# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study  
[PERSON]  
1 EIGe--Research Centre in Industrial Engineering, Management and Sustainability, Lusofona University, Campo Grande, 376, 1749-024 Lisboa, Portugal; [EMAIL_ADDRESS]  
2 CISE--Electromechatronic Systems Research Centre, University of Beira Interior, Calada Fonte do Lameiro, P-62001-001 Covilha, Portugal; [EMAIL_ADDRESS]  
3 ISCE/IPC--Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal; [EMAIL_ADDRESS] (I.F. );  
4 GEMMPE--Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, 3030-788 Coimbra, Portugal  
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[PERSON]  
3 ISCE/IPC--Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal; [EMAIL_ADDRESS] (I.F. );  
6  
[PERSON]  
3 ISCE/IPC--Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal; [EMAIL_ADDRESS] (I.F. );  
6  
[PERSON]  
1 EIGe--Research Centre in Industrial Engineering, Management and Sustainability, Lusofona University, Campo Grande, 376, 1749-024 Lisboa, Portugal; [EMAIL_ADDRESS]  
7  
[PERSON]  
2 CISE--Electromechatronic Systems Research Centre, University of Beira Interior, Calada Fonte do Lameiro, P-62001-001 Covilha, Portugal; [EMAIL_ADDRESS]  
3 ISCE/IPC--Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal; [EMAIL_ADDRESS] (I.F. );  
6  
Footnote 1: email: {jose.fr} {jose. This work aims to demonstrate how it is possible to optimize the data collected from the raw signal given by the sensor and, through the transformation of observable states made by the HMM, how the equipment diagnosis can be obtained. The case study aims to diagnose the state of health of a drying press in a pulp industry, where the section task is to remove water, mainly through pressing. Further, the drying press in a pulp industry is an equipment where the occurrence of failure is harmful to the production chain, which can lead to loss of time and high costs for the company. Therefore, to diagnose this equipment, we have collected data with six sensors, which are coupled to the equipment and are responsible for measuring the current intensity, the level of the hydraulic unit, the torque, the VAT pressure, the speed of rotation, and temperature of the hydraulic unit, in order to characterize the health status of the equipment. This will be done by joining the data from the six sensors and optimizing the observable states, which will later be inserted into an HMM model, allowing the equipment to be diagnosed. The optimization of observable states goes through four phases: data preparation; features generation; Principal Components Analysis (PCA), and Clustering. With this procedure it is possible to extract more information from the signals, to reduce the computational load, and to join the observations of the six sensors, allowing the creation of only one HMM to diagnose the equipment. The objective is to characterize the health of the equipment in three states over the time of data collection: \"Adequate Functioning\", \"Alert state\", and \"Equipment failure\". A theoretical basis is also made on the entire methodology used to carry out the equipment diagnosis in order to demonstrate how the HMM can be applied to assist in Condition-Based Maintenance. As [PERSON] et al. [2] stated, there has been extensive research on maintenance optimization that has also been an important trend in the area of optimization based on maintenance simulation. The authors state that research in this context focuses on the goal of finding the best maintenance policy while minimizing the overall company costs. Like [PERSON] et al. [3] state, \"it was clear that a well maintained property will have a longer useful life with a greater return for the organization\". The main objective of this type of maintenance is to use adequate sensor signals and monitoring techniques to identify and predict the health status of machines, in order to reduce the economic loss due to degradation or failure [4]. Another aspect to consider when talking about data collection with sensors is their reliability [5].