induction motor bearing is one of the key parts of the machine and its analysis and interpretation are important for fault detection. in the present work vibration signal has been taken for the classification i.e. bearing is healthy (h) or defective (d). for this purpose, clustering based classification of bearing vibration data has been carried out using principal component analysis (pca) and ...

This paper presents a new Fast Kurtogram Method in the time-frequency domain using novel types of statistical features instead of the kurtosis. For this study, the problem of four classes for Bearing Fault Detection is investigated using various statistical features. This research is conducted in four stages. At first, the stability of each feature for each fault mode is investigated. Then, res...

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...

In rotating machinery one of the critical components that is prone to premature failure is the rolling bearing. Consequently, early warning of an imminent bearing failure is much critical to the safety and reliability of any high speed rotating machines. This study is concerned with the application of Recurrence Quantification Analysis (RQA) in fault detection of rolling element bearings in rot...

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...

This paper focuses on the problem of accurate Fault Characteristic Frequency (FCF) estimation of rolling bearing. Teager-Kaiser Energy Operator (TKEO) demodulation has been applied widely to rolling bearing fault detection. FCF can be extracted from vibration signals, which is pre-treatment by TEKO demodulation method. However, because of strong noise background of fault vibration signal, it is...

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA)...

The rolling element bearing is a key part in many mechanical equipment. The accurate and timely diagnosis of its faults is critical for predictive maintenance. Vibration signals from a defective bearing with a localized fault contain a series of impulsive responses, which result from the impacts of the defective part(s) with other elements and inevitable noise. Most researches carried out have ...

Bearings play an important role in mechanical transmission. Many disasters are due to bearing faults. This has driven the need in research for early bearing fault detection. The goal is to extract the periodic impulse signals from the very noise signal which are indicative of a bearing fault. This is done by enhancing impulsive signals while suppressing other signals. This paper used a new meth...

Bearings are among the most frequently used components. Bearing failure could lead to complete stall of a mechanical system, unpredicted productivity loss for production facilities or catastrophic consequence for mission-critical equipment. As such, bearing fault detection and diagnosis is an imperative part of most of preventive maintenance procedures. This paper presents a parameter independe...