Tracking Compressor Health Before It Becomes a Failure
An AI model classified rotary compressor operating states and predicted degradation transitions, giving maintenance teams a warning before critical equipment failure.
ITChamps developed an AI-enabled equipment-state degradation and predictive maintenance solution for a critical rotary compressor at a leading petrochemical company, predicting transitions from desirable to undesirable operating states.
The Challenge
Equipment reliability mattered more than a simple failure alarm
The organization operated critical rotary compressors where reliability and availability were essential for uninterrupted plant operations, and needed to identify early degradation rather than waiting for an actual failure.
- Rotary compressors were critical to uninterrupted plant operations.
- Waiting for an actual equipment failure was too costly and disruptive.
- The organization needed to identify the compressor's current operating state, not just binary failure.
- A method was needed to predict transitions from a desirable to an undesirable state.
The Solution
Classify operating state and predict degradation transitions
The solution analyzed historical equipment and operational data, classified compressor behavior into desirable and undesirable states, and predicted the likelihood of transitioning between them.
Analyzed historical compressor operating and performance data.
Classified compressor behavior into desirable and undesirable operating states.
Analyzed historical transitions between desirable and undesirable states.
Transformed equipment and operational parameters into predictive features.
Predicted likely transitions from desirable to undesirable states, and recovery transitions following intervention.
Identified likely contributing causes of degradation, enabling teams to intervene before a critical condition.
Results
Measured business impact
Reduction in unplanned downtime on the rotary compressor.
Increase in Mean Time Between Failures.
Improvement in compressor availability.
Reduction in failure-related losses.
Highlights
- Enabled monitoring the trajectory of compressor health, not just failure detection.
- Identified likely causes and predicted undesirable state transitions early.
- Allowed corrective action before significant equipment deterioration.
- Reduced the risk of production interruption on critical equipment.
Services and technology
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