Vibration Signal Intelligence for Rotating Machinery Defect Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing mechanical defects in rotating machinery rely heavily on manual analysis, which is inefficient, inaccurate, and unable to perform real-time monitoring, often leading to missed maintenance opportunities and accidents.
Innovation Solution
An intelligent identification method that converts vibration signals into frequency domain envelope spectra, screens high energy harmonics using amplitude comparison and filtering, and inputs characteristic parameters into a machine learning algorithm for accurate and real-time defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used for defect diagnosis, then diagnostic experience can be applied, but efficiency and accuracy are insufficient and real-time monitoring cannot be achieved
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated intelligent algorithm system. The machine learning model automatically processes vibration signals, performs envelope spectrum analysis, and identifies defect characteristics without human intervention, thereby simultaneously improving both accuracy and efficiency while enabling real-time monitoring capability.
Solution Approach 2:
The system implements self-service through automated signal processing and defect identification. The intelligent algorithm independently completes the entire diagnostic process from signal acquisition to defect characterization, eliminating dependence on manual analysis and enabling continuous autonomous monitoring of rotating machinery.
2Reliability
If manual diagnosis is used, then flexibility in analysis can be maintained, but real-time online monitoring cannot be performed
Solution Approach 1:
The patent implements continuous real-time monitoring by processing vibration signals continuously without interruption. The system maintains constant surveillance of rotating machinery, automatically updating defect assessments as new data arrives, ensuring no maintenance opportunities are missed and enabling timely intervention before failures occur.
3Measurement precision
If comprehensive frequency multiple check is performed, then identification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by performing frequency multiple checks selectively rather than uniformly across all harmonics. The intelligent algorithm identifies candidate harmonics based on local spectral characteristics and energy distribution, then applies multiple-check validation only to those specific candidates, reducing overall computational complexity while maintaining identification accuracy.
Data Source
AI summary
An intelligent identification method for a vibration characteristic of rotating machinery, the steps providing converting a speed or acceleration time domain signal of mechanical vibration to a frequency domain envelope spectrum by signal processing, extracting a frequency upper limit value fmax of the envelope spectrum; at least screening out a high energy harmonic with a frequency range within fmax/Nmax by amplitude comparison. Nmax is a frequency multiple upper limit multiple for performing a frequency multiple check on the high energy harmonic. Then, extracting at least one set of characteristic parameters, based on respective amplitudes and/or frequencies, of 1-fold to Nmax-fold frequency region peaks of each high energy harmonic. The 1-fold frequency region peak of the high energy harmonic is the high energy harmonic itself. Finally, inputting the at least one set of characteristic parameters of each high energy harmonic into a machine learning intelligent algorithm to perform training and calculation.