Abstract
Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong background noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non-Gaussianity and quadratic phase coupling (QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non-Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non-Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on-line monitoring and diagnosis of valve train faults.
Highlights
1 Introduction Vibration signals are widely utilized for health condition assessment and fault diagnosis in diesel engine and frequently transfer active data from mechanical elements
Some conventional fault diagnosis techniques based on vibration signals extract the characteristic quantities from the time domain and frequency domain, statistical indexes, such as peak amplitude, root mean square amplitude, kurtosis and frequency components [7]
A versatile limit determination named OSTU method [26, 27], which can break down picture histograms and acquire the best edge esteem, was utilized to get paired pictures after the computerized pictures were upgraded
Summary
Vibration signals are widely utilized for health condition assessment and fault diagnosis in diesel engine and frequently transfer active data from mechanical elements. The color spatial distribution is considered to be the texture features of the 2D bispectrum plot and reflects the distribution and intensity of the additional information in the dual frequency domain, and exhibits its self-similarity characteristic It can be characterized by the fractal dimension (FD). A versatile limit determination named OSTU method [26, 27], which can break down picture histograms and acquire the best edge esteem, was utilized to get paired pictures after the computerized pictures were upgraded This technique was performed by calling capacity graythresh from the Matlab picture handling tool stash.
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