Probabilistic Neural Network Based Identification of External faults Experienced by 3-Phase Induction Motors
Journal Title: International Journal of Computational Engineering and Management IJCEM - Year 2015, Vol 18, Issue 3
Abstract
Fault diagnosis and condition assessment (FDCA) of rotating machines is critical due to the pivotal role that these machines play in our industries. Proper FDCA not only augments the machine’s operative lifecycle, it also improves its efficiency, thus reducing chances of cataclysmic failure. This paper defines a realistic FDCA method for 3-phase induction motors using an open source dataset. External faults experienced by IM are monitored by the probabilistic neural network (PNN) whose performance characteristics are then compared with those of Multi-Layer Perceptron (MLP) revealing that the PNN algorithm is much quicker, thereby resulting in lessening of the workload experienced by a computer substantially. RMS value of 3-phase voltages and currents are utilized as input variable for model formation to identify the 6 types of external faults experienced by IMs’ and normal operating (NF) condition. The developed system is then put to the test utilizing a sample set of 160 readings to highlight the efficiency and accuracy of the system for multiple fault scenarios.
Authors and Affiliations
Vihan Talur and Saarang Rastogi , A. P. Mittal Hasmat Malik
Keywords
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Probabilistic Neural Network Based Identification of External faults Experienced by 3-Phase Induction Motors
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