Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network

Abstract

This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damage in valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and five nodes in the input, hidden, and output layers, respectively. The results of the trained system showed that the AE technique could be used to identify the type of damage and its location.