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

• Published in: Measurement : journal of the International Measurement Confederation 2018-11, Vol.128, p.527-536
• Main Authors: Jafarian, Kamal, Mobin, Mohammadsadegh, Jafari-Marandi, Ruholla, Rabiei, Elaheh
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Artificial Neural Networks (ANN); Automotive engines; Classification; Eigenvalues; Engines; Fast Fourier Transform (FFT); Fast Fourier transformations; Fault detection; Fault diagnosis; Feature extraction; k Nearest Neighbor (kNN); Monitoring systems; Neural networks; Performance measurement; Rotary machines; Sensors; Signal analysis; Signal monitoring; Support vector machines; Support Vector Machines (SVM); Vibration; Vibration analysis; Vibration monitoring
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2018.04.062

•Engine misfire and valve clearance faults detection considering signal analysis.•Propose an integrated approach in the signal transformation and feature extraction.•Utilization of the statistical analysis in the selection of significant features.•Application and comparison of machine learning algorithms in the fault diagnosis.•Engine fault diagnosis considering various scenarios and different features. 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.