Fault Diagnosis of Engine Knocking Using Deep Learning Neural Networks with Acoustic Input Processing

Muzammil Ahmed Shaik, Purdue University

Abstract

The engine is the heart of the vehicle; any problems with this component will cause significant damage and may even result in the car being junked. The engine repair cost is enormous, and there is no guarantee that the existing engine will be repaired or replaced. Fault diagnosis in engines is critical; there have been numerous techniques and tools used for fault diagnosis in this revolutionary world, which require some extra cost to detect and still cannot detect faults such as knocking. The engine can have several problems but knocking is the major issue that blows up the engine and results in the breakdown of the vehicle. Our research focuses on this key issue which not only costs thousands of dollars but also results in waste. According to experts, at a very early stage, knocking can be detected by human senses, either visually or audibly. The most noticeable feature in detecting engine faults is the knocking sound. Artificial intelligence deep learning neural networks are well known for their ability to simulate humans; we can utilize this domain to train the networks on sound to detect engine knocking. Many neural networks have been designed for various purposes, one of which is classification. The best widely used and reliable network is the convolution neural network (CNN) which takes input as images and classifies them respectively. Engine sounds have been collected from Google’s Machine Perception research. Our research shows that a prominent feature in building these networks is data. Understanding data and making the most of it is central to data science. A better model is created by meaningful data, not just by designing a complex network. We have used a new algorithmic method of extracting sound and feeding it into all variants of CNN, which we call dependent vehicle sound extraction, in which we use fast Fourier transform (FFT), short-time Fourier transform (STFT), and Mel-frequency cepstral coefficients (MFCCs) for processing input sound signals. We validated the utilization of deep learning networks with a unique dependent vehicle feature extraction technique to detect engine knocking with accurate classification.

Degree

M.Sc.

Advisors

Tan, Purdue University.

Subject Area

Artificial intelligence|Electrical engineering|Mathematics

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