The demand for silent bearing applications has started the research to asses the effect of a viscoelastic damping layer in the vicinity of the bearing, for example between the bearing outer ring and the housing. In order to predict the dynamic behaviour of rolling bearing applications a 3D nonlinear time dependent computational model is developed. Both the vibration generation and the vibration...

A two-dimensional (2-D) explicit dynamics finite element (FE) model of a rolling element bearing was solved using a commercial FE software package, LS-DYNA. It was found that the modelled bearing vibration signals contain a significant amount of numerical noise. This paper provides an explanation of the physical mechanism by which the numerical noise is generated. The noise frequencies were ana...

Introduction With increasing technology, the requirements on the performance of rolling element bearings become more demanding. These types of bearings are used in such applications as computer hard disk drives, which require nanometer level error motion tolerances. In order to measure the error motions of rolling element bearings, the Bearing Analyzer was manufactured with a very precise air b...

Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the ro...

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. In complex machines, the vibration generated by a component is easily affected by the vibration of other components or is corrupt...

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...

Rolling bearing is one of the most widely used elements in rotary machines. In this paper, a novel method is proposed to extract early fault features and diagnosis the early fault accurately for rolling bearing. Wavelet Energy Entropy is introduced as a feature parameter for bearing state monitoring and least square support vector machine (LS-SVM) is used for early fault diagnosis. In order to ...

Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...

Rolling bearing is an important part in mechanical system and faults occur frequently with vibration noise. Empirical mode decomposition (EMD) is a tool for nonlinear and non-stationary signals analysis. However, the major drawbacks of EMD are mode mixing problem, ensemble empirical mode decomposition (EEMD) provides a new tool for signal analysis, and it is an improved technique of EMD. In ord...