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.
Highlights
In recent years, emerging technologies such as Information and CommunicationsTechnologies (ICT) and Industrial Internet of Things (IIoT) allow us to collect and utilize the data of the status of equipment of major plant facilities and equipment
The state of the actual equipment correto each feature vector wasvector set to the thestate equipment failure mode frommode the point sponding to each feature wasstate set toofthe of the equipment failure from the point of time when the history was recorded in the specific type of equipment to the specific point of time
This study has proposed an approach for diagnosing failure modes based on the sensor data gathered from a centrifugal pump
Summary
Technologies (ICT) and Industrial Internet of Things (IIoT) allow us to collect and utilize the data of the status of equipment of major plant facilities and equipment. In chemical plants such as petroleum refineries, an accident due to an equipment failure such as leakage can bring catastrophic damage to industrial workers and to the local community Power plants such as nuclear power and thermal power plants can cause massive losses due to continuous explosions and fire in the event of a disaster due to the continuous processing of high temperature and high pressure and the centralization of utility facilities. This study introduces an equipment diagnosis approach for identifying failure modes with gathered sensor data from plant equipment under the CBM policy. The proposed approach diagnoses what kinds of failures will appear in the future by extracting specific patterns associated with equipment failure modes based on gathered sensor data and establishing diagnosis models using them.
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