Aug 2026· Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering· 0 citations· 31 references
Abstract
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.
In practical applications of rolling bearings, variations in the measurement data distribution caused by diverse operating conditions result in complicated domain adaptation tasks and significantly impair the effectiveness and generalisability of conventional models. Therefore, this study proposes a frequency-domain-aware adaptive domain alignment network (FADAN). This framework initially transforms the measured vibration signal into the frequency domain and subsequently extracts multiscale spectral features via a parallel multiresolution branch optimised using a frequency-domain attention mechanism. To enhance the generalisation capability, a class-aware domain classifier is employed along with an adaptive gradient-inversion layer, enabling precise and dynamic domain alignment. A comprehensive evaluation across the three datasets demonstrates that FADAN consistently outperforms the domain adaptation methods in the control group in terms of both diagnostic accuracy and stability. These results validate the effectiveness and robustness of the proposed framework and establish it as a generalisable solution for bearing fault diagnosis under complex real-world operating conditions.
Shu-Hao Wang, Peng Shen, Ming-Kai Wang et al.· Engineering Research Express· 0 citations
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.
Yuan Zhuang, Deqiang He, Zhen-Zhen Jin et al.· Measurement science and tech...· 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
In industrial applications, multiple faults occurring at the same time contribute to complex, abnormal signal characteristics compared to a single fault condition. If faults are not monitored and identified early, they will lead to unplanned maintenance, production losses, etc. It is often observed that identifying multi-faults is difficult with single-modality sensors due to the same and biased information. A multi-modality sensor approach would be more relevant and accurate. To address the limitations of single-modality diagnosis, this article proposes an alternative method to the traditional fusion process. In this work, vibration and acoustic signals from test rigs at two different institutes are acquired under various multi-fault conditions and pre-processed further to extract time-frequency features. To bring the features of the two modalities into a single comparable range, a text encoding mechanism is adopted which partitions and texturises them across multiple stages. The texturised vibration and acoustic feature vectors are then fused, and a Large Language Model (LLM) is trained with optimised hyperparameters for multi-fault classification. The proposed vibro-acoustic fusion-based methodology is tested against single-modality multi-fault datasets, and its superiority is observed. Maximum classification accuracies of 96.89% and 97.49% are achieved when the model is trained with system-level and element-level multi-fault conditions, respectively. Moreover, model performance is evaluated using data acquired at various rotating speeds and under different imbalance conditions. The proposed method is useful for multi-fault diagnosis of rotating machines under various conditions and can be helpful for incipient-fault diagnosis.
R. K. Mishra, Won-Ho Jung, Wonjun Yi et al.· IEEE Access· 0 citations
The VA-DFN demonstrates exceptional noise-resistant robustness under varying signal-to-noise ratio (SNR) conditions from −6 dB to 2 dB, achieving a maximum diagnostic accuracy of 99.55%, which is significantly superior to existing single-modality and conventional deep learning baseline models.
Fan-Long Zhu, Jun-Yu Lai, Pei-Wen Lu et al.· Measurement and control (Lon...· 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
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