Predictive Maintenance Studies Applied to an Industrial Press Machine Using Machine Learning

Abstract

The main purpose of Industry 4.0 applications is to provide maximum uptime throughout the production chain, to reduce production costs and to increase productivity. Thanks to Big Data, Internet of Things (IoT) and Machine Learning (ML), which are among the Industry 4.0 technologies, Predictive Maintenance (PdM) studies have gained speed. Implementing Predictive Maintenance in the industry reduces the number of breakdowns with long maintenance and repair times, and minimizes production losses and costs. With the use of machine learning, equipment malfunctions and equipment maintenance needs can be predicted for unknown reasons. A large amount of data is needed to train the machine learning algorithm, as well as adequate analytical method selection suitable for the problem. The important thing is to get the valuable signal by cleaning the data from noise with data processing. In order to create prediction models with machine learning, it is necessary to collect accurate information and to use many data from different systems. The existence of large amounts of data related to predictive maintenance and the need to monitor this data in real time, delays in data collection, network and server problems are major difficulties in this process. Another important issue concerns the use of artificial intelligence. For example, obtaining training data, dealing with variable environmental conditions, choosing the ML algorithm better suited to a specific scenario, necessity of information sensitive to operational conditions and production environment are of great importance for analysis. In this study, predictive maintenance studies for the transfer press machine used in the automotive industry, which can predict the maintenance need time and give warning messages to the relevant people when abnormal situations approach, are examined. First of all, various sensors have been placed in the machine for the detection of past malfunctions and it has been determined which data will be collected from these sensors. Then, machine learning algorithms used to detect anomalies with the collected data and model past failures were created and an application was made in a factory that produces automotive parts.