A fault diagnosis method, device, storage medium and equipment
By iteratively updating the filter to minimize the degree of dispersion, the problem of accuracy in fault diagnosis of mechanical equipment in low signal-to-noise ratio environments is solved, and efficient fault signal extraction and diagnosis are achieved.
CN120352120BActive Publication Date: 2026-06-30TSINGHUA UNIVERSITY
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2025-03-27
- Publication Date
- 2026-06-30
AI Technical Summary
Technical Problem
Existing technologies have low accuracy in diagnosing mechanical equipment faults in low signal-to-noise ratio environments, making it difficult to effectively extract fault signals and severely susceptible to noise interference.
Method used
By acquiring the original vibration signal of the mechanical equipment, filtering it using an initial filter, determining the initial search direction and update step size, iteratively updating the filter to minimize the dispersion, obtaining the target filter, and then performing fault diagnosis.
Benefits of technology
This improves the accuracy of fault diagnosis, avoids interference from periodic noise, and ensures the effectiveness and precision of fault diagnosis.
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Smart Images
Figure CN120352120B_ABST
Abstract
This application provides a fault diagnosis method, apparatus, storage medium, and device. The method includes: acquiring the original vibration signal of a target mechanical device; filtering the original vibration signal through an initial filter to obtain an original filtered signal; determining an initial search direction and an initial update step size based on the dispersion of the original vibration signal, the original filtered signal, and the system response of the initial filter; iteratively updating the initial filter multiple times along the initial search direction and according to the initial update step size, with the goal of minimizing the dispersion, until a preset shutdown criterion is met, thus obtaining a target filter. In the multiple iterations, the target filter is iteratively updated along the direction that reduces the dispersion of the original filtered signal. This target filter filters the original vibration signal to obtain the target filtered signal. By focusing on the dimension of dispersion, interference from periodic noise is avoided, ensuring the accuracy of fault diagnosis.
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