Fault diagnosis in vehicle engines using sound recognition techniques

Vehicle engine faults are serious faults that occur inside the engine, the ability to successfully perform fault diagnosis is highly dependent on technician skills. Some experienced technicians have some failure rate, which can lead to serious waste in time and money. Accordingly, an improved diagnosing methods is highly needed. In this paper, we develop an algorithm for fault diagnosis in vehicle engines using sounds techniques, since each engine fault has a specific sound that is distinguished. We collect and analyze sound samples from different types of cars, which represent different types of fault, to create a database of sound prints that will make the whole process of diagnosing engine faults based on sound easier and less time consuming. Different features from the sound samples are extracted and used for diagnosis. The fault under test is compared with the faults in the database according to their correlation, normalized mean square error, and formant frequencies values. The best match is considered the detected fault. The developed system can be useful for the inexperienced technicians and engineers and can be used as a training module for them. The simulation results show the high fault detection rates of the proposed algorithm.