Pre-trained Global Model for Asset Anomaly Detection
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Solution Overview
Problem
Current methods for detecting anomalous behavior in monitored devices are resource-intensive and time-consuming, requiring extensive data for device-specific machine learning models, which is not always available and costly to establish.
Innovation Solution
The use of a pre-trained global model that can be deployed across similar assets with data scaling to detect anomalous behavior, allowing for cost-effective anomaly detection across multiple devices without the need for extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device-specific machine learning models are trained to detect anomalous behavior, then detection accuracy is improved, but resource consumption and training time increase significantly
Solution Approach 1:
The solution segments the anomaly detection task into two parts: a global model trained on aggregated data from multiple assets that captures general anomaly patterns, and asset-specific adaptations that require minimal training. This segmentation allows the system to benefit from both general knowledge and specific characteristics without the full resource cost of training complete device-specific models.
Solution Approach 2:
The global model is trained in advance on aggregated historical data from multiple assets before deployment. This preliminary action pre-establishes anomaly detection capabilities that can be directly applied to individual assets, eliminating the need for extensive on-site training and reducing both time and resource requirements while maintaining detection accuracy.
2Measurement precision
If device-specific machine learning models are trained for each monitored device, then detection precision is improved, but data requirements and training resources increase
Solution Approach 1:
The solution merges data from multiple assets to train a single global model, pooling the training data resources across the entire fleet rather than requiring separate data sets for each device. This combining approach allows the global model to learn from diverse operational patterns and anomalies across all assets, achieving high detection precision without each individual asset needing to provide extensive training data.
Solution Approach 2:
The global model is designed to be universally applicable across multiple asset types and configurations. By training on aggregated data that encompasses various operational scenarios, the model develops multi-functional anomaly detection capabilities that can identify anomalies across different assets without requiring asset-specific retraining, thereby reducing overall data requirements.
3Productivity
If pre-trained global model is used for anomaly detection, then resource requirements are reduced, but model adaptability to specific assets decreases
Solution Approach 1:
The solution applies local quality by allowing the global model to be adapted to specific asset characteristics through minimal asset-specific training or configuration adjustments. While the core model remains general and pre-trained, localized adaptations can be applied to capture asset-specific patterns, achieving both deployment efficiency and asset-specific adaptability by combining global knowledge with local customization.
Data Source
AI summary
A method of behavior monitoring includes receiving, at a device, sensor data from one or more sensors associated with a monitored asset. The method also includes applying, at the device, a data scaling operation to input data to generate scaled input data for a pre-trained global model. The input data is based on the sensor data. The method further includes providing, at the device, the scaled input data to the pre-trained global model to selectively generate an alert.