ML Anomaly Detector for Sensor Deviation in Monitored Systems
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Solution Overview
Problem
Current anomaly detection systems in mechanical and electrical systems, such as IoT sensor deployments and complex vehicle systems, face challenges in accurately identifying anomalies in real-time, leading to potential monetary losses, property damage, and safety issues due to delayed or incorrect responses.
Innovation Solution
A machine learning-based apparatus that utilizes a data interface, anomaly detector, and system applicator to collect and analyze sensor data, learn from historical data, and apply modifications to prevent or mitigate anomalies by detecting deviations in sensor readings from expected norms, thereby reducing false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection systems are used, then system complexity is reduced, but measurement precision and reliability deteriorate due to inaccurate anomaly identification
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between sensor data collection and anomaly detection. This model is trained on historical sensor data to learn normal system behavior patterns, enabling more accurate anomaly identification without requiring complex manual rule-based systems. The model acts as a mediator that transforms raw sensor readings into meaningful anomaly assessments.
Solution Approach 2:
The system performs preliminary action by training the machine learning model on historical sensor data before actual anomaly detection begins. This pre-training phase allows the system to establish baseline patterns of normal operation, which are then used to accurately identify deviations. The preliminary analysis of historical data creates a foundation for more precise real-time detection.
2Loss of time
If real-time anomaly detection is implemented, then response time is improved, but device complexity increases due to continuous data analysis requirements
Solution Approach 1:
The machine learning model enables self-service by automatically analyzing sensor data and identifying anomalies without requiring constant human intervention or complex manual analysis systems. Once trained, the model autonomously processes incoming sensor readings, compares them against learned patterns, and flags anomalies independently, reducing the need for complex external analysis infrastructure.
Solution Approach 2:
The system implements feedback mechanisms where anomaly detection results are fed back into the model for continuous improvement. The model learns from both confirmed anomalies and false positives, adjusting its detection thresholds and patterns over time. This feedback loop allows the system to maintain high accuracy while adapting to changing system behaviors without increasing complexity.
3Measurement precision
If machine learning models are trained on historical data, then measurement precision improves, but loss of time increases during the training phase
Solution Approach 1:
The patent applies preliminary action by performing model training during system setup and maintenance phases, rather than during critical operation periods. Historical sensor data is collected and analyzed offline to train the model, allowing sufficient time for thorough training without impacting real-time response requirements. The trained model is then deployed for rapid inference during actual monitoring.
Solution Approach 2:
The system employs periodic action by retraining the machine learning model at scheduled intervals using accumulated historical data, rather than continuously training during operation. This periodic retraining approach allows the model to improve its accuracy over time while maintaining system operational efficiency. The training occurs during designated maintenance windows when system performance is less critical.
Data Source
AI summary
An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.