In this paper, wavelets as signal processing tools are used to analyze the acceleration data acquired at the cylinder head for the detection and characterization of combustion malfunctions in multi-cylinder industrial engines. The objectives were to collect data on 1) normal operations, and 2) operations with a deactivated cylinder to simulate a faulty condition. Wavelet packet and local discriminatory basis algorithm are used to select wavelets that can recognize different conditions. It is shown that the wavelet packet provides a useful data analysis structure for extracting features that are capable of detecting the combustion malfunction of one cylinder in a 12-cylinder engine. Feature extraction is followed by a classification that uses a neural network for the fault identification phase.
Malfunction Detection in Multi-cylinder Engines Using Wavelet Packet Dictionary
Sae Technical Papers
SAE 2005 Noise and Vibration Conference and Exhibition ; 2005
2005-05-16
Conference paper
English
Malfunction Detection in Multi-cylinder engines Using Wavelet Packet Dictionary
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