# An Evidential Reasoning Assessment Method Based on Multidimensional Fault Conclusion
###### Abstract
By leveraging the latent space's compact and disentangled data representations, this method offers a more robust and accurate measure of machine health compared to traditional reconstruction errors. Although some success has been achieved, autoencoders are considered black-box models, which lack sufficient interpretability when handling complex systems, particularly in applications involving intricate systems like high-speed train running gear. Traditionally, these methods require extensive monitoring data to achieve accurate health status assessments. However, obtaining such large datasets can be challenging and may limit the applicability of these approaches. Nonetheless, there are emerging methods designed to perform well even with limited data [16; 17; 18]. For example, recent work by [PERSON] et al. [18] introduced TabPFN, an approach specifically aimed at early fault classification in rotating machinery with limited data. This method shows that effective assessments can be made with a minimal amount of data, challenging the notion that extensive data are always necessary. As such, it is crucial to consider both data-rich and data-limited scenarios when evaluating the effectiveness of data-driven methods. (3) Methods Based on Semi-Quantitative Information  
These methods successfully combine quantitative data with qualitative knowledge, enabling the representation of various uncertainties. [PERSON] et al. [19] tackled the challenge of inaccurate expert-provided parameters in traditional Belief Rule-Based (BRB) models and the possible decline in model interpretability post-optimization. A new approach is presented for assessing the health condition of intricate systems. [PERSON] [20] highlighted the absence of adequate methods to improve interpretability during the optimization stage of BRB models and the inclination of BRBs to rely on black-box models that prioritize accuracy alone. A new interpretable health status assessment method was proposed. [PERSON] et al. [21] tackled the reliability assessment problem in train control and management systems by proposing an evidential reasoning (ER) rule-based model. This model boasts strong reliability modeling capabilities, interpretable assessment processes, and the ability to describe assessment results under probabilistic and ignorance uncertainties, demonstrating robust performance. [PERSON] et al. [22] noted that existing evidential reasoning models typically set the evidence reference point as a quantitative value or interval value, making it challenging to handle both fuzzy and probabilistic uncertainties simultaneously. A distributed reference point aerospace relay performance assessment model based on evidential reasoning rules is proposed. Evidential reasoning is a typical semi-quantitative information method that leverages various quantified uncertain information and expert knowledge. It has performed well in non-linear health status assessments of complex electromechanical systems. Assessing the running gear system requires monitoring multiple indicators to reflect the system's status accurately. Evidential reasoning can effectively integrate multi-source information indicators, achieving a thorough assessment of the running gear system's health condition. Its unique feature lies in fully considering the weight and reliability of each evidence, enabling the handling of fuzziness, uncertainty, and incompleteness while effectively utilizing qualitative knowledge. This is particularly beneficial for welding robots, which often lack sufficient data. Despite significant achievements, the complex and highly interconnected structure of high-speed train running gear systems poses challenges in obtaining comprehensive health indicator data. Additionally, such data often lack sufficient detail to reveal the system's failure modes, complicating the assessment process. Traditional ER assessment methods typically integrate multiple indicators [23].