Faulty voice diagnosis of automotive gearbox based on acoustic feature extraction and classification technique

This research work objectives to establish a systematic approach to automobile gearbox defect diagnostics by acquiring the acoustic feature from their faulty sound information using efficient machine learning techniques. Machine learning method is important for automated fault diagnosis because it uses acquired data to identify machine conditions and defects from their sound. This motivates studying machine learning for gearbox defect diagnosis using acoustic analysis, and their sound inputs. Unexpected gear and bearing failures result in considerable system damage, significant financial loss, and significant safety concerns, in addition to significant system damage. The constant operation and fluctuating cyclic loading conditions can result in catastrophic failure of the gearbox. Utilising condition monitoring, predictive maintenance of a gearbox has been practised over time. Numerous methods, including acoustic analysis from their normal sound and some faulty sound in gear box and others, can be used to conduct non-destructive testing. Following the creation of an experimental setup for several classes of gearbox defects, acoustic features are extracted from the gear in the form of statistical coefficients in term of mean and correlation coefficients. The acoustic features are calculated using Mel-frequency cepstrum coefficients (MFCC) and their derivatives ∆MFCC and ∆∆ MFCC feature extraction technique. The best statistical features are chosen using support vector machine (SVM), decision tree (DT), linear decrement analysis (LDA), Naive Bayes (NB) and Logarithm regression (LR) classifiers. All of the mentioned classifiers here SVM and NB are given the best classification result. From the features based classifiers techniques, the defective component was found.