AttenResNet18: Cross-Domain Fault Diagnosis Model | Sciencefather #Researcherawards

Introduction

Cross-domain fault diagnosis for rolling bearings has emerged as a critical research focus in the field of intelligent maintenance and mechanical system health monitoring. Traditional diagnostic methods often struggle when confronted with data shifts across different domains, leading to significant performance degradation. The growing demand for robust and accurate fault detection techniques necessitates the development of innovative approaches that not only align feature distributions effectively but also handle noisy conditions with resilience. In this context, the introduction of the Attention-Enhanced Residual Network (AttenResNet18) offers a transformative solution by combining advanced neural network architectures with adaptive distribution alignment mechanisms, thereby addressing key limitations in existing domain adaptation strategies.

Challenges in Cross-Domain Fault Diagnosis

A major challenge in cross-domain fault diagnosis is the distribution mismatch that occurs when the training and testing data originate from different working conditions or sensor setups. Most existing methods attempt to mitigate this issue using domain adaptation techniques, but they frequently ignore the crucial differences between marginal and conditional distributions. Moreover, the presence of noise in vibration signals further complicates reliable diagnosis, leading to decreased robustness and reduced detection accuracy. These challenges highlight the need for advanced methodologies that can selectively capture relevant features, filter out noise, and dynamically balance distribution alignment to ensure high-precision diagnostic outcomes.

Attention-Enhanced Residual Network (AttenResNet18)

The proposed AttenResNet18 model introduces a one-dimensional attention mechanism embedded within a residual network structure, enabling the network to dynamically assign importance to different positions in the input sequence. This allows the system to capture long-range dependencies and essential discriminative features, ensuring that critical fault-related patterns are not overshadowed by irrelevant noise. By integrating attention into the residual learning framework, the model enhances feature representation and robustness, making it particularly effective in noisy environments and diverse cross-domain applications.

Dynamic Balance Distribution Adaptation (DBDA)

To address the limitations of conventional domain adaptation methods, the study proposes the Dynamic Balance Distribution Adaptation (DBDA) mechanism. This approach introduces an MMD-CORAL Fusion Metric (MCFM), which integrates Maximum Mean Discrepancy (MMD) with CORrelation ALignment (CORAL). By fusing these complementary techniques, the method captures both marginal and conditional distribution shifts with greater accuracy. Additionally, an adaptive factor dynamically regulates the balance between the two distributions, allowing the model to achieve better generalization and adaptability to new, unseen tasks. This adaptability is crucial in real-world fault diagnosis scenarios where domain variations are inevitable.

Experimental Validation and Results

Extensive experiments conducted on two benchmark rolling bearing datasets demonstrate the effectiveness of the proposed AttenResNet18 framework with DBDA. The model achieves an impressive average accuracy of 99.89%, significantly surpassing the performance of existing state-of-the-art methods. The results validate that the integration of attention mechanisms and dynamic distribution adaptation not only enhances fault feature extraction but also ensures robustness against noise disturbances. These findings highlight the potential of this approach to serve as a reliable diagnostic tool in industrial applications, offering superior precision in detecting and classifying bearing faults.

Research Significance and Future Directions

This research represents a substantial advancement in cross-domain fault diagnosis, providing a framework that is both noise-resilient and distribution-aware. The combination of AttenResNet18 with the DBDA mechanism establishes a new benchmark in fault detection accuracy and adaptability. Future work may explore extending this methodology to other rotating machinery components, incorporating multi-sensor data fusion, and integrating real-time monitoring capabilities. Additionally, further investigation into lightweight implementations of the model could enable deployment in resource-constrained industrial environments, paving the way for smarter and more sustainable predictive maintenance systems.

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