## 2 Related Work  
### Mechanical Fault Diagnosis  
In recent years, the rapid development of artificial intelligence has positioned intelligent fault diagnosis methods as a significant research focus, drawing on advancements in deep learning across various domains, including computer vision, natural language processing, mechanical engineering, etc. According to relevant reviews and studies, current approaches to mechanical fault diagnosis can be categorized into two main types; i.e., the first integrates traditional signal processing techniques with deep learning models for fault diagnosis, while the second directly employs vibration signals as input, leveraging the deep learning model to achieve integrated feature extraction and fault classification [11; 18]. The first type of method often involves converting the original time-domain signal into the frequency domain, thereby facilitating fault diagnosis. For instance, [PERSON] et al. [19] connected and fused data samples from the time, frequency, and time-frequency domains to ensure that the input data encompass information from various transformation domains. They then utilized a Siamese neural network to extract fault features and leverage multi-source sensor data effectively. Similarly, [PERSON] et al. [20] proposed a frequency-domain bidirectional long short-term memory cycle generative adversarial network, which considers the spectrum to contain more informative features than the time-domain signal. They combined the spectral characteristics of the time series signal with the advantages of generative adversarial networks for enhanced fault diagnosis. [PERSON] et al. [21] provided a brief review of fault diagnosis, primarily focusing on traditional machine learning methods. [PERSON] et al. [22] categorized artificial intelligence-based approaches in system health management and further reviewed the applications of deep learning. In actual production settings, the original vibration data of the hoist contains substantial state information closely related to the mechanical equipment. Utilizing deep learning methods to directly extract features from vibration signals provides a more intuitive and accurate reflection of the health status of mechanical systems. To minimize reliance on human expertise in the feature extraction process, some researchers have modified deep learning models to directly process raw mechanical vibration signals. For instance, [PERSON] et al. [23] applied the double tree complex wavelet packet transform to preprocess vibration signals, enhancing the extraction of fault information. They constructed a deep belief network to extract fault-sensitive features from each frequency band, thereby improving the diagnosis accuracy of both rolling bearing compound faults and their severity. [PERSON] et al. [24] utilized an optimized Morlet wavelet to process vibration signals, extracting time-domain features from vibration signals and frequency-domain features from pulse signals. A deep belief network was then trained to achieve fault diagnosis for planetary gearboxes. [PERSON] et al. [25] introduced a dislocation time series convolutional neural network, which captures relationships between signals at different intervals in periodic mechanical signals by incorporating a dislocation layer. [PERSON] et al. [26] proposed a novel data-driven diagnostic methodology using stacked autoencoders, which was valited on two electromechanical systems. The approach demonstrated its effectiveness and adaptability for condition-monitoring strategies across various bearing technologies. The primary limitation of current hoist fault diagnosis technology is its over-reliance on expert knowledge during the data preprocessing stage and the use of non-targeted dimensional transformation methods. Specifically, while depending heavily on expert prior knowledge for data preprocessing and feature selection aims to optimize model inputs, it inevitably introduces human bias, which can adversely affect the model's ability to learn the natural characteristics of the data.