## 1 Introduction  
Rotating machinery health status assessment is a key factor for the safe operation of equipment [1, 2]. Once a fault occurs, it is often difficult to quickly identify the specific cause, which may lead to equipment damage, downtime, and production halts, resulting in economic losses or even major accidents that threaten workers' safety. As a crucial aspect of fault prediction and health management, health status assessment enables the accurate and timely evaluation of a machine's current degradation state and prediction of its remaining useful life, ensuring operational safety. This not only directly improves the working efficiency of mechanical equipment but also indirectly reduces maintenance costs through early warnings. Therefore, researching health status identification methods for rotating machinery is of great practical significance for enhancing its operational reliability and stability. Currently, health state identification methods can be broadly categorized into three main types: knowledge-based, model-driven, and data-driven approaches. Knowledge-based methods are applied in systems where model development is challenging or where nonlinearity is significant. These methods rely on theoretical knowledge and expert experience for state assessment. Prominent techniques include expert systems (ESs) [3,4], fault tree analysis (FTA) [5, 6, 7], the analytic hierarchy process (AHP) [8], and fuzzy comprehensive evaluation [9,10]. Although these methods provide a comprehensive system evaluation with strong interpretability and relatively simple operation, they are limited to qualitative analysis, are highly subjective, and lack generalizability. Furthermore, they exhibit low levels of digitization and intelligence, resulting in generally lower accuracy. The core idea of model-driven methods is to develop performance degradation models based on physical and chemical principles or operational data analysis to characterize the system's health state. These methods rely on analyzing mechanical system operations and fault mechanisms to construct models that describe equipment performance degradation, facilitating health state assessment. Common techniques include the cloud center of gravity method [11], mechanism-based modeling [12,13], and state estimation approaches [14, 15, 16]. Although these approaches provide relatively accurate assessment results, they involve complex modeling processes, high operational costs for large and intricate machinery, and challenges in model validation and practical implementation. Additionally, they exhibit poor adaptability and generalization capabilities. To overcome the limitations of knowledge-based methods, which require extensive prior knowledge, and model-driven methods, which struggle with complex mathematical degradation modeling, the use of data-driven health state identification methods has gained significant attention [17]. Researchers have primarily concentrated on the two aspects outlined below. Research on Single-Modality Feature Extraction for Equipment Operational State. [PERSON] [18] proposed a one-dimensional deep convolutional neural network (1D-DCNN), which directly learns features from vibration signals to identify the various health conditions of gears. Similarly, [PERSON] [19] employed efficient machine learning techniques to extract acoustic features from sound data related to the operational state of automotive gearboxes, thereby developing a systematic approach for gearbox state identification. In a different approach, [PERSON] [20] introduced a feature extraction method based on time-frequency image data, representing both the time and frequency information of signals through two-dimensional time-frequency images. Additionally, [PERSON] [21] proposed a novel Sparse-Assisted Intelligent Recognition method, which utilizes prior knowledge of shift-invariance prediction.