Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring

Fault diagnosis of the rotary machines is investigated through different kinds of signals. However, the literature shows that the vibration signal analysis is the most commonly used and effective approach. This research investigates the engine faults, including the misfire and valve clearance faults, using the vibration data captured by four sensors placed in different locations of the automobile engine and under different experimental circumstances. The application of the Fast Fourier Transform (FFT) is proposed as a feature extraction methodology which leads to the extraction of 16 features. In addition, four features are extracted using the acquired signals eigenvalues. The statistical approach is proposed to select features for classification of the engine's state. The Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k Nearest Neighbor (kNN) classification algorithms are employed to predict if the motor works healthily based on the selected features and, if not, what kind of faults is in the engine. The performance of ANN, SVM, and kNN in fault diagnosis is analyzed considering different scenarios, features, and based on multiple performance metrics. Comparing the results with the similar efforts in the literature proves the validity of the proposed methods and highlights their superiorities.