Rolling-element bearings are widely used in various mechanical and electrical systems. A reliable online bearing fault diagnostic technique is critically needed in industries to detect the occurrence of a fault so as to prevent system’s performance degradation and malfunction. To improve the fault diagnostic reliability and efficiency, a genetic algorithm based feature optimization technique is...

This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal measured for the diagnosis. A fuzzy neural network is used to automatically distinguish the fault types of a bearing by time domain fe...

The use of efficient signal processing tools (SPTs) to extract proper indices for the fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The 2nd part of this two-part paper is, in turn, divided into two parts. Part two covers the signal processing techniques which can be applied to non-stationary conditions. In this paper, all utilized SPTs for n...

Abstract: A novel bearing vibration signal fault feature extraction and recognition method based on the improved local mean decomposition (LMD), permutation entropy (PE) and the optimized K-means clustering algorithm is put forward in this paper. The improved LMD is proposed based on the self-similarity of roller bearing vibration signal extending the right and left side of the original signal ...

Ball bearings are among the most important and frequently encountered components in the vast majority of rotating machines, their carrying capacity and reliability being prominent for the overall machine performance. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. This paper presents fault detection of ball b...

In the age of Internet of Things and Industrial 4.0, the prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. Mechanical big data has the characteristics of large-volume, diversity and high-velocity. Effectively mining features from such data and accurately identifying the machinery health conditions with new advanced methods becom...

This paper presents a methodology for an automation of fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components. The system uses the wavelet packet decomposition using ‘rbio5.5’ real mother wavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The decomposition level is determined by the samp...

In the fault diagnosis of the motor, the vibration signals can fully reflect the status of the motor. In this paper, on the basis of wavelet packet fault feature extraction, a new approach for motor fault diagnosis based on wavelet packet analysis and fuzzy RBF neural network was presented.The method gains the energy of characteristic channel of bearing failure vibration signals of asynchronous...

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features...