Predictive maintenance

The nature and degree of asphalt deterioration is analyzed for predictive maintenance of roadways. See more at Pavement condition index.

Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach claims more cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item.[1][2]

The main appeal of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. By taking into account measurments of the state of the equipment, maintenance work can be better planned (spare parts, people, etc.) and what would have been "unplanned stops" are transformed to shorter and fewer "planned stops", thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.

Predictive maintenance differs from preventive maintenance because it does take into account the current condition of equipment (with measurements), instead of average or expected life statistics, to predict when maintenance will be required. Machine Learning approaches are adopted for the forecasting of its future states.[3]

Some of the main components that are necessary for implementing predictive maintenance are data collection and preprocessing, early fault detection, fault detection, time to failure prediction, and maintenance scheduling and resource optimization.[4] Predictive maintenance has been considered to be one of the driving forces for improving productivity and one of the ways to achieve "just-in-time" in manufacturing.[5]

  1. ^ Goriveau, Rafael; Medjaher, Kamal; Zerhouni, Noureddine (2016-11-14). From prognostics and health systems management to predictive maintenance 1 : monitoring and prognostics. ISTE Ltd and John Wiley & Sons, Inc. ISBN 978-1-84821-937-3.
  2. ^ Mobley, R. Keith (2002). An introduction to predictive maintenance (2nd ed.). Butterworth-Heinemann. pp. 4–6. ISBN 978-0-7506-7531-4.
  3. ^ Susto, Gian Antonio (2015). "Machine Learning for Predictive Maintenance: A Multiple Classifier Approach". IEEE Transactions on Industrial Informatics. 11 (3): 812–820. doi:10.1109/TII.2014.2349359. S2CID 18888927.
  4. ^ Amruthnath, Nagdev; Gupta, Tarun (February 2018). "Fault Class Prediction in Unsupervised Learning using Model-Based Clustering Approach". ResearchGate. doi:10.13140/rg.2.2.22085.14563. Retrieved 19 January 2022.
  5. ^ Amruthnath, Nagdev; Gupta, Tarun (April 2018). "A Research Study on Unsupervised Machine Learning Algorithms for Fault Detection in Predictive Maintenance". ResearchGate. doi:10.13140/rg.2.2.28822.24648. Retrieved 19 January 2022.