# Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform
## 1 Introduction  
In the Industry 4.0 (I4.0) revolution, smart factories are becoming strongly interconnected and are facing major technological changes. The concept of I4.0 is based on cyber-physical production systems (CPPS) [1, 2, 3, 4] linking the real and virtual world, in which fully automated production plants automatically manage industrial processes. Smart manufacturing requires certain basic technologies to enable devices or machines to change their behavior in response to different situations and requirements based on past experiences and learning abilities [5, 6, 7]. These technologies enable direct communication with production systems, thus enabling problem solving and quick adaptation decisions. Some technologies also have artificial intelligence (AI), which allows production systems to learn from experience to ultimately implement a connected, intelligent, and ubiquitous industrial practice. All kinds of failures in industrial systems reduce factory performance, which becomes especially critical in fully automated environments. Therefore, fault finding in automation systems plays a key role in avoiding further losses. Designing a reliable and effective predictive maintenance (PdM) system can help overcome the shortcomings of physical systems. For these reasons, PdM plays a very important role, due to the fact that a minor fault may cause the entire production line to stop. The article proposes a predictive fault detection method useful in Smart Factory automation systems [1, 8].  
Analysis of mechanical signals (i.e., belt drive systems) is one of the most important areas of sustainable production development. Reducing the number of production down-time is one of the necessary research goals for sustainable production in the I4.0 era. Due to growing demand for methods of analyzing mechanical signals, the article attempts to solve the problems related to detection of anomalies, which may occur in belt drive system for future prediction of production system faults, which is an integral part of the development of a modern enterprise [9, 10]. In a modern facility, data analysis can be performed using NAZCA4.0 created by APA Group [11]. NAZCA4.0 allows to measure all desire parametersof the production line (i.e., vibration or energy parameters) from which the current state and any anomalies can be analyzed to increase the efficiency of production engineering. The belt transmission system is one of the most critical components and the most sensitive part to degradation. The basic idea is firstly to find the causes, then to develop a smart way to understand when the system was passing from safe situation to safety critical and lastly to critical one. Belt looseness is the main cause of common failures, which may force the system to stop. The proposed PdM method is designed for the specific belt drive but could also be generalized for other kinds of transmission systems [9, 12, 13, 14].  
In the production process of any enterprise, some product defects can be distinguished, which arise due to the lack of training of employees, inadequate selection of process parameters, or the condition of the machine. Appropriate methods and tools help to eliminate or prevent the appearance of defects and flaws in a given product. A malfunctioning machine can deteriorate the quality of production because before the source of product defects is located, a certain amount of a factory with a defect may be produced, which translates into financial losses. In the following paper, the authors focused on the study of one of the most frequently damaged machine components: the belt drive. Belt transmissions are among the most frequently used components, and at the same time they are sensitive links of production machines, where their unexpected failure can cause costly downtime and unplanned maintenance. Therefore, it is extremely important to develop methods to ensure reliable, continuous, and economical operation of these devices.