Maintenance
The automation's growth, triggered by the new climate of excessive competitiveness, has made the industry dependent on technology, which highlights the need to implement efficient maintenance policies since, in this situation, a failure has severe consequence (Bastos et al., 2009; Ilonen et al., 2005; Lee et al., 2009).
There are two generic types of maintenance: reactive maintenance, which involves the equipment's repair after the breakdown, and pro-active maintenance, in this case, the repair operation occurs before the failure.
The first type of maintenance is generally known as corrective maintenance. In this type of philosophy, the maintenance interventions only occur after the equipment stops working (Bastos et al., 2009).
Alternatively, the second type, pro-active maintenance, can be divided into two different maintenance philosophies’: Preventive and Predictive. The central objective of preventive maintenance is to prevent failures, which can be carried out by predetermined and constant time-based maintenance interventions (calculated according to the reliability of the equipment's components and the production time), or through the monitoring of the equipment's most important process variables.
Conversely, the main objective of predictive maintenance is to predict the equipment's future “health” state and the remaining useful life until a failure occurs(Arno, Dowling, & Schuerger, 2015; Bastos et al., 2009; Efthymiou et al., 2012; Ilonen et al., 2005; Lee et al., 2009; Mobley, 2002).
In Bastos et al. (2009) an architecture for a predictive maintenance system is proposed. This system analyses several types of data through data mining algorithms, with the goal of predicting failures and increasing the equipment's reliability. Similarly, Efthymiou et al. (2012) developed a related platform. However, this platform, in addition to doing everything the previous one does, also allows the user to obtain and visualize several performance indicators.
When analyzing the two previous platforms, it was detected that these involved the prior existence of a data history with the equipment's failures, its causes, among others. In addition to this, and unlike the systems proposed in the previous works, the web platform presented in this paper goes beyond predictive maintenance, allowing companies to manage the access to the platform's web pages and to implement an autonomous maintenance plan.