Early Fault Diagnosis Model Design of Reciprocating Compressor Valve Based on Multiclass Support Vector Machine and Decision Tree

• Published in: Scientific programming Vol. 2022; pp. 1 - 7
• Main Authors: Yu, Zhihong, Zhang, Bosi, Hu, Guangxia, Chen, Zhigang
• Format: Journal Article
• Language: English
• Published: New York Hindawi 08.06.2022; Hindawi Limited
• Subjects: Accuracy; Algorithms; Classification; Decision analysis; Decision trees; Efficiency; Fault diagnosis; Feature extraction; Gearboxes; Lagrange multiplier; Multivariate analysis; Neural networks; Reciprocating compressors; Recognition; Safety valves; Suction; Support vector machines; Valves; China
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2022/7486271

According to the character of frequent fault occurrence, difficult diagnosis of large reciprocating compressor valves, an early fault diagnosis model of reciprocating compressor valve based on multiclass support vector machine and decision tree is designed. A series of simulation experiments of the suction valve and exhaust valve on a large-scale reciprocating compressor experimental bench are made and the valve fault principle is analyzed. Using the advantages of fast and efficient decision tree classification and the prominent characteristics of support vector machine in small sample binary classification, a multivariate classification and recognition model is constructed. The typical characteristic parameters of gearbox vibration signal are extracted as the fault feature vector training model under different fault conditions, and the samples are tested. The experimental results show that the recognition effect of this method is significantly better than that of the neural network method in the case of small samples, and the recognition efficiency is improved more than that of the conventional multivariate support vector machine method which can be effectively applied to reciprocating compressor valve fault diagnosis.