A Multikernel Higher Order Moment Matching Joint Domain Adaptive Network for Cross-Domain Mechanical Fault Diagnosis

With the deepening advancement of smart manufacturing, regular fault diagnosis plays a crucial role in optimizing productivity and maintaining equipment safety and stability. Joint domain adaptation (JDA) techniques for fault diagnosis have garnered significant attention; however, their ability to characterize nonlinear, high-dimensional features remains limited. Additionally, low pseudo-label confidence levels result in insufficient distribution alignment, leading to domain drift that compromises cross-domain fault diagnosis performance. Based on this, this paper proposes a multi-kernel higher-order moment matching joint domain adaptive network (MKHoJDA) for cross-domain mechanical fault diagnosis. Firstly, a multi-scale dilatable residual convolution (MDRC) module is proposed to extract transferable features from both the source and target domains more accurately. Secondly, to address the limitation of traditional JDA in statistically measuring higher-order features, a multi-kernel higher-order moment matching (MKHoMM) statistical metric strategy is designed. Multiple kernel functions are combined to create a more expressive composite kernel, enhancing the power of feature mappings, reducing distribution discrepancies between domains. Additionally, to address the dependency of conditional distribution alignment (CDA) on pseudo-label accuracy, a prototype-aware pseudo-label feedback (PPF) mechanism was developed. This mechanism evaluates sample confidence using Jensen-Shannon (JS) divergence, selects high-confidence target domain samples for pseudo-label generation, and dynamically updates prototype centers. This approach effectively mitigates the negative impact of pseudo-labels while improving CDA performance. Finally, multiple cross-domain fault diagnosis experiments were conducted on the JNU and PU datasets. The average accuracy significantly surpassed that of other advanced comparative models, demonstrating that the proposed algorithm effectively mitigates the impact of data distribution differences and exhibits exceptional cross-domain fault diagnosis performance.