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The automobile service experts often assess the health condition of the motorcycles based on the sound produced by taking test rides. To be effective, this process of fault diagnosis needs to be automated. The purpose of this paper is to present a method for fault detection of motorcycles that employs the slopes of the pseudospectral segments as features. Further, the estimated pseudospectrum of a sound signal is divided into eight segments, and the slope of each segment is computed. Artificial neural network (ANN) classifier is used for classification. The experimental results show that the proposed method achieves satisfactory results with an average accuracy of 78% for healthy motorcycles and 89% for faulty motorcycles. The study can be extended to locate the faults in subsystems of vehicles. The proposed work finds applications in allied areas such as fault diagnosis of machinery, musical instruments, electronic gadgets etc.

Acoustic Signal Based Fault Detection of Motorcycles Using Slope Vector of the Estimated Pseudospectrum