## 1 Introduction  
Hoists are essential heavy machinery widely used in industries such as mining, ports, and chemicals [1; 2], where stable operation is critical to production efficiency and safety. However, prolonged high-load operation can lead to various faults, including gear wear and bearing seat loosening. Bearing failures are particularly prevalent, accounting for 30% to 40% of all machinery malfunctions [3]. If left unaddressed, these issues can significantly reduce operational efficiency and pose safety risks [4]. In coal mining production, the main shaft device of the mine hoist is the core component that supports the normal operation of the entire system, bearing immense load pressures. After prolonged periods of high-load operation, the main bearing can experience performance degradation, leading to failures such as ring fractures [5], blockage [6; 7], cage damage [8], and corrosion [9]. If these failures are not detected and addressed promptly, they could jeopardize the stability and safety of personnel and material transport in the mine, potentially resulting in serious safety incidents. Therefore, implementing real-time monitoring and early warning systems for the hoist can effectively prevent chain reactions caused by faults, underscoring its significant practical importance [10; 11]. Although deep learning technology has shown transformative capabilities in different fields, such as image recognition and natural language processing [12; 13], it encounters distinct challenges in applications like machinery fault diagnosis. Most mechanical fault data are time series signals, such as those generated by rolling bearing vibrations. Unlike images and text, which contain abundant contextual information, time series are recordedcontinuously, capturing only a few scalar values at each timestamp [14; 15]. Furthermore, in the context of hoist operation and maintenance monitoring, the proportion of fault data is often significantly lower than that of normal data. This is particularly evident in real-world industrial scenarios, where issues arise from an abundance of normal samples, a scarcity of fault samples, and insufficient labeled data. These factors contribute to suboptimal training performance and reduced diagnostic accuracy for hoist fault diagnosis models [16; 17], ultimately hindering the broader adoption of intelligent diagnostic technologies in production environments. To address these challenges, this paper aims to leverage recent advancements in deep learning, particularly in convolutional neural networks within the field of computer vision, to develop fault diagnosis methods for hoists based on two-dimensional modeling of time series. By employing a time-series-to-image transformation technique, vibration signals generated during hoist operation are converted into intuitive, information-rich two-dimensional images, which retain essential properties of the time series, such as continuity, periodicity, and trends. Furthermore, a time series two-dimensional model for hoist fault diagnosis is designed by integrating the efficient learning pathways of residual networks with multi-scale feature extraction. This model ensures accurate fault identification under various operating conditions, enabling timely fault warnings that enhance the safety and reliability of the hoist. Overall, the proposed method focuses on improving feature representation fidelity and guiding improved diagnostic outcomes. The main contributions are as follows:  
* The proposed multi-path information fusion fault diagnosis network (MPIFNet), which converts and reconstructs one-dimensional vibration signals from rolling bearings into a two-dimensional format, thereby offering a more detailed and structured view of the information. * A deep learning architecture is designed that integrates the multi-pathways of residual networks to extract multi-scale semantic information. * The experimental findings indicate that MPIFNet achieves a fault diagnosis accuracy exceeding 98.00% across various operating conditions.