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.
Highlights
The valves in an internal combustion engine play a significant role in engine performance
This study constructed an expert system based on acoustic emission data to distinguish between valve fault types
In order to distinguish between the fault types in valves, an artificial neural network (ANN) was used based on acoustic emission (AE) features
Summary
The valves in an internal combustion engine play a significant role in engine performance. In recent years, implementing condition monitoring techniques for internal combustion (IC) engines has led to reliable nonintrusive methods for engine diagnosis [3]. It is possible for turbulent flow, such as air flow through valves, to induce vibrations in their structures. Three fault types (valve clearance, valve head crack, and valve head notch) were artificially simulated as follows. The test rig was a cylinder head for a spark-ignited engine The aim of this investigation at this stage was to use AE signals in VL to identify fault types in an engine that is not running
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