In order to supply a gap of current resonance vibration and STFT demodulation method applied to rolling bearing fault feature extraction of city rail vehicle, a fault diagnosis method for rolling bearing is presented, which is based on the integration of improved wavelet packet, frequency energy analysis and Hilbert marginal spectrum. When faults occur in rolling bearing, the energy of the roll...

In order to achieve accurate fault diagnosis of rolling bearings, a hierarchical decision fusion method for bearings is proposed. The back propagation neural networks (BPNNs) architecture includes detection layer, isolation layer and degree identification which reduce the calculation cost enhance maintainability algorithm. By wavelet packet decomposition signal reconstruction raw vibration bear...

The resonance demodulation is an important method in rolling bearing fault feature extraction and fault diagnosis. But in the traditional resonance demodulation method, the resonant frequency of the accelerometer sensing fault information is discrete to some degree due to processing, debugging and installing factors, and the parameters of the band-pass filter are in need for defining beforehand...

Fault diagnosis of monoblock centrifugal pump is conceived as a pattern recognition problem. There are three important phases involved in a pattern recognition namely feature extraction, feature selection and classification. In this study, stationary wavelet transform (SWT) is used for feature extraction and J48 algorithm is used for feature selection and classification. The different fault con...

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vec...

This paper mainly deals with the issue of incipient fault diagnosis for rolling element bearing. Firstly, an envelope demodulation technique based on wavelet packet transform and energy operator is applied to extract the fault feature of vibration signal. Secondly, the relative spectral entropy of envelope spectrum and the gravity frequency are combined to construct two-dimensional features vec...

The fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. For solving this problem, a feature extraction method combing the Hilbert-Huang transform with singular value decomposition was proposed in this paper. The method includes three steps. Firstly, instantaneous amplitude matrices were obtained by Hilbert-Huang transform from rolling bearing...

It is well known that motor current is a nonstationary signal, the properties of which vary with respect to the time-varying normal operating conditions of the motor. As a result, Fourier analysis makes it difficult to recognize fault conditions from the normal operating conditions of the motor. Time–frequency analysis, on the other hand, unambiguously represents the motor current which makes s...

A new scheme for detector generation for the Real-Valued Negative Selection Algorithm (RNSA) is presented. The proposed method makes use of genetic algorithms and Quasi-Monte Carlo Integration to automatically generate a small number of very efficient detectors. Results have demonstrated that a fault detection system with detectors generated by the proposed scheme is able to detect faults in an...

Abstract For dynamic systems, fault detection and diagnosis involve the observation of vibration signals. Faults associated with hydrodynamic bearings are one most common causes forced shutdowns in rotating machinery. If it is a wear fault, bearing clearance affected changing system characteristics. Therefore, time response machine subjected to worn analysed observe behaviour presence wear. Spe...