormality.

Predictive analytics with M2M telematics provides such deep insights into the machine operations and full functionality status – giving rise to optimal maintenance schedules with improved machine availability.

The enormous data streamed in realtime from sensors attached to the machine/vechile is processed with big data architectures to enable anamoly detection and failure prediction in real time.

The demo video above showcases the Predictive Maintenance in operation. It demonstrates computing the Failure rate and MTTF from realtime operating conditions of machine.

More details can be found in my M2M Telematics & Predictive Analytics paper.