Context-Aware Feature Generation for Asset Fault Prediction
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Solution Overview
Problem
Traditional asset monitoring systems fail to detect anomalies effectively due to their inability to consider various operation conditions and interdependencies between sensor data from different assets, leading to missed anomalies and inefficient fault detection.
Innovation Solution
The system generates features that account for different operation conditions and interdependencies of sensor data, using machine learning models to determine anomaly scores and predict faults, enabling intelligent detection and prediction of asset anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor alarm limits are set with respect to normal operating range values, then the system is simple to operate, but it fails to detect anomalies under various operation conditions
Solution Approach 1:
The patent implements dynamic anomaly detection by training machine learning models on historical sensor data that captures various operation conditions. The system adapts to different operational states (normal, startup, shutdown, transient) by learning from historical patterns, allowing anomaly limits to dynamically adjust based on the current operational context rather than using fixed thresholds.
Solution Approach 2:
The system changes the parameters used for anomaly detection from simple fixed thresholds to complex multi-parameter machine learning models. These models incorporate multiple sensor readings, historical patterns, and operational context parameters to dynamically determine anomaly limits, significantly improving detection accuracy while accepting increased system complexity.
2Reliability
If traditional monitoring considers only individual sensor values, then the system is easier to implement, but it misses interdependencies between sensors
Solution Approach 1:
The patent merges multiple sensor data streams and combines them with historical operational data into a unified machine learning model. The system integrates readings from multiple sensors, process variables, and operational context into a comprehensive anomaly detection framework that captures interdependencies between different components and sensors.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it detects anomalies, identifies fault patterns, captures sensor interdependencies, and adapts to different operational conditions. This multi-functional approach improves reliability by comprehensively analyzing relationships between sensors while managing complexity through a unified modeling framework.
3Adaptability or versatility
If fixed alarm limits are used for all operation conditions, then the system requires minimal configuration, but it cannot adapt to different operational states
Solution Approach 1:
The system performs preliminary action by training machine learning models on historical sensor data before actual anomaly detection begins. This offline training phase captures various operation conditions and establishes baseline patterns, enabling the system to adapt to different operational states without requiring real-time configuration changes or manual intervention during operation.
Solution Approach 2:
The machine learning model provides self-service by automatically adapting to different operational conditions through its training on historical data. The system autonomously learns normal patterns for various operation states (normal operation, startup, shutdown, transient conditions) and adjusts anomaly detection thresholds without requiring manual reconfiguration, thereby achieving adaptability while managing complexity through automated learning.
Data Source
AI summary
Various embodiments described herein relate to intelligently detecting and predicting asset anomalies and faults. Such detection is enabled by generating feature that capture relationships between asset sensor values and different operation conditions or states of assets. In this regard, one or more features based at least on sensor data collected from a plurality of sensors associated with an asset are generated, and a data stream comprising data associated with the asset is received. An anomaly score for the data stream is then determined based at least on the one or more features. In accordance with determining whether the anomaly score is indicative of a potential fault of the asset, fault data indicative of the potential fault is generated, and presentation of the fault data and an indication of the one or more features considered in the determination of the anomaly score via the user interface is caused.