Research on Fault Diagnosis Method of Reciprocating Compressor Based on RSSD and Optimized Parameter RCMDE
Source abstract
Abstract
As for the fault diagnosis process of a reciprocating compressor, vibration signals are often non-stationary, nonlinear, and multi-coupled, which makes it difficult to conduct effective fault information extraction. In this paper, a method based on optimized resonance-based sparse signal decomposition (RSSD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The quality factors in RSSD are optimized by atom search optimization (ASO) primarily, then the optimal quality factors are applied to the RSSD of reciprocating compressor fault signals. The noise interference in the original vibration signal can be effectively distinguished from the low resonance component after decomposition. The genetic algorithm (GA) is employed to optimize the core parameters of RCMDE. Finally, the RCMDE of the low-resonance component is extracted as the eigenvalue for pattern recognition. The experimental study illustrates that the spring failure, valve wear, and normal valve conditions of reciprocating compressors can be effectively distinguished by the proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Advanced Algorithms and Applications · Advanced Sensor and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Key Laboratory of Green Process Equipment of Jiangsu Province, Changzhou University, Changzhou 213164, China
Funding
National Natural Science Foundation of China · Natural Science Research Project of Universities in the Jiangsu Province · Jiangsu Province Graduate Research and Practice Innovation Program
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