## 5 Conclusions  
This paper proposed the application of a process for the PHM strategy. Recently, officials in charge of process equipment that required high safety, such as plants, have been very interested in applying PHM technology for maintenance. To study the application cases of these technologies, this study applied sensor data in the operation of MAB equipment during plant processes. Sensor data collected from operational information systems in plant processes can be major decision parameters for fault diagnosis in complex systems.  
In order to diagnose the operating conditions of the MAB equipment, the collected sensor data were prepared with examples of fault diagnosis through diagnostic systems, data analyses and classifications, data feature extractions, dimension reductions, and classification using PCA. A total of 236 sensors were initially selected in the operation information system, and 23 sensors were re-selected through a Pearson correlation analysis that best represented the normal and failure conditions of the MAB equipment. The 23 types of sensor data were extracted; each had a statistical feature at a certain time interval. In addition, it was visualized to clearly express the operation status of each piece of MAB equipment in a two-dimensional plane through a dimension reduction process by PCA. As a result, the decision criteria were prepared to diagnose the operation status of the MAB equipment.  
To validate the training results of the analyzed diagnostic process, sensor data--for approximately one month of December 2015, including normal state and filter plugging failure state--were applied as test data. As a result, the filter plugging failure of the MAB equipment, which was confirmed around 30 December, 2015, was detected about 15 days before the failure and determined that it was a complete failure about 10 days prior.  
From the results, the validity of the diagnostic process proposed in this study is considered valid. However, since there is a limit to validating the proposed method with one test result, the validation process and update of the training model will continue to be necessary to validate its validity with appropriate datasets, such as various failure histories. However, it could be a potential direction for future maintenance strategies for MAB equipment in the plant process presented in this study.  
Conceptualization, S.-Y.H. and [PERSON]-H.L.; methodology, S.-Y.H. and J.-H.L.; validation, K.-S.K., H.-J.K., and H.-B.J.; formal analysis, S.-Y.H. and K.-S.K.; investigation, S.-Y.H., [PERSON]-[PERSON] and [PERSON]-H.L.; data curation, S.-Y.H. and [PERSON]-H.L.; writing--original draft preparation, S.-Y.H., [PERSON]-[PERSON] and J.-H.L.; writing--review and editing supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0001968, HRD Program for Industrial Innovation).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because it is only partially available for research.  
**Acknowledgments:** This research was partially supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0001968, HRD Program for Industrial Innovation), and by the Autonomous Ship Technology Development Program (20011164, Development of Performance Monitoring and Failure Prediction, and Diagnosis Technology for Engine System of Autonomous Ships), funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).  
**Conflicts of Interest:** The authors declare no conflict of interest.