Search

In this paper, Neural Network (NN) approach is developed and utilised to detect winding faults in an electrical machine using the samples data of electrical machine in both the healthy and different fault conditions (i.e. shorted-turn fault, phase-to-ground fault and coil-to-coil fault). This is done by interfacing a data acquisition device connected to the machine with a computer in the laboratory. Thereafter, a two-layer feed-forward network with Levenberg–Marquardt back-propagation algorithm is created with the collected input dataset. The NN model developed was tested with both the healthy and the four different fault conditions of the electrical machine. The results from the NN approach was also compared with other results obtained by determining the fault index (FI) of an electrical machine using signal processing approach. The results show that the NN approach can identify each of the electrical machine condition with high accuracy. The percentage accuracy for healthy (normal), shorted-turn, phase-to-ground and coil-to-coil fault conditions are 99, 99.6, 100 and 100% respectively. [ABSTRACT FROM AUTHOR]

Electrical machines are useful in nuclear power plants, military applications, domestic appliances and industrial utilities. The relevant literature indicates that the induction machine takes about ninety percent of all electrical machine globally used in the industry. The risk of the failure of this machine can be avoided if the proper diagnostic scheme is designed and implemented to detect failure/impending faults at an incipient stage. This paper describes Discrete Wavelet Transform (DWT) based diagnosis technique that analyses stator currents of induction machine under healthy and faulty conditions. This enables the extraction of harmonic components caused by each signal for further analysis. On one hand, the maximum energy for each signal gives information about the time a certain fault occurs in the machine and the time it starts deviating from the normal (healthy) state. On the other hand, a Fault Index (FI) is generated for each situation in order to classify the state of the machine into normal, medium or high. The colours; green, yellow and red are used to represent the normal, medium and high states respectively. Should the faults at medium state are not detected on time, it may lead a more serious fault (high state). Thus, this technique is robust to identify a fault of an induction machine at the incipient state with a very minimal time. [ABSTRACT FROM AUTHOR]