Predicting Machine Failures Before They Cost Downtime
An AI model reading machine alarms and production data predicted likely failures and their probable causes, cutting unplanned downtime by roughly a fifth.
ITChamps developed an AI-enabled predictive maintenance solution for a valve manufacturer, using machine alarms and production data to predict potential failures and their likely causes ahead of breakdowns.
The Challenge
Alarms existed, but no systematic way to predict failures or their causes
Although machine alarms and production data were available, the organization needed a systematic way to identify patterns preceding failures and understand the likely causes of machine breakdowns.
- Machine failures and unplanned downtime were difficult to predict.
- Alarm and production data existed but weren't systematically analyzed for failure patterns.
- Maintenance was largely reactive, acting only after a failure occurred.
- The team needed to understand likely causes, not just predict failure timing.
The Solution
Predict failures, identify likely causes, and intervene early
The solution integrated machine alarms, production data, and historical failure information, analyzed the relationship between operating conditions and failures, and delivered AI-based failure prediction with likely-cause insight.
Consolidated machine alarms, production data, and historical failure information.
Identified historical alarm sequences and operating conditions preceding machine failures.
Engineered alarm, production, and machine-behavior features for predictive modelling.
Predicted the likelihood of potential machine failures and identified machines with increased downtime probability.
Analyzed relationships between alarm patterns, production conditions, and historical failures to identify probable causes.
Gave maintenance teams early insight to investigate and address likely causes before major failure.
Results
Measured business impact
Reduction in unplanned machine downtime.
Increase in Mean Time Between Failures.
Improvement in machine availability.
Reduction in failure-related losses.
Highlights
- Enabled a transition from reactive maintenance toward predictive maintenance.
- Identified both the potential failure and its likely cause.
- Allowed maintenance teams to act before failures caused prolonged downtime.
- Improved equipment reliability and maintenance planning.
Services and technology
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