## 1 Introduction
Through the application of an overlapping segmentation strategy, class-specific dictionaries were designed to exploit both local and non-local features in data segmentation, ultimately enabling health state identification through a sparsity-based diagnostic approach. Furthermore, [PERSON] [22] developed an automatic recognition algorithm to extract structural state information from rotating machinery foundation systems under various operating conditions, including ramp-up (accelerated shaft rotation). This algorithm integrates Operational Modal Analysis (OMA), hierarchical clustering, and k-means clustering to automatically identify stable states. In another study, [PERSON] [23,24] converted vibration signals into gray texture images and explored intelligent fault diagnosis methods for rotor-bearing systems in motors with variable working conditions, focusing on improving model generalization and reducing model complexity. Through the enhancement of the CNN model and introduction of AdaBN, the transfer diagnosis of the motor rotor-bearing system from known to unknown conditions was successfully conducted. Through techniques such as adaptive activation, classifier structure optimization, and multi-scale feature extraction, the accurate diagnosis of the motor rotor-bearing system was achieved while reducing model parameters and FLOPs. Research on Multimodal Feature Extraction for Equipment Operational State. [PERSON] [25] presented a comprehensive mathematical framework for the analysis and diagnostic application of vibration and acoustic emission (AE) signals in electrical equipment (EE). Key contributions include the establishment of a vibration model that simulates the multi-resonant system's response to generating impulses, reflecting the dynamics of rolling bearings, and a model for AE that integrates continuous and discrete signal components. [PERSON] [26] addressed the issue of inadequate modality representation and insufficient exploration of intrinsic characteristics by proposing a multimodal neural network model. After a two-dimensional modal transformation of time-domain signals was performed, an information fusion mechanism was introduced that combines a continuous wavelet transform with symmetric point diagrams, resulting in a two-level information fusion-based multimodal CNN architecture. [PERSON] [27] provided effective tools and theoretical support for assessing equipment conditions by establishing mathematical models and identification methods for vibroacoustic signals and utilizing the Pearson curve system to statistically analyze vibroacoustic signals from power industry objects. Similarly, [PERSON] [28] proposed an end-to-end, deep clustering-based health state identification method for rotating machinery using multimodal fusion, specifically designed to handle completely unlabeled application scenarios. In another approach, [PERSON] [29] introduced a multimodal recognition method that combines vibration signals with thermal images. This method leverages thermal imaging technology to intuitively capture thermal changes during gearbox operation and integrates vibration signals to provide a comprehensive reflection of state features. [PERSON] [30] developed a rotating machinery health state identification method based on multimodal information fusion and coordinate attention mechanisms. By integrating information at the data, feature, and decision levels, the method enhances the accuracy of state identification. Furthermore, [PERSON] [31] designed an end-to-end, multi-task, multimodal fusion network (M2 FN) for intelligent state diagnosis. This approach extracts discriminative features from multimodal data to achieve accurate and reliable diagnoses. Lastly, [PERSON] [32] proposed a collaborative fusion convolutional neural network. This method incorporates a multi-scale shrinkage denoising module to extract hierarchical, modality-specific features from different modalities. Additionally, a central fusion module was introduced to explore intrinsic correlations and integrate crossmodal features.