Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (1804.07265v1)

Abstract: In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.

Deep Transfer Network with Joint Distribution Adaptation for Intelligent Fault Diagnosis

The paper presents an advanced architecture termed as Deep Transfer Network (DTN) integrated with Joint Distribution Adaptation (JDA), aimed at enhancing fault diagnosis in industrial applications. This work emerges from the growing need to rely on intelligent systems for fault diagnosis in modern, increasingly complex machinery where re-training diagnostic models for new tasks or unseen conditions poses a substantial challenge.

Key Contributions and Methodology

The authors propose a DTN framework that extends traditional CNN architectures by incorporating a transfer learning approach, specifically JDA. This combination facilitates the adaptation of a pre-trained diagnostic model from a source domain with labeled data to a target domain, which is typically unlabeled, by minimizing discrepancies in statistical distributions across these domains. The framework is structured around the core idea of leveraging both marginal and conditional distribution adaptation to ensure robust domain adaptation.

The framework's architecture employs unsupervised domain adaptation, which is crucial in real-world industrial scenarios characterized by a lack of labeled target data and the inherent variability of operational conditions. Unlike conventional methods that only focus on aligning marginal distributions, this approach emphasizes the adaptation of conditional distributions, which is critical in fault diagnosis contexts given the sensitive nature of feature discrimination required for accurate fault classification.

Empirical Evaluation and Results

Through extensive empirical testing on three distinct datasets—wind turbine faults, bearing faults, and gearbox faults—the framework demonstrated superior transfer performance. Notably, it achieved state-of-the-art results across a range of diverse scenarios, including various operating conditions, fault severities, and fault types, highlighting the efficacy of incorporating JDA. Specifically, DTN with JDA consistently outperformed other methods, with an average accuracy of 97.5%, marking a notable improvement over traditional CNN models.

Moreover, the specialized capability of JDA in avoiding negative transfer is emphasized, where transferring knowledge without explicit target domain supervision might usually degrade performance in hard transfer tasks. This capability underscores the practical benefits of the proposed framework over marginal-only adaptation techniques like MDA.

Implications and Future Directions

The implications of this research extend beyond theoretical advancements into practical applications in condition monitoring systems within industrial settings. The ability to efficiently transfer diagnostic models without substantial labeled data reduces the precedence for costly data annotation processes and enables rapid deployment across varied equipment and fault scenarios.

In terms of future research directions, the exploration of this framework in real-world settings with imbalanced fault distributions presents a pertinent avenue. Additionally, extensions of this work could also delve into optimizing computational efficiency while maintaining high transfer accuracy, which is crucial for real-time fault detection applications.

Overall, the DTN with JDA presents a significant stride towards resolving challenges posed by domain discrepancies, marking a promising advancement in the field of industrial fault diagnosis through deep learning and transfer learning methodologies.