Ensemble Autoencoder Training for Drift-Resilient Anomaly Detection
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Solution Overview
Problem
Existing PHM systems struggle with integrating real-world data from complex engineering assets, particularly in maritime vessels, due to system drift, confounding variables, and under-represented data sets, leading to inaccurate anomaly detection and failure prediction.
Innovation Solution
A method and system for training an autoencoder using a combination of clustering, KDtree algorithm, and bagging to handle under-represented data, incorporating Mahalanobis distance for anomaly scoring and decomposition, enabling real-time anomaly detection and failure prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional PHM algorithms are applied to real-world engineering assets, then the system can operate in complex environments, but the detection accuracy deteriorates due to system drift and data irregularities
Solution Approach 1:
The system dynamically adapts to changing operating conditions by continuously updating the autoencoder model with new data from different operating envelopes. The model evolves over time to accommodate system drift and environmental changes, maintaining detection accuracy despite varying operational contexts.
Solution Approach 2:
The system changes key parameters including training data composition (incorporating under-represented failure modes), model architecture (using ensemble autoencoders), and evaluation metrics (Mahalanobis distance) to improve detection accuracy while maintaining adaptability to complex environments.
2Measurement precision
If more comprehensive training data is collected to cover all operating envelopes, then the model becomes more accurate, but the data collection time and complexity increase
Solution Approach 1:
The system performs preliminary actions by proactively collecting and storing data from all possible operating envelopes during normal operation, even before failures occur. This preparatory data collection enables the model to be trained on comprehensive datasets that include rare failure modes, improving accuracy without extending the actual deployment time.
Solution Approach 2:
The system creates synthetic copies of failure scenarios by using data augmentation techniques and transfer learning from similar systems. This allows the model to learn from multiple operating envelopes and failure modes without requiring physical access to every possible failure scenario, reducing data collection time while maintaining accuracy.
3Reliability
If the autoencoder model is trained on under-represented data sets, then the detection of rare failure modes improves, but the training data quality and consistency deteriorate
Solution Approach 1:
The system merges multiple data sources including under-represented failure modes, normal operating data, and synthetic generated data into a unified training dataset. This combination balances the representation of rare failure modes with overall data consistency, allowing the model to detect rare events while maintaining stable performance across all operating conditions.
Solution Approach 2:
The system introduces intermediary processing steps including data normalization, feature engineering, and anomaly filtering that mediate between raw inconsistent data and the training model. These intermediary layers standardize the data composition while preserving the distinctive characteristics of under-represented failure modes, improving both reliability and consistency.
4Measurement precision
If multiple cloned autoencoders are aggregated to handle different data sets, then the detection accuracy for diverse failure modes improves, but the system complexity increases
Solution Approach 1:
The system segments the overall detection task into multiple specialized autoencoder models, each trained on specific operating envelopes or failure modes. This segmentation allows each model to specialize in detecting particular types of anomalies, improving overall accuracy while maintaining manageable complexity through modular architecture and independent training of each segment.
Data Source
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AI summary
The invention relates to a system and method for detecting anomalous system behaviour. The system comprises a plurality of sensors and a trained autoencoder. The method of training comprises: obtaining training data and test data comprising multiple data records for at least one engineering asset which corresponds to the engineering asset whose behaviour is to be classified, wherein the data records comprise a plurality of sensor readings for the engineering asset; fitting the autoencoder to the obtained training data; running the test data through the encoder of the fitted autoencoder to obtain encodings of the test data; generating a plurality of data sets from the obtained encodings, wherein the generated plurality of data sets include under-represented data sets; cloning the fitted autoencoder to create a cloned autoencoder for each of the generated plurality of data sets; and aggregating the cloned autoencoders to form an over-arching autoencoder. The method further comprises calculating an error data set between the training data and data reconstructed by the over-arching auto encoder; obtaining, using the calculated error data set, estimated parameters for calculating an anomaly score for each data record, wherein the anomaly score is selected from a Mahalanobis distance and a squared Mahalanobis distance; and estimating, using the calculated error set, parameters for calculating a decomposition of the anomaly score to identify a contribution from each sensor reading to the anomaly score.