Fault Classification in Vehicle Power Transmission using Machine Learning

Abstract

The work describes the application of machine learning (ML) to the categorization and diagnosis of vehicle faults in the power transmission system. For each failure characteristic condition, a machine learning algorithm may be employed to categorize their separate diagnostic elements. The use of acoustic sensors can be used to create a real-time detection approach for vehicle engines and transmission systems. Previously, it was contacting drivers or obscuring services based on vehicle maintenance and driving safety degree under internet of vehicle (IoV) needs. The car's variable acoustic signals are captured and categorised utilising fuzzy logic controllers through the data acquisition device (DAQ) in this manner (FLC). Further, the results are optimized using Particle Swarm Optimization (PSO) technique. While previous systems used 15 fault conditions, we have just used 8 conditions to obtain results.