Abstract:
 Rolling element bearings are widely used in many mechanical systems and affect their safe operation reliability. The use of an integral Oil Debris Sensor the lubrication system allows continuous monitoring chips particles originating from evolving failure oil wetted components.
 This paper examines mass loss Monitoring sensor as a Health indicator to assess damage severity ...

The rolling surface for railroad wheels can be a heat source that may have an impact on the performance of the wheel bearing. In this study, experimental data from an electrically-heated railroad wheel set is analyzed by constructing thermal models of the wheel set. A steady finite-element model, a steady-analytical model, and a transient lumped-parameter model are discussed. Model parameters a...

specific strength, high specific stiffness, and many other advantages (Mordike et al., 2001). With the increased consciousness of energy saving and environmental protection, Mg alloys are becoming widely recognized as the candidates to replace steel and aluminium alloys in many fields, such as in vehicles and the electronics industry. Mg alloys are thus becoming known as new ‘green’ engineering...

The present paper aims to demonstrate why usually when theoretical mathematical models are used to compute the frequencies corresponding to a faulty rolling bearing a deviation is obtained between the computed values and the real frequencies emitted by such a device. A laboratory rolling bearing test ring has been developed to perform the current studies. From the obtained results we highlight ...

Extension neural network is a new type of neural network that combines extension theory and artificial neural network. Extension neural network has been applied to pattern recognition, fault diagnosis and clustering. According to fault characteristics of rolling bearing, we propose a fault diagnostic method for rolling bearing based on extension neural network. We construct the fault diagnosis ...

Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standa...