Step-by-step: How to Implement Predictive Maintenance (PdM)

For an Organization looking to integrate PdM as part of your Digital Transformation or a Reliability & Maintenance Professional who is upskilling to stay relevant in the job market, you will find the below step-by-step guide on implementing Predictive Maintenance informative.

• Step 1: Identify the critical assets based on business factors.
• Step 2: Formulate a maintenance plan in accordance with your Asset Management Program. Assess if Predictive Maintenance is the effective strategy.
• Step 3: Analyze failure modes to find the detectable failure mechanism. Collect historical data (if available) and Live data on the failure mechanism from the system.
• Step 4: Build a Machine Learning model through the steps: Preprocessing, Identifying health indicators, Algorithm training, Testing & Validation.
• Step 5: Deploy the model on live-data infrastructure to make predictions. Detect Anomalies (outliers from the normal operation – Potential Failure), Estimate Remaining Useful Life (time left to a Functional Failure), and Classify Fault Types.
• Step 6: Notify the Maintenance & Reliability team about the prediction results. Plan & Schedule maintenance work with the support of Inventory, Personnel, and other specified Resources.
• Step 7: Monitor the performance of Maintenance Work, Equipment Reliability, and PdM ML Model individually using metrics.
• Step 8: Collect feedback and iterate Steps 2 to 7 for continuous improvement. Replicate if equipment conditions change.

Predictive Maintenance (PdM) is widely regarded as the apex of maintenance strategy and Machine Learning’s (ML) prediction power is proving to be a competitive advantage. Combining both to carry out ML-based Predictive Maintenance makes the organization truly world-class.

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