Fault diagnosis of rolling element bearings using artificial neural network

Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a Perceptron Multilayer Artificial Neural Network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects

Contribution of the two rectifiers reconfiguration to fault tolerance connected to the grid network to feed the GMAW through processor-in-the-loop

This study aims to propose a new diagnosis technique based on the Park’s vector and the polar coordinates of electric currents for the detection and location of open-circuit faults (OC) at the level of two rectifiers connected to the grid network to feed the Gas Metal Arc Welding process (GMAW). This diagnosis technique allows the early location of faulty switches (Thyristors) to overcome the negative effect of faulty rectifiers on welding current, welding voltage, and droplet diameter. For that, the reconfigurable rectifiers have been integrated to accomplish the welding process. The proposed diagnosis technique is applied to reconfigurable rectifiers connected to the GMAW system through numerical simulations using MATLAB/Simulink and real-time processor-in-the-loop (PIL) implementation via DSpace ds 1103 card. The simulation and PIL experimental results show similar trends and great success of the diagnosis technique and the two rectifiers reconfiguration for overcoming the open circuit faults and obtaining high welding quality while maintaining the work-piece and avoiding the distortions caused by the faulty rectifiers, which affecting the grid network and on the GMAW system at the same time

Study of the performance of fault-tolerant multi-level inverter included in shunt active power filter

Nowadays, the large number of shunt active power filters (SAPF) is installed in many grid networks to eliminate the source currents harmonics and enhance power quality. These filters are installed in different places according to the filtration requirements. The connection between SAPF and grid network has a negative effect during the open-circuit fault of the insulated gate bipolar transistor (IGBT) switch of the SAPF. This paper proposes the application of the new diagnostic method based on the trigonometric circle and mean value variations techniques to the early detection and precise location of the open-circuit fault of the IGBT switches, and the inclusion of the modified reconfigurable inverter topology to allow the perfect continuity of the filter currents, and improve the diagnostic of the open-circuit fault. A single-sided amplitude spectrum technique (SSAS) is applied on the source currents to get the THDi% value. The obtained simulation results prove, the great success of the proposed diagnostic method, the ability of the modified reconfigurable inverter to be adapted to the grid network, the short response time between the diagnosis and the reconfiguration process is about 7 ms which is very sufficient to guarantee the rapid continuity of the shunt active power filter