# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 1 Introduction  
The mine hoist is a large-scale system integrating machine, electricity, and liquid. It is indispensable transportation equipment for coal mining. The performance of the brake system is directly related to the safety and reliability of the mine hoist. It is of great significance to carry out research on the fault diagnosis of the brake system to ensure the safe operation of the hoist. Disc brake systems are widely used in mine hoisting equipment and are comprised of disc brakes and hydraulic stations. Figure 1 shows the large mine hoist system (Figure 0(a)) in which a mine is operating and the disc brakes (Figure 0(b)) and hydraulic station (Figure 0(c)) of the hoist. Because of the complex structure of the system, there are many uncertain factors and information in actual engineering. Also, there are many complex relationships among faults, and multiple faults occur simultaneously. Hence, it is a complicated process to use existing knowledge to analyze and infer fault diagnosis [1]. In recent years, many researchers have applied artificial intelligence and other new technologies for fault diagnosis, which provides a new idea for the research of mine hoist fault diagnosis methods. To improve mine hoist safety and to prevent the crash of a cage at the shaftboundaries, [PERSON] et al. [2] used a fault tree to analyze the accidents scenarios of a cage crash in a shaft and proposed two generic fault trees. [PERSON] et al. [3] introduced the support vector machine into hoist brake system fault diagnosis, which improved the efficiency of diagnosis greatly. [PERSON] [4] put forward a fault diagnosis classification method based on SOM (Self-Organizing Map), which achieved the first level of diagnosis successfully. In fault diagnosis, a large amount of diagnosis knowledge is the premise of effective fault diagnosis, but at present, the fault diagnosis of large-scale complex mechanical and electrical equipment brake system mainly depends on the traditional fault diagnosis expert system and the experience of field technicians, which does not fully use the existing fault diagnosis knowledge and monitoring data. The stability and correctness of the diagnosis results are based only on successful diagnosis examples and experience, resulting in a lack of scientific theoretical basis for diagnosis decisions, and low diagnostic efficiency, backward knowledge management, etc. To fully use on-site monitoring data and expert experience, the authors of this study used the methods of knowledge engineering, multi-source information fusion, evidence theory, and other techniques to optimize the combination and extraction of this information, and generate diagnostic rules, which provide a reliable basis for fault warning and diagnosis [5; 6; 7; 8]. This has laid the foundation for research on this topic. With the continuous improvement of intelligent sensors and monitoring technology, as well as the increase of measuring points and the increase of sampling frequency, the monitoring data of lifting equipment based on operating conditions has the characteristics of large data, large data dimension and redundant data attributes. Existing diagnostic methods can no longer meet the processing needs of rapid data growth. As the most efficient data processing algorithm in the era of big data, machine learning has developed. This method studies fault characteristics based on data. By collecting the running data of the equipment and analyzing and processing it, it extracts the useful characteristic data to diagnose the system faults. Especially in terms of fault classification, it has advantages unmatched by other algorithms [9]. The commonly used machine learning classification algorithms are a decision tree, Bayesian classification, and support vector machine. [PERSON] et al. [10] used the decision tree construction method to design the fault alarm system, which solved the problems of passive detection of fault detection of mine hoist and inaccurate fault data, detection delay, and so on. Based on the Bayesian theory, [PERSON] et al.