Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

Abstract

Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

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Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

“…Ali et al [63,64] investigated fault detection technologies based on artificial intelligence (AI) and AE signals. They proposed two AI models to detect the valve condition in a reciprocating compressor based on several AE signals using SVM and ANN [63,64]. In the literature [65], the ANN and SVM models were trained and evaluated for detection of valve faults in an RC.…”