# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 6 Conclusions  
This paper presented a diagnostic method based on Hidden Markov Models.  
The objective of the research described was to present an approach to diagnose failures in drying press equipment in a pulp industry, through HMM. The paper provides us with a theoretical basis for an application of a fault detection method through the optimization of observable states later applied to an HMM model. The added value of this failure detection method is due to the model making its prediction without prior information about the equipment conditions. The method was experimentally validated through a case study. For this, six variables were considered, through the collection of observations from six sensors, constituting a set of data with approximately three years and three months and with observations collected minute by minute.  
The method was based on the optimization of observable states inserted in the HMM model, where data preparation is done first, followed by resource generation, where it generated 10 different statistical features, by which the objective was to better characterize the signals and do a reduction of the data. Then, a PCA is made and, finally, a Clustering, where each cluster represents the observable states to be used to train the HMM model. Then, a training and testing procedure was carried out on the HMM model to validate the method's ability to detect a failure.  
Through the research carried out, it can be inferred that the method presented, based on HMM, can be used to diagnose the equipment health state supported on CBM strategies.  
Based on this, companies have greater support for decision-making about equipment reliability, helping them be more competitive in the market.  
Based on the previous case study, further research will be carried out with the aim of extending the time for equipment failure prediction as well as for Online Calibration Monitoring (OML). In this way, it will be possible to allow the extension or elimination of periodic sensor calibration intervals, allowing to improve the safety and reliability of observations through greater precision and reliability of the sensors used.  
Conceptualization, J.T.F., I.F., J.R. and A.J.M.C.; methodology, J.T.F. and I.F.; software, A.M. and I.F.; validation, J.T.F., I.F. and J.R.; formal analysis, J.T.F. and J.R.; investigation, A.M. and I.F.; resources, J.T.F., I.F., J.R. and A.J.M.C.; writing--original draft preparation, A.M.; writing--review and editing, J.T.F., I.F. and J.R.; project administration, J.T.F. and A.J.M.C.; funding acquisition, J.T.F. and A.J.M.C. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowvska-Curie grant agreement 871284 project SSHARE and the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-029494, and by National Funds through the FCT--Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/29494/2017, UIDB/04131/2020, and UIDP/04131/2020.  
The authors declare no conflict of interest.