一种面向旋转机械多传感器故障诊断的模态融合深度聚类方法  

A Modal Fusion Deep Clustering Method for Multi-sensor Fault Diagnosis of Rotating Machinery

作　　者：伍章俊[1,2] 许仁礼 方刚 邵海东[4] WU Zhangjun;XU Renli;FANG Gang;SHAO Haidong(School of Management,Hefei University of Technology,Hefei 230009,China;Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China;Ministry of Education Engineering Research Center for Intelligent Decision-Making&Information System Technologies,Hefei 230009,China;College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]过程优化与智能决策教育部重点实验室,合肥230009 [3]智能决策与信息系统技术教育部工程研究中心,合肥230009 [4]湖南大学机械与运载工程学院,长沙410082

出　　处：《电子与信息学报》2025年第1期244-259,共16页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(52275104);湖南省创新平台与人才计划(2023RC3097)。

摘　　要：针对单传感器和单模态信号特征信息不足的问题,该文提出一种基于多模态融合的端到端深度聚类旋转机械多传感器故障诊断方法(EDCM-MFF)。首先,利用门控递归单元自编码模块提取多传感器故障信号的深度时序特征。然后,应用短时傅里叶变换(STFT)将故障信号转换为时频图像,并通过卷积自编码器提取这些图像的深度空间特征。接着,设计了一种模态融合注意力机制,通过计算不同模态深度特征之间的亲和矩阵,实现模态特征的融合。最后,采用Kullback-Leibler(KL)散度聚类,以端到端方式实现故障类型的识别。实验结果显示,该方法在东南大学齿轮箱和轴承数据集上的识别准确率分别为99.16%和98.63%。与现有的无监督学习方法相比,所提方法能够更有效地实现多传感器和多模态的旋转机械故障诊断。Objective Rotating machinery is essential across various industrial sectors,including energy,aerospace,and manufacturing.However,these machines operate under complex and variable conditions,making timely and accurate fault detection a significant challenge.Traditional diagnostic methods,which use a single sensor and modality,often miss critical features,particularly subtle fault signatures.This can result in reduced reliability,increased downtime,and higher maintenance costs.To address these issues,this study proposes a novel modal fusion deep clustering approach for multi-sensor fault diagnosis in rotating machinery.The main objectives are to:(1)improve feature extraction through time-frequency transformations that reveal important temporal-spectral patterns,(2)implement an attention-based modality fusion strategy that integrates complementary information from various sensors,and(3)use a deep clustering framework to identify fault types without needing labeled training data.Methods The proposed approach utilizes a multi-stage pipeline for thorough feature extraction and analysis.First,raw multi-sensor signals,such as vibration data collected under different load and speed conditions,are preprocessed and transformed with the Short-Time Fourier Transform(STFT).This converts time-domain signals into time-frequency representations,highlighting distinct frequency components related to various fault conditions.Next,Gated Recurrent Units(GRUs)model temporal dependencies and capture long-range correlations,while Convolutional AutoEncoders(CAEs)learn hierarchical spatial features from the transformed data.By combining GRUs and CAEs,the framework encodes both temporal and structural patterns,creating richer and more robust representations than traditional methods that rely solely on either technique or handcrafted features.A key innovation is the modality fusion attention mechanism.In multi-sensor environments,individual sensors typically capture complementary aspects of system behavior.Simply concatenating their outputs

关 键 词：旋转机械 故障诊断 多模态融合 深度聚类 

分 类 号：TN911.7[电子电信—通信与信息系统] TH133[电子电信—信息与通信工程] TP183[机械工程—机械制造及自动化]