Electromechanical Wear Detection Using Multi-Parameter Machine Learning
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Solution Overview
Problem
Existing methods for monitoring the wear of electromechanical devices, such as relays and contactors, are limited in their ability to accurately predict aging and failure, often relying on single-factor analysis and requiring manual feature extraction, which can lead to inefficient maintenance and potential downtime.
Innovation Solution
A device and method utilizing machine learning, specifically deep learning techniques, to analyze operating parameters of electromechanical devices, allowing for the determination of wear and imminent failure without manual feature extraction, by training neural networks with mass data to infer the current state and predict future conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-factor analysis methods are used to monitor wear, then device complexity is reduced, but measurement precision and reliability of wear prediction deteriorate
Solution Approach 1:
The monitoring system segments the wear analysis into multiple independent measurement dimensions (current, voltage, temperature, vibration, etc.), each captured by separate sensors and processed independently. This segmentation allows comprehensive multi-factor analysis while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system combines multiple types of measurement data (electrical, thermal, mechanical) into a composite dataset that feeds into the machine learning model. This composite approach integrates diverse information sources to achieve accurate wear prediction, analogous to using composite materials that combine different properties for superior performance.
2Device complexity
If manual feature extraction is used, then device complexity is reduced, but productivity and measurement precision of wear identification deteriorate
Solution Approach 1:
The system implements self-service through automated machine learning models that automatically extract features and identify wear patterns without manual intervention. The neural network autonomously processes raw sensor data, performs feature extraction, and generates wear assessments, eliminating the need for manual analysis while significantly improving identification efficiency and precision.
3Reliability
If comprehensive multi-factor monitoring is implemented, then measurement precision and reliability improve, but device complexity and loss of energy increase
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing sensor data in real-time, preparing it for analysis before wear actually occurs. This ongoing data collection and preprocessing infrastructure is established in advance, enabling rapid and accurate wear prediction when needed without last-minute complexity spikes.
Solution Approach 2:
The system implements feedback loops where machine learning model predictions are continuously refined based on actual wear outcomes and new sensor data. This feedback mechanism improves prediction reliability over time while the system learns to prioritize the most informative measurement factors, effectively managing complexity through adaptive optimization.
4Measurement precision
If comprehensive multi-factor monitoring is implemented, then measurement precision improves, but loss of energy and device complexity increase
Solution Approach 1:
The system applies partial action by selectively activating monitoring based on operational conditions and risk levels. During normal operation, the system monitors key parameters at standard precision levels. When wear indicators suggest approaching failure thresholds, the system intensifies monitoring precision and frequency, achieving high measurement precision only when most needed while conserving energy during stable periods.
Data Source
AI summary
The present invention relates to a device for identifying wear of an electromechanical device, the device comprising: a measuring device, which is configured to detect at least one predetermined operating parameter of the electromechanical device; and an evaluation device, which is configured to determine a current operating state of the electromechanical device from the detected predetermined operating parameter of the electromechanical device by means of machine learning with the aid of mass data, preferably in the form of training data.