Machine Anomaly Root Cause Analysis Using Reconstruction Errors
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Solution Overview
Problem
Current machine monitoring methods detect anomalies but fail to consistently identify their causes, leading to time-consuming manual root cause analysis and potential machine downtimes, especially in large systems where complex sensor data relationships are involved.
Innovation Solution
A method using machine learning-based models that reconstruct operating parameters to determine reconstruction errors, allowing for consistent cause analysis by selecting operating parameters with significant errors, thereby pinpointing the likely faulty machine part.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning methods are used to detect anomalies, then anomaly detection capability is improved, but consistent root cause analysis capability deteriorates
Solution Approach 1:
The patent segments the root cause analysis by computing individual reconstruction errors for each operating parameter separately. Instead of treating the anomaly detection as a single black-box output, the method breaks down the overall reconstruction error into parameter-specific components, allowing consistent identification of which specific parameter contributes most to the anomaly.
2Loss of information
If manual root cause analysis is performed using expert knowledge, then cause identification is possible, but analysis time increases
Solution Approach 1:
The system performs self-service root cause analysis by automatically computing reconstruction errors for each operating parameter and identifying the parameter with the largest error. This eliminates the need for manual expert analysis, as the system autonomously determines the likely cause of the anomaly based on the model's reconstruction performance.
3Measurement precision
If separate anomaly detection and root cause analysis methods are used, then detection accuracy is improved, but result consistency deteriorates
Solution Approach 1:
The patent merges anomaly detection and root cause analysis into a unified framework. The same autoencoder model used for anomaly detection is leveraged to compute parameter-specific reconstruction errors for root cause analysis. This ensures that both functions use the same learned representations and consistency in their results.
4Loss of information
If numerous sensors are used to capture operating parameters, then monitoring comprehensiveness is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant information from the complex sensor data by identifying the single operating parameter with the largest reconstruction error. Instead of processing and analyzing all sensor data equally, the method extracts the key diagnostic information needed for root cause identification, simplifying the analysis while maintaining comprehensiveness.
Data Source
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AI summary
The invention relates to a method for analyzing the cause of an anomalous state of a machine in a model-based monitoring method that detects anomalous states of the machine by determining an anomaly measure, wherein the model models machine states by reconstructing at least two operating parameters of the machine, which serve as input values of the model, as output values, wherein in the model the output values have deviations from the respective corresponding input values and the model has been trained by machine learning to minimize the deviations.If an anomalous condition is detected by the monitoring procedure, the procedure includes: determining a reconstruction error for each of the operating parameters based on the input value and the output value of the respective operating parameter reconstructed by the model, selecting at least one operating parameter based on the reconstruction errors, and outputting the at least one selected operating parameter.