Development of Scalable Application for Ground up Using Cloud Computing with Deep Learning
Keywords:
predictive maintenance, machine learning, CNC machining, sensor data analysis, scalabilityAbstract
In this study we explore the potential of applying machine learning (ML) strategies for CNC (Computer Numerical Control) vertical or horizontal machining centers to predictive maintenance. The research focuses on scalable applications for analyzing sensor data from machines parts including: temperature, pressure vibration gas content detection as well as oil density sensors. A dataset of sensor readings collected over one week is used to train and test various ML models, such as Artificial Neural Networks (ANNs), Decision Trees (DTs), Random Forests (RF), and Naive Bayes (NB). Model performance results are judged based on the precision/recall or F1 score and the accuracy ratings. Also, confusion matrices are used to provide detailed insights into the classification performance of each model. The highest accuracy value is achieved by ANN, followed closely by DT, RF and NB. As such, This research points out that ML models are capable of using sensor data to make highly accurate predictions and in doing so fill the need for a truly proactive approach to maintenance which can increase resource use efficiency in manufacturing industries, reduce downtime and boost productivity overall. The findings represent a step forward in predictive maintenance methods which opens up opportunities to develop forward-looking strategies for industry.
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