A Study on PF–IFF-Based Diagnosis Model of Plant Equipment Failure
Source abstract
Abstract
There are two types of maintenance policies for equipment: breakdown maintenance and preventive maintenance. In the case of applying preventive maintenance, the maintenance is carried out based on time or the condition of the equipment. However, with the development of Information and Communications Technologies (ICT) and the Internet of Things (IoT) technology, the data collected from equipment has rapidly increased and the use of Condition-Based Maintenance (CBM) to perform appropriate maintenance based on the condition of the equipment is increasing. In this study, based on gathered sensor data, we introduce an approach to diagnosing the condition of the equipment by extracting specific data features related to the types of failures that occur with equipment. To this end, we used the K-means clustering method, support vector machine (SVM) classifier, and Pattern Frequency–Inverse Failure mode Frequency (PF–IFF) method with the Term Frequency–Inverse Document Frequency (TF–IDF) method. As a case study, we applied the proposed approach to a centrifugal pump and carried out computational experiments for assessing the performance and validity of the proposed approach.
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Research context
OpenAlex topics: Fault Detection and Control Systems · Machine Fault Diagnosis Techniques · Advanced Sensor and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
EENTA Co., Seongnam 13449, Korea
Extreme Technology Research Center for Ship and Offshore Platform, INHA University, Incheon 22212, Korea
Department of Naval Architecture and Ocean Engineering, INHA University, Incheon 22212, Korea
Department of Industrial and Management Engineering, Incheon National University 119 Academy, Yeonsu-gu, Incheon 22012, Korea
Department of Industrial Engineering, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul 04066, Korea
Funding
National Research Foundation of Korea
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