## 1 Introduction
Hence, with the growing security requirements of hoisting systems, the traditional methods will be difficult to achieve high accuracy, real time and generalization performance. Since 2006, deep learning (DL) [10] has become a rapidly growing research direction [11]. As an important DL algorithm, the convolutional neural network (CNN), has recently become a research hotspot in the field of pattern recognition [12], and is widely used in speech recognition [13], image recognition [14; 15; 16], behavior detection [17; 18], text classification [19] and more. In the field of image recognition, the original image can be put into the CNN directly without complicated pretreatment. Additionally, owing to CNNs' local receptive field, weight sharing, and down sampling, it is highly invariant to image information in the deformation of translation, inclination, scaling, and so on. CNNs have been widely applied because of the aforementioned advantages [20]. Considering the capability of DL to address big data and learn high-level representation, it can be a powerful and effective method for machine health monitoring systems (MHMS) [11]. At present, in the field of MHMS, the CNN-based health monitoring and fault diagnosis of mechanical systems are still in the initial stage of exploration. [PERSON] et al. [21] used a CNN to realize gearbox fault detection and classification. [PERSON] et al. [22] used a CNN to realize fault detection and recognition in the rotating machinery without expert experience. [PERSON] et al. [23] did a comprehensive study of different CNN configurations for automated feature extraction in industrial inspection. [PERSON] et al. [24] successfully developed a one-dimensional (1D) CNN on raw time series data for real-time motor fault detection. [PERSON] et al. [25] proposed a deep convolutional network for spindle bearing fault diagnosis, and they used wavelet packet energy images as the input. [PERSON] et al. [26] also proposed a 1D CNN, which can execute damage detection and structural damage localization in real-time via normalized vibration signals. Fault diagnosis methods based on CNN have only been under development for approximately four years (2015-2018) [11], the CNN-based method is also under great demand to address these challenges. However, although DL technology has great potential, there are still few applications emerging from the research into the health monitoring and fault diagnosis of mechanical systems [27], especially in terms of hoisting systems. Due to the important role of the hoisting system, it is rarely shutdown. Thus, the real-time monitoring of BTRs via machine vision will involve massive images (i.e., big image data). The traditional methods find it difficult to process big data, so it is very suitable to apply CNNs for the diagnosis of BTR faults. Additionally, the research in this paper has great significance because CNNs have not yet been applied in the field of health monitoring and fault diagnosis for hoisting systems' BTRs. This paper presents the design of an online BTR monitoring system based on machine vision and a CNN, that can provide reliable fault warning information, realize the automation of BTR' fault monitoring, and improve the safety of the mine hoisting system. The main contributions of this paper are as follows: (1) The deep learning method is introduced to the health monitoring and fault diagnosis of hoisting systems for the first time, and a CNN method is proposed that diagnoses BTR faults more accurately than k-nearest neighbor (KNN) and artificial neural network with back propagation (ANN-BP) algorithms; (2) A method of establishing a BTR image dataset that can cover the entire feature space is put forward; (3) The same framework can be applied to other health monitoring and fault diagnosis applications where machine vision and CNN are demanded. This paper is organized as follows: the image data-driven monitoring system framework is proposed in Section 2. In Section 3, the principles are introduced and the design of the CNN structure is presented.