A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.

Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.

This study proposes a new condition diagnosis method for rotating machinery developed using least squares mapping (LSM) and a fuzzy neural network. The non-dimensional symptom parameters (NSPs) in the time domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using detection index (DI) is also proposed for detecting and distinguishing faults in rotating machinery. In order to raise the diagnosis sensitivity of the symptom parameters the synthetic symptom parameters (SSPs) are obtained by LSM. Moreover, possibility theory and the Dempster & Shafer theory (DST) are used to process the ambiguous relationship between symptoms and fault types. Finally, a sequential diagnosis method, using sequential inference and a fuzzy neural network realized by the partially-linearized neural network (PLNN), is also proposed, by which the conditions of rotating machinery can be identified sequentially. Practical examples of fault diagnosis for a roller bearing are shown to verify that the method is effective.

Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction.

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.