Predictive Maintenance (PdM) is a prominent strategy comprising all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the main challenges of PdM is to design and develop an embedded smart system to monitor and predict the health status of the machine. In this work, we use a data-driven approach based on machine learning applied to woodworking industrial machines for a major woodworking Italian corporation. Predicted failures probabilities are calculated through tree-based classification models (Gradient Boosting, Random Forest and Extreme Gradient Boosting) and calculated as the temporal evolution of event data. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime (RUL) of woodworking machines. The effectiveness of the proposed method is showed by testing an independent sample of additional woodworking machines without presenting machine down. The Gradient Boosting model achieved accuracy, recall, and precision of 98.9%, 99.6%, and 99.1%. Our predictive maintenance approach deployed on a Big Data framework allows screening simultaneously multiple connected machines by learning from terabytes of log data. The target prediction provides salient information which can be adopted within the maintenance management practice.

Introduction
The increasing availability of Big Data technology platforms and data-driven applications are changing the way decisions are taken in the industry in important areas such as scheduling [1], maintenance management [2,3], and quality improvement [4,5,6]. In manufacturing industry machines and systems become more advanced and complicated. End-users demand comprehensive maintenance service in their production equipment to ensure high availability preventing machine downtimes. In this context, machine learning can be efficiently used for optimal maintenance decision-making. Most of the companies and manufacturers possess huge amounts of sensor, process, and environment data. Combining the data with information about the failures creates useful train data sets for predictive maintenance.
Approaches to maintenance management are grouped into three main categories which, in order of increasing complexity and efficiency [7], are: (i) Run-to-Failure (R2F), (ii) preventive maintenance and (iii) predictive maintenance. R2F is the simplest approach dealing with maintenance interventions which are performed only after the occurrence of failures. Preventive maintenance (PM) comprises all the operational techniques and actions required to ensure machine availability and to prevent a “machine-down” failure. PM is defined as regularly scheduled maintenance actions based on average failure rates driven by time-, meter-, or event-based triggers. A properly implemented PM strategy can provide many benefits to an organization by extending equipment life, optimizing resource expenditures, and balancing work schedules. However, poor maintenance strategies can reduce a plant’s overall machine productive capacity by 5% to 20% [8]. Extensive PM programs require many labor resources, and there is often a probability of performing excessive maintenance that has no positive impact on the equipment [9]. Therefore, it is difficult to determine the optimal level of PM, and it may require years of maintenance actions and data collection before payback is realized [10]. Such maintenance is often carried out separately for every component, based on its usage or on some fixed schedule. Unlike traditional maintenance methods, Predictive Maintenance (PdM), could play a central role for asset utilization, service, and after-sales in the realizing Industry 4.0 new technological services. PdM is defined as a process of determining maintenance actions according to regular inspections of an equipment asset’s physical parameters, degradation mechanisms, and stressors to correct problems before machine-down occurs [9]. Standard EN 13306:2001 [CEN: “Maintenance terminology”, European Standard EN13306, 2001] defines PdM as “condition-based maintenance carried out following a forecast derived from the analysis and evaluation of significant parameters of the degradation of the item”. Contrary to the classic PM programs, PdM could improve the performance of the equipment, strengthening the business model of companies. Thanks to including a set of sensing, condition-based monitoring (CBM), predictive analytics and distributed systems technologies, it is possible to provide remote technical help based on continuous monitoring. PdM works particularly well for systems easy to monitor and have easily identifiable characteristics that can be statistically analyzed to determine the Remaining Useful Lifetime (RUL) [11]. Notwithstanding research unanimously states that implementing of PdM can bring enormous benefits to businesses in terms to reduce life-cycle costs [12] and increasing product reliability [13], advanced maintenance technologies have not yet been well implemented in the manufacturing industry. This depends on various reasons, including the difficulty of gaining appropriately condition monitoring (CM) data (e.g., vibrations, currents, temperatures, etc.) and collect run to failure data in the industrial production environment [14]. In fact, this requires the machines equipped with sensors and proper data acquisition systems and often implies huge investments to redesign sub-systems of the machines. However, many types of equipment, including CNC machine tools, can already provide in real time massive state data about machining process and a large amount of event data related to errors and faults generated by the diagnostic systems embedded by their controllers. All these data together represent a huge wealth of information that nowadays machine tool manufacturers can easily access thanks to the new Internet of Things (IoT) and Information and Communication Technologies (ICT) introduced with the Industry 4.0

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