Circuit Breaker Trip Cause Detection Using Vibration Spectrograms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing circuit breakers lack the ability to differentiate between overload and short circuit tripping causes, which is crucial for appropriate corrective measures and safety assessment.
Innovation Solution
A method using vibration sensors and supervised machine learning to analyze time-domain vibration signals, transforming them into spectrograms, and applying a parameterized prediction model to determine the tripping range as normal, overload, or fault tripping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If electronic triggers with cause identification are used, then the ability to identify tripping causes is improved, but the device complexity and cost increase
Solution Approach 1:
The patent replaces electronic triggers with a mechanical vibration-based detection system. Vibration sensors (accelerometers) detect mechanical vibrations during circuit breaker operation, and these vibrations are analyzed to identify tripping causes. This mechanical approach substitutes the need for complex electronic cause-identification triggers while maintaining diagnostic capability.
Solution Approach 2:
The patent creates a copy of the tripping event information through vibration signals. Instead of directly measuring electrical parameters to identify causes, the system captures mechanical vibration copies of the tripping event, which are then analyzed to deduce the underlying electrical fault conditions.
2Measurement precision
If vibration sensors and machine learning analysis are added to the switching device, then the measurement precision of tripping cause identification is improved, but the device complexity increases
Solution Approach 1:
The circuit breaker performs self-diagnosis by analyzing its own operational vibrations. The vibration sensors mounted on the device capture vibrations generated during tripping, and the embedded processing unit analyzes these vibrations to automatically identify the tripping cause without requiring external testing equipment or complex additional sensors.
Solution Approach 2:
The patent transforms the physical vibration signals into analytical parameters through signal processing. Time-domain vibration signals are converted into frequency-domain spectrograms, and specific spectral features are extracted as diagnostic parameters. This parameter transformation enables precise tripping cause identification from raw vibration data.
3Ease of operation
If the switching device provides detailed tripping information, then the ease of operation for maintenance personnel is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary analysis of vibration data during the tripping event itself. The processing unit analyzes vibrations in real-time or near-real-time as they occur, immediately classifying the tripping cause. This preliminary action provides maintenance personnel with instant diagnostic information, eliminating the need for time-consuming post-event analysis.
Solution Approach 2:
The patent extracts only the essential diagnostic information from complex vibration signals. Instead of presenting raw vibration data or requiring full spectral analysis, the system extracts key features and directly outputs the tripping cause classification, providing maintenance personnel with actionable information without overwhelming them with data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately identifies the tripping cause, enabling safer and targeted maintenance by distinguishing between normal, overload, and fault tripping through a human-machine interface.
Implementation Method 1
The article 'On-site Online Condition Monitoring of Medium-Voltage Switchgear Units', by C. Nicolaou et al, published in LAK22, 12th International Learning Analytics and Knowledge Conference, November 8, 2021, describes a method for monitoring the switching conditions of switching devices, using time-domain vibration signals.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to a method and system for determining a tripping range of an electrical circuit switching device (4) adapted to supply an electrical installation (12). The system (2) comprises an electronic computing module configured (6) to receive data from at least one vibration sensor (16) integrated into said switching device (4) and configured to select, from spectrograms calculated from acquired time-domain vibration signals, a predetermined subset of operational characteristics; apply a parameterized prediction model by supervised machine learning to the values of the operational characteristics to obtain an estimated value of the breaking current, and determine, as a function of the estimated breaking current value, a tripping range of the switching device from among: a normal trip, an overload trip, and a short-circuit trip.