A Condition Monitoring System for the Analysis of Bearing Distributed Faults Condition monitoring is used in the plants to protect rotating equipments from failures and assist the management to plan a required shutdown before failure of the equipment. Bearings are the one of the important components in the rotating machines and premature failure of bearings causes huge financial loses. This paper attempts to analyze the bearing distributed faults through a noninvasive diagnostics approach. The distributed faults are created in the bearing using electric discharge machine. The test rig has been developed for the experimental investigations of the Park analysis approach. The results of the proposed Park approach have been analyzed and shown to be a reliable diagnostics approach for the bearing distributed faults.

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every realworld running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into subsignals to solve the timedomain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.