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Vibration signal analysis is a significant approach in fault diagnosis. This article presents a new vibration signal preprocessing scheme called Automated Relative Energy based Empirical Mode Decomposition (AREEMD) and a novel Cepstral Autoregressive (cAR) feature extraction. AREEMD generates the modes based on high relative energies and automates the signal reconstruction. The cAR features take advantage of the cepstrum information and provide discriminant markers for various machine faults. The weighted K-nearest neighbor delivered the best results when validated on four different datasets. The proposed scheme yielded promising performances of average accuracies of 99.3% using SUBF, 97.4% for CWRU, 100% on the Ottawa, and 99.1% using the combined dataset. The significant generalizability of the technique is demonstrated by its high performance in identifying faults that will aid in the detection of early warnings, regardless of motor speeds and size of faults, assuring a reduction in system shutdowns brought on by bearing failure. [Display omitted] • A new in-house machine fault vibration dataset (SUBF v1.0) is created. • A novel AREEMD technique is devised for intelligent vibration signal preprocessing. • Novel powerful discriminative Cepstral Autoregressive Features are proposed. • Proposed framework is validated on SUBF, CWRU, Ottawa, and a combined dataset. [ABSTRACT FROM AUTHOR]

A novel fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition (MEEMD) and Probabilistic Neural Network (PNN) is presented in this paper. It aims to achieve more accurate and reliable sensor fault diagnosis in thermal power plant. To restrain the mode mixing problem in traditional EMD, an MEEMD is proposed based on signal reconstruction and pseudo component identification. The MEEMD is applied to decompose the original thermal parameter signals into a finite number of Intrinsic Mode Functions (IMFs) and a residual to extract the sensor fault feature. After analyzing the inherent physical meanings of each IMF and residual, the variances of them are extracted as feature eigenvectors to express the signal feature. Finally, PNN is used as the classifier for detection and identification of sensor faults. Based on the practical normal signals, which are collected from a main steam temperature sensor of a CLN600–24.2/566/566 steam turbine, three types of representative sensor fault signals are simulated to test the proposed method. By analyzing simulation and real signal, the analysis results indicate that the MEEMD can restrain the mode mixing problem in traditional EMD effectively, and the proposed fault diagnosis method had better performance than the other two fault diagnosis methods including basic PNN and EMD–PNN. [ABSTRACT FROM AUTHOR]

• Synchroextracted operator in SET is upgraded with 2nd order Taylor expansion. • A better approximation of local instantaneous frequency can be achieved in ASET. • Qualitative and quantitative evaluations of different methods are implemented. • ASET can be utilized to deal with vibration signal with time-varying nature. • Specific harmonics can be identified by ASET under variable operating conditions. This paper introduces an upgraded synchroextracting transform (SET), namely ameliorated synchroextracting transform (ASET), to deal with fast time-varying and strong frequency modulated signals for time and frequency analysis (TFA). SET is a recently developed time-frequency representation (TFR) method with an exceptional capability to enhance the readability of TFR, yet the kernel of SET is based upon the assumption of purely harmonic signal, therefore, it is more applicable to slow time-varying and weekly frequency modulated signals. However, for rotating machines in real practice, fluctuation loads and varying rotational speeds are always inevitable, the resultant vibrations will exhibit non-stationary frequency-modulated nature. To tackle this problem, in this paper, the synchroextracted operator in SET is upgraded in terms of second-order Taylor expansion to estimate the fast and strong frequency modulated signals so that a better TFR can be achieved. The advantages of ASET is demonstrated with simulated and experimental studies by comparing the existing time and frequency analysis methods. The sharpness of TFR, the ability of signal reconstruction and the robustness to noise pollution are also demonstrated. [ABSTRACT FROM AUTHOR]