SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

Abstract

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.

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

“…They have been successfully applied in several field of PdM applications. Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique.…”

“…In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124]. Some authors [21,56,82,112,120,124] have given attention to GBM technique. Some authors [65,66,79,109,112,117] carried out studies by use of DT technique.…”

“…Some authors [65,66,79,109,112,117] carried out studies by use of DT technique. However, it is observed that there is less consideration given to XGBoost technique and there are only a few studies found in the literature [62,81,120].…”

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

“…In parallel, the scientific literature has been contributing to the challenges of enterprise integration and interoperability in the smart manufacturing era (Zeid et al, 2019 ). Existing literature mainly focuses on the development of architectures for addressing lower level interoperability challenges of Industry 4.0, such as distributed storage, data aggregation, and service orchestration (Pisching et al, 2018 ; Bicocchi et al, 2019 ; Fraile et al, 2019 ) as well as big data infrastructures (Pedone and Mezgár, 2018 ; Calabrese et al, 2020 ). A considerable amount of research has also focused on architectures for CPS, digital twins, and AAS (Lee et al, 2015 ; Bader and Maleshkova, 2019 ; Bousdekis et al, 2020a ; Cavalieri and Salafia, 2020 ).…”

Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

“…Markets and trades generally comprised a huge amount of well-structured data that lies the foundation from the application of a data-driven model rather than a knowledge-based technique [5]. Experts and decision support systems (DSS) using a portion of AI are widely spread in different domains [6]- [9]. In the context of financial services DSS are often used for different application ranging from investment advice, credit granting, online portfolio selection [10] and service to trading [5].…”