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Signal processing and feature extraction are key steps for engine fault diagnosis. An adaptive multi-scale morphological gradient (AMMG) algorithm, which has an edge of keeping the details of the original signal in small scale structure elements and suppressing noises in large scale ones, is employed to extract the useful signal components hiding in the original vibration signals blurred by strong noises. Furthermore, the non-negative matrix factorization technique is utilized to calculate the features of the signal which is pre processed by AMMG for engine fault diagnosis. The effect of application in practical engine fault diagnosis demonstrates that AMMG and the proposed feature extraction scheme have comparatively higher precision of fault classification, therefore providing an effective method for engine fault diagnosis. [ABSTRACT FROM AUTHOR]

Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: Time–frequency representations (TFR) have been intensively employed for analyzing vibration signals in mechanical faults diagnosis. However, in many applications, time–frequency representations are simply utilized as a visual aid to be used for vibration signal analysis. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing a two direction two dimensional linear discriminative analysis (TD-2DLDA) technique for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals. Then, a novel feature extraction technique, named TD-2DLDA, is proposed to represent the time–frequency matrix. As opposed to traditional LDA, TD-2DLDA is directly conduct on 2D matrices rather than 1D vectors, so the time–frequency matrix does not need to be transformed into a vector prior to feature extraction. Therefore, the TD-2DLDA can reduce the computation cost and preserve more structure information hiding in original 2D matrices compared to the LDA. The promise of our method is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experimental results indicate that the TD-2DLDA obviously outperforms related feature extraction schemes such as LDA, 2DLDA in gear fault diagnosis. [Copyright &y& Elsevier]

Abstract: A novel feature extraction and selection scheme was proposed for hybrid fault diagnosis of gearbox based on S transform, non-negative matrix factorization (NMF), mutual information and multi-objective evolutionary algorithms. Time–frequency distributions of vibration signals, acquired from gearbox with different fault states, were obtained by S transform. Then non-negative matrix factorization (NMF) was employed to extract features from the time–frequency representations. Furthermore, a two stage feature selection approach combining filter and wrapper techniques based on mutual information and non-dominated sorting genetic algorithms II (NSGA-II) was presented to get a more compact feature subset for accurate classification of hybrid faults of gearbox. Eight fault states, including gear defects, bearing defects and combination of gear and bearing defects, were simulated on a single-stage gearbox to evaluated the proposed feature extraction and selection scheme. Four different classifiers were employed to incorporate with the presented techniques for classification. Performances of four classifiers with different feature subsets were compared. Results of the experiments have revealed that the proposed feature extraction and selection scheme demonstrate to be an effective and efficient tool for hybrid fault diagnosis of gearbox. [Copyright &y& Elsevier]