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Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.

In the engineering practice, it frequently occurs that designers, final or intermediate users have to roughly estimate some basic performance or specification data on the basis of input data available at the moment, which can be time-consuming. There is the need for a tool that will fill the missing gap in the optimization problems in engineering design processes, by making use of the advances in the artificial intelligence field. This paper aims to fill this gap by introducing an innovative Design Support System (DesSS), originated from the Decision Support System, for the prediction and estimation of machine specification data such as machine geometry and machine design on the basis of heterogeneous input parameters. As the main core of the developed DesSS, we introduced different machine learning (ML) approaches based on Decision/Regression Tree, k-Nearest Neighbors, and Neighborhood Component Features Selection. Experimental results obtained on a real use case and using two different real datasets demonstrated the reliability and the effectiveness of the proposed approach. The innovative machine learning-based DesSS meant for supporting the designing choice, can bring various benefits such as the easier decision-making, conservation of the company's knowledge, savings in man-hours, higher computational speed and accuracy.

Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpected motor failures and greatly improves the system reliability. This paper describes a Machine Learning architecture for Predictive Maintenance, based on Random Forest approach. The system was tested on a real industry example, by developing the data collection and data system analysis, applying the Machine Learning approach and comparing it to the simulation tool analysis. Data has been collected by various sensors, machine PLCs and communication protocols and made available to Data Analysis Tool on the Azure Cloud architecture. Preliminary results show a proper behavior of the approach on predicting different machine states with high accuracy.

Electric machines together with power electronic converters are the major components in industrial and automotive applications. The frequent situation in the engineering practice is that designers, final or intermediate users have to roughly estimate some basic performance data or specification data or other metrics related to the specific task they have, on the basis of few data available at a particular instant of time or at the time of use. This paper addresses this problem in the Industry 4.0 scenario by introducing innovative Design support system (DesSS), originated from the Decision Support System (DSS), for the prediction and estimation of machine specification data such as machine geometry and machine design on the basis of other heterogeneous parameters (i.e. motor performance, field of application, geographic market, and range of cost). For the development of the DesSS different machine learning techniques were compared such as Decision/Regression Tree (DT/RT), Nearest Neighbors (NN), and Neighborhood Component Features Selection (NCFS). Experimental results obtained on the real use case demonstrated the appropriateness of the application of the machine learning approaches as the main core of the DesSS used for the estimation of the machine parameters. In particular, the results show high reliability in terms of accuracy and macro-F1 score of the 1-NN+NCFS and RT for solving respectively the classification and regression task. This approach can viably replace the model-based tools used for the parameters prediction, being it more accurate and with higher computational speed.