A novel cross-domain fault diagnosis method for multi-condition industrial processes based on meta-domain adaptation with progressive meta-learning.

Complex industrial processes are characterized by high dynamics, diverse operating conditions, and strong inter-system coupling, often leading to reduced production efficiency and product quality fluctuations. Employing advanced fault diagnosis technologies has become an effective approach to support high-quality and efficient execution of industrial processes. However, the increasing prevalence of customized manufacturing has introduced substantial variability in working conditions, under which traditional fault diagnosis methods struggle to perform effectively. Each working condition can be abstracted as a domain. Therefore, employing domain adaptation techniques to achieve multi-condition fault diagnosis is one of the key approaches to addressing the above challenge. Based on the above observation, a novel neural network-based cross-domain fault diagnosis method for multi-condition industrial processes via meta-domain adaptation with progressive meta-learning is proposed. First, an adversarial dual-scale neural network is designed to address the challenge of feature alignment across multiple source domains, comprising a one-dimensional convolutional neural network feature extractor and a multi-layer perceptrons domain discriminator. A progressive adversarial strength adjustment strategy is proposed to better extract domain-invariant yet discriminative shared features, thereby enhancing domain generalization. Second, to tackle practical issues such as imbalanced condition distributions, limited sample availability, and intra-source heterogeneity, a meta-learning mechanism is employed to reduce internal distributional discrepancies within source domains. Additionally, multi-kernel maximum mean discrepancy is employed to explicitly align source and target features, facilitating robust generalization under substantial domain shifts. Finally, the constructed cross-domain feature extractor and fault classifier are used to achieve fault diagnosis in industrial processes. The proposed method is evaluated on the benchmark Tennessee Eastman process and a real hot strip mill process, demonstrating its effectiveness and superiority.