A BMFO-KNN based intelligent fault detection approach for reciprocating compressor

Amitkumar Patil (), Gunjan Soni () and Anuj Prakash ()
Additional contact information
Amitkumar Patil: Malaviya National Institute of Technology
Gunjan Soni: Malaviya National Institute of Technology
Anuj Prakash: Tata Consultancy Service

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 2, No 3, 797-809

Abstract: Abstract Reciprocating compressors are one of the critical components in petrochemical and process industries. In order to minimize the downtime and reliability enhancement of the system, it is crucial to develop an intelligent fault detection approach that can identify system anomalies before it progresses into severe faults or leads to system break down. Existing studies suggest that signal processing and machine learning technique based combined fault detection models have produced effective results. However, quality of results produced by such approach heavily depends on the input features. Hence, it is imperative to build an effective fault detection model that intelligently overcomes the shortcomings of high dimensionality and efficiently predicts the impending faults. In order to do so, a binary Moth Flame Optimization (a swarm intelligence algorithm) based wrapper-type feature selection approach combined with K-nearest neighbours-based fault classification approach is proposed for fault detection in a reciprocating compressor. The proposed model produced superior results compared to contemporary feature selection techniques (such as, particle swarm optimization, grey wolf optimization, principal component analysis) combined with K-nearest neighbours. The proposed approach can intelligently eliminate redundant features and detect faults in reciprocating compressor at minimal false alarm rate ( 99%).

Keywords: Fault diagnostics; Feature selection; Reciprocating compressor; Condition monitoring; System health management (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-021-01395-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:13:y:2022:i:2:d:10.1007_s13198-021-01395-2

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-021-01395-2

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().