# Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System
## 1 Introduction  
The implementation of Industry 4.0 involves a major focus on maintenance [1]. Traditionally, a preventive or corrective technique would be carried out based on inspections and replacements that have been scheduled or based on repairing breakdowns when they occur. However, in the last decades, predictive maintenance has acquired great relevance because it is based on the anticipation of failures and the minimization of downtime [2, 3]. Its axis of execution is the continuous monitoring of equipment, using advanced technologies and techniques to collect real-time data on its internal state. In this way, key information can be interpreted in order to determine if an intervention is necessary. This information is examined using algorithms and artificial intelligence techniques to obtain and identify patterns or trends that may indicate possible failures or imminent problems. With these procedures, action can be taken before a breakdown occurs, avoiding costly downtime and improving efficiency and productivity in the industry [4, 5]. Transmission belts are one of the key units in many industrial pieces of equipment, since, as elements of power transmission and movement between different components, they are present in practically all types of machinery [6, 7]. They should be considered as delicate elements since they are subject to permanent erosion due to friction, tension and environmental conditions [8, 9]. For this reason, predictive maintenance is the most appropriate methodology to guarantee optimal performance. In this technique, condition monitoring is crucial, since the reception of digital signals from sensors located on the machinery allows for obtaining relevant data. These measurements are the first phase in fault diagnosis and allow for a Remaining Useful Life (RUL) study on a particular component or equipment. In this way, it is possible to develop accurate predictive maintenance. Monitoring the condition of the transmission belts can be carried out using various techniques and technologies [10; 11]. Although the most common is visual inspection, it is not adequate, since it only allows the visible problems to be detected and this, in addition to not providing an exhaustive evaluation of the internal condition of the belt, can result in a too-late evaluation. Therefore, the use of vibration sensors is usually chosen [12; 13]. Vibration monitoring allows for detecting anomalies, which may be indicative of misalignments, imbalances or tension problems in the transmission belts, allowing corrective measures to be taken before catastrophic failure occurs [14]. In addition to monitoring, the processing of recorded data is the second principal element of predictive maintenance. In this sense, due to the great evolution of artificial intelligence techniques, these represent powerful information processing tools that make it possible to anticipate failures and minimize downtime in the industry. In the last decades, there has been a great expansion in the application of signal processing techniques. Machine Learning (ML) techniques have been widely used owing to their successful results in predictive problems [15]. However, they are considered black boxes, since it is not possible to establish a relation between their inputs and outputs. This involves an important limitation because there is a lack of capability for interpretation in their learning. Meanwhile, the relevance of Artificial Neural Network (ANN) in multiple fields of research should be pointed out, but they stand out especially in fatigue modeling [16], in classification problems or life predictions [17]. The principal advantages reside in the excellent adaptivity, brilliant self-learning properties and important capabilities for extracting relations in complex data sets [18].