A Deep Learning Solution for Fault Detection and Diagnosis Applied to Internal Combustion Engines

Abstract

In today's competitive manufacturing environment, special attention is given to the quality and reliability of manufactured products. Condition monitoring and more precisely Fault Detection and Diagnosis (FDD) are aimed at addressing that attention for increased customer satisfaction. The economic implications of FDD are highly valued in the industry, and academia is leveraged to provide smart responses. The focus of this research is the development of an FDD algorithm for internal combustion engine faults via engine block vibration using deep learning. The FDD solution would have to be implemented in software where it could operate in the absence of human intervention. The proposed solution includes two elements namely: input feature construction and fault classification. Short-time Fourier Transform (STFT) and Convolutional Neural Networks (CNNs) perform the aforementioned elements. The FDD solution detects and diagnoses fault signatures from 4 different knock sensors mounted on a V8-type Ford engine. The solution comprises the STFT which converts the knock sensors’ signal from the time domain to the crank angle-frequency domain, hence providing features to be used for diagnosis. These features are then used as input to a CNN, which can learn the crank angle-frequency patterns found in the input data and subsequently perform classification. Transfer learning is used in the proposed solution to circumvent domain shift and improve generalization. This gives the FDD solution advantages such as high diagnosis accuracy, robustness against perturbations in data quality and no need for human intervention.