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Studies in Engineering and Technology Vol. 7, No. 1; August 2020 ISSN 2330-2038 E-ISSN 2330-2046 Published by Redfame Publishing URL: http://set.redfame.com 1 Data Analysis for the Preventive Maintenance of Machinery Ricardo Piqueras 1 , Jose Maria Fernandez-Crehuet 1 1 Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal nº 2, 28006 Madrid, Spain Correspondence: Jose Maria Fernandez-Crehuet, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal nº 2, 28006 Madrid, Spain. Received: November 23, 2019 Accepted: August 18, 2019 Online Published: October 15, 2019 doi:10.11114/set.v7i1.2814 URL: https://doi.org/10.11114/set.v7i1.2814 Abstract This article shows the importance of data for the resolution of current problems, through a case study of preventive maintenance of a company’s machines. From a given inspection of the condition of the machines, enough data is obtained to simulate the operation of the machines under specific conditions using Python programming environment, for a long enough period of time to obtain reliable information about the machines’ lifetime. By using “R”, the statistical analysis of the data is performed to determine the optimal period between repairs, schedule the company’s preventive maintenance and show the possibility of solving complex problems from a simple data set. Keywords: simulation, data, linear regression, statistical analysis, maintenance 1. Introduction In recent times, the maintenance of machinery and equipment has experienced great technical breakthroughs that have generated a new mentality around this activity (Rivas, 2020). During the beginnings of the use of machinery for industrial purposes, it was operated in such a way that its repair was carried out after the failure of any of its components (Muthanandan, 2019). This produced chain breakdowns that caused additional costs, unnecessary increases in repairs’ complexity and permanent failures in components which could no longer be used ( Kruczek, 2019). This type of maintenance is known as corrective maintenance and today is still used for some high complexity equipment in which it is not possible to monitor each and every one of its components (Castillo, 2014). However, in most cases, the upkeep of the equipment has evolved enormouslyand is currently based on failure prediction to try to minimize their impact on the system (Fernandes, 2020). Nowadays, there are several types of maintenance with very different functions and characteristics. In this article, reference will be made, in particular, to preventive maintenance, which can be defined as a set of scheduled activities, such as tests, inspections and repairs, which aim to mitigate the impact of the failures produced on a certain piece of equipment (Muñoz, 2015). In order to explain in detail the methodology and the process followed to carry out a preventive maintenance plan, the data analysis of a company’s machinery will be accomplished. Specifically, the study will focus on “Maderas S.A.”, a fictitious Spanish company devoted to the manufacturing of wood pieces and interested in the implementation of a maintenance system that improves the efficiency of its lathes. Nowadays, data are going through a period of revolution driven, not only by their abundance, but also by the continuous development of new technology based on their analysis, treatment and transformation (Emovon, 2018). This article presents a practical example of the power of data and the great support they provide for problem solving. They have infinite applications and are one o f the fundamental pillars for companies’ productive development (Business Software Alliance, 2015). 2. Theory Goods and services received by customers go through a productive cycle that consists of different phases. During the operation stage, the system can be subject to failures that interrupt the productive activity and obstruct the proper manufacturing of goods. To prevent these issues, equipment maintenance is used, which can be defined as the set of activities applicable to goods and services that aim to prevent breakdowns, as well as ensure the regular operation and good condition of the machinery (Cuartas, 2008). The maintenance tasks are applicable to any type of good susceptible to failing during its period of activity, so that they

