Rotating machinery breakdowns are most commonly caused by failures in bearing subsystems. Consequently, condition monitoring of such subsystems could increase reliability of machines that are carrying out field operations. Recently, research has focused on the implementation of vibration signals analysis for health status diagnosis in bearings systems considering the use of acceleration measure...

Crack fault of rotor is one of the most prominent problems faced by magnetic bearing rotor system. In order to improve the safety performance of this kind of machinery, it is necessary to research the vibration characteristics of magnetic bearing cracked rotor system. In this paper, the stiffness model of the crack shaft element was established by the strain energy release rate (SERR) theory. T...

steering assist system controls the force transfer behavior of the steering system and improves the steering probability of the vehicle. moreover, it is an interface between the diver and vehicle. fault detection in electrical assisted steering systems is a challenging problem due to frequently use of these systems. this paper addresses the fault detection and reconstruction in automotive elect...

The aim of study is to apply the condition monitoring technique in the journal bearing to detect the faults at an early stage and to prevent the occurrence of catastrophic failures. This study presents fault diagnosis on journal bearing through the experimental investigation at high rotational speed. Journal bearings are widely used to support the shaft of industrial machinery with heavy loads,...

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 ...

This paper reports an enhanced energy operator (EEO) method to detect bearing faults. This new energy operator exploits both the interference handling capability of a differentiation step and the noise suppression nature of the integration process. All these elements, i.e., differentiation, integration and energy operator, are implemented by a simple formula in one step. The main advantages of ...

Diagnostics of rolling elements under varying operational conditions, where disturbances and other rotating elements have strong influence on correctness of analysis, requires engagement of advanced signal processing techniques. Extraction of signal components generated by bearing faults has been proven to be an exceptionally promising method for rolling element bearing fault detection. In this...

Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used...

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, ...