Induction Machine Rotor Anomaly Detection via Low-Frequency Signal Rectification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting anomalies in the rotor of induction machines, such as broken rotor bars, are computationally intensive and require observation of frequency components close to the primary components, making them inefficient and costly for large machines.
Innovation Solution
A method and system that process signals from induction machines to obtain low-frequency signals, rectify them, and declare anomalies based on these rectified signals, using techniques such as instantaneous impedance computation and signal processing to reduce computational and memory storage requirements, allowing for on-line monitoring and early detection of rotor anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency response analysis is used to detect rotor anomalies, then detection accuracy is improved, but computational complexity and memory storage requirements increase
Solution Approach 1:
The patent extracts only the essential low-frequency components from the current signal that are directly related to rotor anomalies, rather than performing complete frequency response analysis. This selective extraction maintains detection accuracy while significantly reducing computational complexity and memory requirements.
Solution Approach 2:
The patent segments the current signal into different frequency components and focuses analysis only on the low-frequency segment that contains anomaly information. This segmentation allows the system to ignore high-frequency components, reducing overall computational burden while preserving detection capability.
2Reliability
If complete frequency response analysis is performed, then anomaly detection capability is improved, but processing time increases
Solution Approach 1:
The patent extracts only the low-frequency components containing anomaly information, eliminating the need to process the entire frequency spectrum. This extraction approach maintains reliable anomaly detection while dramatically reducing processing time for implementation in real-time monitoring systems.
3Measurement precision
If high computational resources are allocated for signal processing, then detection precision is improved, but cost and system complexity increase
Solution Approach 1:
The patent extracts only the necessary low-frequency signal components for anomaly detection, avoiding the need for complex computational resources. This approach achieves accurate detection with simpler, more cost-effective hardware and software implementations suitable for industrial environments.
Data Source
AI summary
A method for detecting an anomaly in an induction machine includes obtaining or receiving a signal from the induction machine; processing the signal so as to obtain a low frequency signal, then rectifying the low frequency signal; and, declaring if the anomaly is present, based on the rectified low frequency signal. A system for detecting anomalies is also disclosed. The present invention has been described in terms of specific embodiment(s), and it is recognized that equivalents, alternatives, and modifications, aside from those expressly stated, are possible and within the scope of the appending claims.