Neural network fault classification of transient data in an automotive engine air path. (9th July 2008)

• Record Type: Journal Article
• Authors: Sangha, Mahavir S.; Gomm, J. Barry; Yu, Dingli
• Is Part Of: International journal of modelling, identification and control. Volume 3:Number 2(2008)
• Page Start: 148
• Page End: 155
• Publication Date: 2008-07-09
• Subjects: fault diagnosis -- fault isolation -- radial basis function NNs -- RBF -- artificial intelligence -- neural networks -- ANNs -- fault classification -- transient data -- automotive engines -- air paths -- spark ignition MVEM -- mean value engine models -- fault patterns; Engineering -- Methodology -- Periodicals; Science -- Methodology -- Periodicals; 001.42
• Languages: English
• ISSNs: 1746-6172

Abstract

Classification of automotive engine air path faults from transient data is investigated using Neural Networks (NNs). A generic Spark Ignition (SI) Mean Value Engine Model (MVEM) is used for experimentation. Several faults are considered, including sensor faults, Exhaust Gas Recycle (EGR) valve and leakage in intake manifold. Consideration of different fault intensities for all the sensor and component faults is a unique feature of this research. The identification of a fault and its intensity has been considered both equally important. Radial Basis Function (RBF) NNs are trained to detect and diagnose the faults, and also to indicate fault size, by recognising the different fault patterns occurring in the transient data. Three dynamic cases of fault occurrence are considered with increasing generality of engine operation: (1) engine accelerates or retards from mean speed, (2) engine runs at different steady speeds and (3) engine accelerates or retards from any initial speed. The approach successfully classifies the faults in each case.