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

M Ali Al-Obaidi Salah,Ali Abdul-Hussain Mahdi,K.H Hui,M Salman Leong,Ahmed M Abdelrhman,L.M Hee,Yh Ali,Lim Meng Hee

doi:10.1051/matecconf/201925502013

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.

Highlights

The valves consider a crucial component in any reciprocating compressor due to the valve essential role in compressor efficiency
The study proposed a fault detection technique based on acoustic emission (AE) parameters and artificial neural network (ANN)
The results showed that feed forward back propagation (FFBP) was effective and this suggested technique attained 100% success in prediction and classification at high speed during training

Summary

Introduction

The valves consider a crucial component in any reciprocating compressor due to the valve essential role in compressor efficiency. PV diagram and support vector machines (SVM) have been used to detected and classify reciprocating compressor valves faults in several studies [7, 8]. Van et al [6] presented a new approach for valve fault diagnosis of reciprocating compressors valve using three signals including pressure, vibration and current of induction motor. They classify the valve fault using the deep belief networks (DBN). Houxi et al [11] employed the information entropy and support vector machine (SVM) method to for valve fault diagnosis in reciprocating compressors. Many studies llustrated that AE technique can detect the fault in the initial stage at lower speed while conventional vibration technique is not able [12,13,14,15,16]

Methods

Results

Conclusion