Research on Diesel Engine Piston Wear Fault Diagnosis Method Based on the Local Wave Time-frequency Analysis

Abstract

A large number of non-stationary dynamic signals are generated in the working machinery and equipment. Especially, diesel engines often encounter non-stationary transient and time-varying modulation signals, such as the impulse response signals caused by cylinder piston wear. These kinds of signals generated from diesel engine are analyzed by the method of the Local Wave time-frequency proposed in this paper, and then according to the analysis to diagnose the working state of the diesel engine. It proved that the proposed method is feasible and effective. Moreover, it provides an effective way for the diesel engine condition monitor and fault diagnosis.

A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine

“…In recent years, researchers have introduced image processing technology into the field of diesel engine fault diagnosis, using signal time spectrum maps for image feature extraction and classification recognition. Wang et al [7] use the Wigner-Ville distributions (WVD) of vibration acceleration signals and probabilistic neural networks (PNN) to identify the failure of diesel valve train; He et al [8] proposed a novel denoising method for reliable machinery fault diagnosis based on timefrequency analysis and manifold learning; Zhao et al [9] analysed the local wave time-frequency method and applied it to the analysis of diesel engine vibration signals; Liu et al [10] proposed a fault diagnosis approach for diesel engines based on self-adaptive WVD, fast correlation-based filter, and relevance vector machine. e above methods have greatly promoted the development of fault diagnosis technology of diesel engine, but there are still many ways to explore.…”