Fault Detection of Motorcycles Using the Slopes of the Estimated Pseudospectrum of the Produced Sounds

The service experts assess the condition of the vehicles based on the produced sound. To benefit both, the riders and the experts, the fault diagnosis process needs to be automated. The paper presents a methodology for fault detection of motorcycles, which uses the slopes of the eight pseudospectral segments as features. The computed features are classified into healthy and faulty using artificial neural network (ANN) classifier. The experiments are conducted to prove the effectiveness of the proposed method. The results are satisfactory with an average accuracy of 85%. The study can be extended to localize the sources of the faults in subsystems. The proposed work finds applications in fault detection of machines, musical instruments, electronic gadgets and the like.