Predictive maintenance for critical production equipment

This project focused on implementing a Predictive Maintenance solution at Toyota's Cambridge plant, with a critical focus on a single production crane. The plant's operational tempo is exceptionally high, with an output rate of approximately one Corolla per minute.

A breakdown of this single crane was not merely an inconvenience but a severe financial risk, with millions of dollars lost for every hour of downtime. The primary technical hurdle was creating a reliable model to predict device failure, which required synthesizing a vast and diverse set of operational data that was previously unsynced and siloed.

The data streams that needed to be incorporated included environmental readings (e.g., humidity sensors), operational diagnostics (e.g., vibration and noise measurements), historical records (e.g., past maintenance reports), and production data (e.g., defects observed earlier or later in the production line).

Our solution centered on the unique ability to rapidly blend hundreds of parameters from multiple disparate data sources. This robust data pipelining and management infrastructure allowed us to quickly identify correlations and stressors being placed on the crane.

The key innovation was the ability to automate the model generation process. Instead of manually testing every model, the system could rapidly ingest the synchronized data, determine which models were the most accurate and consistent at identifying precursor signs of maintenance issues, and select the best one.

By finding reliable leading indicators and predictors of failure, the solution enabled Toyota to schedule maintenance proactively and accurately, moving away from reactive repairs and significantly reducing unplanned device downtime.