# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 5 Conclusions  
In this paper, a combination of various statistical methods and machine learning have been proposed to predict the active and non-active states of the machine. In this approach, reliability analysis, the proposed feature extraction method MSTV, as well as time- and frequency-domain analyses, were used to extract features from a raw dataset. Hence, 17 useful features were obtained in the extraction phase, including Weibull, lognormal, and exponential probability distributions, and the proposed feature extraction the MV, RMSV, SMRV, KC, SF, SC, which will be utilized to extract the new features from the MSTV. In addition, RMSF and RVF. Furthermore, after normalization, we used to train four types of machine learning, i.e., the decision tree, kNN, random forest, and a linear SVM model to detect the active and non-active states of the machine. According to the experimental results, the linear SVM model's predicted result indicated a 98% accuracy rate compared with the actual value;  
Figure 9: Comparison of the actual and predicted results of the support vector machine (SVM). (**a**) Actual machine states (in every second). (**b**) Predicted machine states (in every second) using linear SVM.  
this demonstrated that the model was highly accurate and, therefore, can be utilized to reduce machine faults and increase productivity.  
Even though the performance of the non-active state detection model was satisfactory with high accuracy, the statistical method to extract the train features was conservative, and the detection model could not solve all the issues encountered in manufacturing. Therefore, in the future, we plan to utilize more statistical methods such as the Hilbert-Huang Transform or the fast Fourier transform along with the proposed approach to extract more valuable features and develop a promising non-active state prediction model. Finally, we will attempt to create a user-friendly interface for displaying the results in a web application platform.  
Conceptualization, K.-H.Y.; methodology, T.B.; software, A.N.; validation, formal analysis, T.B.; investigation, T.B.; resources, T.B.; data curation, T.B.; writing--original draft preparation, T.B.; writing--review and editing, T.B.; supervision, S.B.; software-upgrade and data generation, K.-H.Y.; project administration, K.-H.Y.; funding acquisition, K.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Technology Innovation Program (2004367, development of cloud big data platform for the innovative manufacturing in ceramic industry) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation)  
We also appreciate the efforts of [PERSON], a PhD student in Department of Computer Science at Chungbuk National University, who helped in revision of this paper.  
The authors declare no conflict of interest.