This paper uses Multi-Layer Perceptron Neural Network (MLPNN) for comparing the linear dimensionality reduction techniques (DRTs) for fault diagnosis in rolling element bearing (REB).The vibration signals from normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired under different radial loads and ...

The wheelset bearing play an important role in train running safety. In this paper, we introduce the nonlinear dynamical method to study the wheelset bearing vibration signal. Based on the comparative studies of the phase graph and the correlation dimension of the vibration signals under normal condition, outer peeling fault condition and rolling element electric erosion fault condition, the fo...

Multibody dynamics of healthy and faulty rolling element bearings were modeled using vector bond graphs. A 33 degree of freedom (DOF) model was constructed for a bearing with nine balls and two rings (11 elements). The developed model can be extended to a rolling element bearing with n elements and 3 n DOF in planar and 6 n DOF in three dimensional motions. The model incorporates the gyroscopic...

The failure of rotating machinery sometimes involves several faulty components. Existence of both bearing fault and gearbox fault is widely observed and in this situation the vibration feature of the bearing fault can be masked by the faulty gearbox vibration signals. In this research, a method is proposed based on wavelet packet transform and envelope analysis to extract fault features of the ...

In this work an automatic fault classification system is developed for bearing fault classification of three phase induction motor. The system uses the wavelet packet decomposition using ‘db8’ mother wavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The selection of best node of wavelet packet tree is performed by using best tree a...

Vibration signals resulting from rolling element bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. This paper proposes a method for processing of signals resulting from rolling element bearing defects, based on the use of a shifted wavelet filter family. Using a time-frequency repre...

In a complex field environment for modern mechanical equipment, how to identify all kinds of operational status of the rolling element bearings fastly and accurately is very important and necessary. A novel approach to automated diagnosis is introduced, which is based on feature extraction with the Dual-Tree Complex Wavelet Transform (DT-CWT), then attribute reduction with rough set theory and ...

Rolling element bearing is essential part of machinery. The rolling bearing, with outer ring fixed, is a multi body mechanical system with rolling elements that transmit motion and load from the inner raceway to the outer raceway. The rolling bearings dynamical behaviour analysis is an important condition to determine the machine vibration response. Modern trend of Dynamic analysis is useful in...

The generation of synthetic vibration signals enables the testing novel machine diagnostic methods without costly introduction real failures. One major goals vibration-based condition monitoring is early detection bearing faults. This paper presents a modeling technique based on combination known mechanical properties modeled object (phenomenological part) and observation (behavioral part). mod...