The bearings are the most important mechanical elements of rotating machinery. They are employed to support and rotate the shafts in rotating machinery. On the other hand, any fault in bearing can lead to losses on the level of production and equipments as well as creation an unsafe working environment for human. For these reasons, Condition monitoring and fault diagnosis of these bearings has ...

Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is used to extracted features which constitute A-matrix. GMM is utilized to...

Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the U.S. into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to moni...

Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals....

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...

Abstract - Use of efficient signal processing tools (SPTs) to extract proper indices for fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The Part1 of the two parts paper focuses on Fourier-based techniques including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault fete...

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...

The present paper aims to demonstrate why usually when theoretical mathematical models are used to compute the frequencies corresponding to a faulty rolling bearing a deviation is obtained between the computed values and the real frequencies emitted by such a device. A laboratory rolling bearing test ring has been developed to perform the current studies. From the obtained results we highlight ...

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...