Studies in Engineering and Technology Vol. 7, No. 1; 2020 2 are focused on the preservation of both equipment and machinery, as well as industrial buildings, among other examples. The objectives pursued by the maintenance can be synthesized in several points:  To avoid and reduce the breaks, proceeding to their repair in the event of this taking place.  Reduce the impact of failures and extend the useful life of the assets.  Avoid accidents and ensure people's safety.  Reduce production costs. There are several types of maintenance, briefly mentioned in the introduction, that are grouped according to their different characteristics and applications. These are: corrective maintenance, total productive maintenance, predictive maintenance and preventive maintenance (Muñoz, 2015). The previously mentioned corrective maintenance represents the set of activities that involve repair and replacement of deteriorated components, once the failure has occurred. It has obvious drawbacks such as the impossibility of knowing the moment of failure, which involves the risk of occurring at a critical moment in the production process, or chain faults, previously described in the introduction, that cause errors in other components as a consequence of the first fault. The total productive maintenance is based on the permanent approach towards the improvement of the efficiency, through the full implication of all the people who participate in the productive process. The role of the maintenance department is assumed by the entire company, contributing together to maintenance optimization. Predictive maintenance represents the set of monitoring activities of a given system, which allow corrections to be made and interventions in goods when a fault symptom is detected. It is characterized by its great complexity and difficult application, since it implies the monitoring of the whole data of all the components that conform a determined system. From a given set of historical data and the monitoring of the different phases that lead to the failure of each of the components, a causal relationship can be developed between small deviations in the performance of the element and subsequent failure of the same, allowing the intervention and repair of the fault before it takes place. Finally, preventive maintenance, in which the present article focuses, refers to maintenance scheduling based on studies with the aim of mitigating the impact of errors. Good performance of this maintenance can be compared to a healthy habit in terms of exercise and nutrition. It does not represent a health guarantee but increases its probability. Its use gives many advantages to the organization, some of them being mentioned in the following points:  Increase in the security of all the elements involved in the production cycle.  Reduction of downtime, with the consequent increase in the availability of the equipment.  Reduction of costs.  Decrease in the number and scale of the repairs.  Undoubtedly increase in the useful life of the equipment.  Reduction of exposure to risks. In spite of all the advantages listed, their use involves the risk of making unnecessary changes in components whose use could be extended over time, as well as the increase in inventories and staff available (García, 2012). Through a correct treatment and interpretation of the data by using the appropriate tools, it is intended to generate a repair schedule that optimizes the useful life of the machinery used by the company "Maderas S.A.". The development of the procedure is explained in the following sections (Levitt, 2011). 3. Obtaining Data The determination of the period between repairs of the different machines in order to properly characterize and implement the preventive maintenance program can be carried out in several ways, and we have chosen for the present case the analysis of the equipment’s average life. The operating period of the different lathes of the company varies according to two param eters: the machine’s supplier and its maintenance team. The company has 4 different suppliers, who will be named using numbers 1 to 4; and 3 maintenance teams, which will be named with the letters A, B and C. Depending on the assigned parameters, the machines will have different values of their average life, which will need to be reasonably estimated in order to program the periods between maintenances. “Maderas S.A.” has a total of 300 lathes. With the objective of obtaining reliable data of the machines’ lifetime and to be able to perform its statistical treatment to reach logical conclusions, an inspection of each of the machine’s condition is realized, determining the period elapsed since their last repair, as well as if they are in operation or in a fault state. This information is collected in the following table:

Studies in Engineering and Technology Vol. 7, No. 1; 2020 3 Table 1. Data of 10 o fthe 300 lathes Lifetime Broken Team Provider 56 0 TeamA Provider4 81 1 TeamC Provider4 60 0 TeamA Provider1 86 1 TeamC Provider2 34 0 TeamB Provider1 30 0 TeamA Provider1 68 0 TeamB Provider2 65 1 TeamB Provider3 23 0 TeamB Provider2 81 1 TeamC Provider4 The most useful information is offered by the machines that are already broken when the inspection takes place. Since the period of time from the last repair to the fault coincides with their total useful life the total time they are able to operate without failing can be known. From the complete data set of the 300 machines, which is not included in this article, it can be seen that the equipment purchased from supplier 1 has an average useful life that ranges from 85 to 93 days, depending on the assigned maintenance team. The machines belonging to the second supplier last between 85 and 93 days, those of the third between 60 and 65, while those obtained from the fourth provider vary between 81 and 88 days of average lifetime. This information, although valuable, is not sufficiently representative since most of the lathes are in correct working condition at the moment of the inspecti on and the lathes’ period of operation does not provide conclusive results concerning average lifetime. In addition, it should be mentioned that industrial equipment obeys, in most cases, a failure curve that is known as the bathtub curve. It illustrates the probability of a certain piece of equipment failing during its lifetime. During the early life of the component, called the infant mortality zone, the fault probability begins with a high value and decreases as time goes by, reflecting the possibility of the component not having been produced or repaired correctly. After this initial zone, a phase with a constant and much smaller failure probability is presented, called random zone. Finally, when approaching the end of its useful life, the breakdown probability increases drastically until it occurs, identifying this period with the name of wear zone. In the few data acquired from the company's lathes, the running time of the machines was grouped only in the wear zone, given the low probability of failure in the infant mortality period compared with thefinal zone of its useful life. This means that the set of data obtained does not represent the failure of the machines in a realistic way, besides being insufficient to be able to take decisions and to draw satisfactory conclusions. Therefore, an interesting alternative to generate a larger volume of data is simulation. It is done through the Python program, and it allows us to observe the behavior of the machines during a year or even more time, while the real simulation time takes a few milliseconds (Rossum, 2009). The code sheet is not included in the article, since it is not necessary for the understanding of the programming because it has been carried out using a considerably simple method, detailed below. Ilustration 1. Designed failure curve

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