Jul 2026· Measurement science and technology· Vol 37, pp. 316102· 0 citations· 49 references
Physics
TL;DR
A novel vibration–acoustic multimodal contrastive learning framework designed to jointly regularize vibration–acoustic features at the levels of sample distribution, feature statistics, and feature structure enhances multi modal consistency, feature discriminability, and information diversity.
Abstract
In order to address common challenges in industrial environments, including limited labeled samples, variable operating conditions, and cross-machine fault diagnosis, this study proposes a novel vibration–acoustic multimodal contrastive learning framework. First, a multi-level constrained contrastive learning method is designed to jointly regularize vibration–acoustic features at the levels of sample distribution, feature statistics, and feature structure. This enhances multi modal consistency, feature discriminability, and information diversity, enabling the learning of robust joint representations. Second, a dynamic frequency domain collaborative enhancement module is introduced during the fine-tuning stage, which progressively integrates frequency domain features into vibration time series features while adaptively adjusting feature weights, thereby improving discriminative capability and representation stability. Finally, the proposed framework is validated on two motors with significant differences in structure and operating conditions, as well as on a series of variable condition and cross-machine fault diagnosis tasks. Experimental results demonstrate that, with only 10 labeled samples per class, the method achieves over 99.57% diagnostic accuracy in cross-machine tasks, highlighting its adaptability to distribution shifts and structural differences and confirming its robust generalization performance in cross-machine and cross condition fault diagnosis.
In engineering applications, mechanical equipment must adapt to complex and dynamic working environments, where the rotational speed often varies over time, resulting in significant distribution discrepancies across different operating conditions. Meanwhile, information obtained from a single vibration signal is often insufficient and susceptible to external interference. Traditional single-source domain adaptation methods may suffer from negative transfer and fail to effectively exploit complementary knowledge from multiple source domains for target-domain fault diagnosis, resulting in reduced reliability and generalization performance of diagnostic models. To address these limitations, this paper proposes a Progressive Multi-Dimensional Multi-Source Domain Adaptation (PMMDA) method. From the perspective of collaborative utilization of multi-source data, the proposed method integrates multimodal information from vibration and acoustic signals and employs a multi-level feature alignment strategy to achieve progressive alignment between source and target domains. Additionally, an adaptive weighting mechanism is introduced to dynamically balance the contributions of different source domains during model training, thereby enhancing the overall learning performance. Experimental results on two sets of bearing fault diagnosis tasks under time-varying rotational speed conditions demonstrate that the proposed method can effectively mitigate the impact of distribution discrepancies, significantly improving the accuracy and generalization capability of the diagnostic model, and verifying its potential and reliability in complex operating conditions.
He Qin, Zhongwei Zhang, Xinyu Li et al.· Proceedings of the Instituti...· 0 citations
A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.
Canan Taştimur· Information Technology and C...· 0 citations
The proposed MSFormer incorporates a parallel multi-scale Convolutional Neural Network architecture and hierarchical Transformer modules to comprehensively process 1D vibration signals to provide a powerful and precise intelligent solution for mechanical fault diagnosis.
Shu Guo, Jin Li, Tian-Ci Zhang· Machines· 0 citations
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
DaXin Li, Hong Wang, Hai Xue et al.· Engineering Research Express· 0 citations
A four-branch multi-modal unsupervised domain-adaptive fault diagnosis framework, termed CRG-DA Net, based on ConvNeXt and ResNet1D, aimed at enabling cross-condition fault diagnosis under unlabeled target data is proposed.
Zhihao Zhao, Li Xu, Jingjing Cai et al.· Measurement and control (Lon...· 1 citation
An acoustic–vibration fusion method for bearing fault diagnosis based on a multi-scale Swin–CNN hybrid architecture that employs a Bayesian optimization-based tunable Q-factor wavelet transform (BO-TQWT) to enhance fault-sensitive subbands under low signal-to-noise ratio conditions, and converts acoustic and vibration signals into two-dimensional time–frequency maps.
Mengran Liu, Zhao-Tao Du, Zhen-Xiang Xiong et al.· Measurement science and tech...· 0 citations
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