PMMDA based on the fusion of acoustic and vibration signals under time-varying speed conditions for bearing 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.