Machine Learning Maintenance Models for Multi-Sensor Anomaly Alerts
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Solution Overview
Problem
Conventional equipment maintenance approaches, such as reactive and rule-based methods, often fail to detect anomalies in a timely manner, leading to costly repairs and potential catastrophic failures in industrial systems.
Innovation Solution
A computer-implemented predictive maintenance process using machine learning techniques, including neural networks and histogram-based models, to analyze sensor data from equipment, detect anomalies before failures occur, and generate alerts for maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict equipment failures, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by training machine learning models offline on historical sensor data to establish baseline patterns of normal equipment operation. These pre-trained models are then deployed to continuously monitor incoming sensor data in real-time, enabling fast anomaly detection without requiring complex real-time computations during operation.
Solution Approach 2:
The patent introduces intermediate processing layers including feature extraction modules that transform raw sensor data into meaningful features before feeding them to the machine learning model. This intermediary step reduces the dimensionality and complexity of the input data, making the computational burden more manageable while preserving the essential patterns needed for accurate anomaly detection.
2Reliability
If continuous monitoring of all sensors is performed, then reliability improves, but energy consumption increases
Solution Approach 1:
The system extracts and focuses monitoring efforts on the most critical sensors and parameters that have the highest predictive value for equipment failure. Rather than continuously monitoring all sensors at full resolution, the machine learning model identifies and prioritizes the subset of sensors that provide the most valuable information for detecting anomalies, reducing overall energy consumption while maintaining reliability.
Solution Approach 2:
The patent implements partial monitoring by using the machine learning model to selectively intensify monitoring of specific sensors when anomaly indicators are detected. During normal operation, not all sensors are monitored at maximum intensity, but when the model detects patterns suggesting potential failures, it automatically increases monitoring frequency and detail for the affected components, balancing energy usage with reliability needs.
Data Source
AI summary
A predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score. Responsive to the anomaly score exceeding a threshold value, the server may issue an alert. The machine learning model may be supervised or unsupervised. In one embodiment, the machine learning model use several sensor channels to predict the values of one or more vitals of the equipment and compare the predicted values to the actual measured values of the vitals. The server may assign an anomaly score based on the differences between the predicted values and the measured values. In one embodiment, the machine learning model may be an autoencoder that generates a distribution of the measurement values to determine the likelihood of observing the actual measured values in a normal operation. In one embodiment, the server may use a histogram approach to predict anomaly.