{'fraction_non_alphanumeric': 0.025915996425379804, 'fraction_numerical': 0.0, 'mean_word_length': 5.256983240223463, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Using MFRF Technique

In the dissertation, a hybrid technique based on detection and diagnosis of fault in multi-phase induction motor (IM) is performed. The present technique is the hybridization of Moth Flame optimization (MFO) and Random Forest algorithm (RFA) and it is named as MFRF method. The multiphase IM is evaluated under normal conditions in the initial period. The fault is maintained in multi-phase IM as well as characteristics of system are observed. In the defective period, signals are scaled, that may seen as waveforms are distorted. Distorted waveforms are made up of various frequency methods are required to represent as frequency of time domain as evaluation of failure. IM. The proposed technique is performed in MATLAB/Simulink platform. Implementation of established technique is contrasted to existing methods, like ANN, S-Transform and GBDT. The statistical measures are determined to demonstrate the successfulness of established technique, like precision, sensitivity and specificity, mean median and standard deviation.