Semi-supervised vibration-based classification and condition monitoring of compressors

Potočnik, Primož (author), Govekar, Edvard (author)

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

• Language: English
• Keywords: condition monitoring, reciprocating compressors, classification, semi-supervised, neural networks, extreme learning machines
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Author Accepted Manuscript
• Year: 2017
• Number of pages: Str. 51-65
• Numbering: Vol. 93
• PID: 20.500.12556/RUL-106541
• UDC: 519.7:004.032.26:007(045)
• ISSN on article: 0888-3270
• DOI: 10.1016/j.ymssp.2017.01.048
• COBISS.SI-ID: 15296539
• Publication date in RUL: 04.03.2019

Record is a part of a journal

• Title: Mechanical systems and signal processing
• Shortened title: Mech. syst. signal process.
• Publisher: Elsevier
• ISSN: 0888-3270
• COBISS.SI-ID: 169243

Secondary language

• Language: Slovenian
• Keywords: spremljanje stanja, batni kompresorji, razvrščanje, delno-nadzorovano, nevronske mreže

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0241
• Name: Sinergetika kompleksnih sistemov in procesov