Developing an expert prognosis system of the reciprocating compressor based on associations among monitoring parameters and maintenance records. (March 2021)

• Record Type: Journal Article
• Authors: Lu, Yen-Ju; Tung, Fang-Yun; Wang, Chen-Hua
• Is Part Of: Journal of loss prevention in the process industries. Volume 69(2021)
• Publication Date: 2021-03
• Subjects: Expert diagnosis system -- Reciprocating compressor -- Association rule -- Confidence -- Failure mode; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2020.104382
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: The reciprocating compressor is, in general, a critical equipment in a process plant. For certain ultra-high-pressure process, if the reciprocating compressor fails, often it will cause serious impact to not just the compressor itself, but also the process surrounds it. To prevent compressors from failures, an expert diagnosis system is needed. However, the traditional rule-based expert system is quite inefficient and difficult to create. For an expert prognosis system that is customized to meet needs of a specific process, one needs to refer to plant maintenance history, which is hard to come by due to the fact that most maintenance was poorly documented. This research attempt to demonstrate the feasibility of developing an expert prognosis system through implementation of association rules. Rather than mining from maintenance history, records of failure cases were collected from technical journal articles by extracting information containing failure symptoms and causes on failed components, that mimicking repair history. In total, 115 failure information out from 41 journal articles were gathered. Applications of this approach to practical use in a process plant is easy by replacing the failure information table with that from datamining the repair history. The failure information was first tabulated and then put through association analysis for support, confidence, and lift between two parameters. The demonstration program has been successful with 1-to-1, many-to-1, and many-to-many analysis among failed components, failure modes, and operation parameters. Highlights: For the demonstration purpose, failure information from published literatures mimics the maintenance history. Use of the association analysis to create equivalent functionalities of the IF-Then rule with the certainty value. Based on the association rules, establish diagnosis expert system for rotating equipment. The diagnosis expert system can quickly infer faults of rotating equipment from single and double parameters.