Leveraging Physics-Informed Transformers for Advanced Signal Compression and Reconstruction in Machinery Fault Diagnosis
Authors: Zhou Qianyu , Tang Jiong 
Published: January 2025

In the realm of smart manufacturing, where continuous monitoring and diagnostics play crucial roles in enhancing efficiency and reliability, managing the deluge of data from machinery sensors poses significant challenges. This advanced manufacturing approach integrates data analytics and automation to optimize operations, necessitating efficient data compression and reconstruction strategies for wireless transmission. The high-volume data streams generated by components like gearboxes and bearings require adept handling to overcome bandwidth limitations and energy constraints, crucial for maintaining system performance and sustainability in real-time health monitoring systems. Thus, efficient data handling becomes paramount, not just for minimizing transmission costs but also for ensuring that vital diagnostic information is preserved and accurately analyzed in cloud-based or edge computing platforms. Addressing the need for efficient data handling, this study presents an effective solution, the physics-informed Transformer for signal compression and reconstruction. This method is specifically designed to improve machinery diagnosis by compressing sensor data for streamlined transmission and reconstructing it with high fidelity to preserve vital diagnostic details. The effectiveness of our method lies in leveraging the transformer model, chosen for its exceptional ability to handle complex data sequences and its adaptability to the intricate nature of vibration signals. Transformers excel in extracting meaningful patterns from data, making them an ideal choice for the tasks of signal compression and reconstruction in the context of machinery diagnosis. This approach is further enhanced by incorporating physics-informed principles, subtly weaving domain-specific knowledge into the model’s operations. By comparing domain-specific representations between the original and reconstructed signals, we ensure that essential diagnostic information is accurately captured and preserved. This methodology allows for the faithful representation of critical machinery health indicators in the reconstructed signal, facilitating the precise identification of faults in gearboxes and similar machinery without delving into the specifics of the underlying physics-informed mechanisms. The evaluation of our physics-informed Transformer model was conducted using an exclusive, in-house gearbox dataset that meticulously captures a broad spectrum of fault conditions. This rich dataset provided the groundwork for an exhaustive validation of our innovative approach, demonstrating its significant advantages over traditional data compression and reconstruction methodologies, which typically fail to account for the nuanced dynamics prevalent in machinery signals. The comparative analysis revealed that our physics-informed model not only substantially improves upon the benchmarks set by conventional techniques in terms of compression efficiency and the accuracy of signal reconstruction but also excels in retaining indispensable diagnostic details. This preservation of critical information underpins the model’s enhanced capability to diagnose faults accurately, a feature that proves invaluable across varied and rigorously demanding operational conditions. The physics-informed framework thereby emerges as a highly effective solution, adept at navigating the intricacies of real-world machinery diagnosis with exceptional precision and reliability.