Predictive Maintenance

Use case identification

The predictive maintenance use case identification covers the equipment criticality, the failure modes, the data availability, the model feasibility, and the business value.

Data preparation

The data preparation covers the data collection, the data cleaning, the data labeling, the feature engineering, and the data partitioning.

Model development

The model development covers the model selection, the training, the validation, the testing, and the model documentation.

Integration with CMMS

The integration with the CMMS (Computerized Maintenance Management System) covers the work order creation, the technician notification, the model feedback, and the performance tracking.

How to use this page

Use this Predictive Maintenance page as a planning checkpoint before vendor selection, architecture review, validation scoping or implementation sequencing. The strongest next step is to compare the guidance with your current SOPs, system inventory, batch records, data flows and QA review routines so the discussion starts from evidence instead of assumptions.

Evidence to prepare

For Predictive Maintenance, prepare the records, owners, risks and decision criteria linked to use case identification, data preparation, model development, integration with cmms. Useful evidence includes current process maps, interface lists, audit trail expectations, exception workflows, data retention rules and the business reason for changing the current operating model.

Frequently asked questions