###### Abstract  
In large systems, such as power plants or petrochemical plants, various equipment (e.g., compressors, pumps, turbines, etc.) are typically deployed. Each piece of equipment operates under generally harsh operating conditions, depending on its purpose, and operates with a probability of failure. Therefore, several sensors are attached to monitor the status of each piece of equipment to observe its conditions; however, there are many limitations in monitoring equipment using thresholds such as maximum and minimum values of data. Therefore, this study introduces a technology that can diagnose fault conditions by analyzing several sensor data obtained from plant operation information systems. The equipment for the case study was a main air blower (MAB), an important cooling equipment in the plant process. The driving sensor data were analyzed for approximately three years, measured at the plant. The fault history of the actual process was also analyzed. Due to the large number of sensors installed in the MAB system, a dimension reduction method was applied with the principal component analysis (PCA) method when analyzing collected sensor data. For application to PCA, the collected sensor data were analyzed according to the statistical analysis method and data features were extracted. Then, the features were labeled and classified according to normal and fault operating conditions. The analyzed features were converted with a diagnosis model, by dimensional reduction, applying the PCA method and a classification algorithm. Finally, to validate the diagnosis model, the actual failure signal that occurred in the plant was applied to the suggested method. As a result, the results from diagnosing signs of failure were confirmed even before the failure occurred. This paper explains the case study of fault diagnosis for MAB equipment with the suggested method and its results. pricipal component analysis; prognostics and health management; fault diagnosis; classification; main air blower +
Footnote †: journal: Applied Sci. 2021  
The maintenance strategy is the process of taking timely, appropriate actions, and making accurate logistics decisions based on outputs from diagnostics and prognostics, available resources, and operational demands [5]. In this study, for the application of the strategy concept into the current maintenance method, and to implement it, the PHM procedure was firstly defined. The suggested procedure was applied to analyze the operation data of the actual plant process equipment. Based on the proposed strategy, this study seeks to establish a system to diagnose the conditions of the target equipment based on several sensor data generated during plant operations. In general, for data-based state diagnostics, parameters are selected to analyze and monitor acquired data [6], select the parameters to be monitored, determine the inspection frequency, and establish the criteria for diagnosis. Furthermore, it is also necessary to develop a decision method that selects the reasonable maintenance method [7; 8]. The maintenance method usually requires many tasks, combining sensing and interpretation of environmental, operational, and performance-related parameters [7; 9; 10] for assessing the reliability of a system. The system could be conducted by (1) gathering system status data; (2) monitoring the system conditions based on gathered data; (3) making a diagnosis of the system status; (4) predicting the remaining useful life of the system; and (5) executing appropriate actions, such as repair, replace, and disposal based on reasonable decision making [7; 11]. Figure 1 provides an overview of the overall procedures for maintenance methods for monitoring equipment status in general: data acquisition, data manipulation, system definition, application, and validation/maintenance, which could be a basis for the PHM procedure [10; 11].