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

Potočnik, Primož (avtor), Govekar, Edvard (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: condition monitoring, reciprocating compressors, classification, semi-supervised, neural networks, extreme learning machines
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Recenzirani rokopis
• Leto izida: 2017
• Št. strani: Str. 51-65
• Številčenje: Vol. 93
• PID: 20.500.12556/RUL-106541
• UDK: 519.7:004.032.26:007(045)
• ISSN pri članku: 0888-3270
• DOI: 10.1016/j.ymssp.2017.01.048
• COBISS.SI-ID: 15296539
• Datum objave v RUL: 04.03.2019

Gradivo je del revije

• Naslov: Mechanical systems and signal processing
• Skrajšan naslov: Mech. syst. signal process.
• Založnik: Elsevier
• ISSN: 0888-3270
• COBISS.SI-ID: 169243

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: spremljanje stanja, batni kompresorji, razvrščanje, delno-nadzorovano, nevronske mreže

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0241
• Naslov: Sinergetika kompleksnih sistemov in procesov