The technologies of condition monitoring and fault diagnosis for mechanical equipment are critical to ensuring the reliable operation of mechanical systems. However, the characteristic information generated by early equipment failures, such as harmonic components and transient shocks, is often masked by environmental noise. Accurately identifying weak fault features in the presence of strong background noise has become an important research topic in the field of equipment status monitoring and fault diagnosis. Existing linear diagnostic methods are difficult to meet detection needs under complex operating conditions. Therefore, there is an urgent need to develop new technologies to improve the detection capability of weak signals. 

Acoustic metamaterials, as a kind of artificial microstructure with subwavelength scales, enable flexible manipulation of sound waves and exhibit unique advantages in sound field enhancement and noise suppression. Among them, Gradient Acoustic Metamaterials (GAM) , which are composed of spatial variation units or media can realize functions such as acoustic enhancement and sound rainbow trapping, providing a novel approach to the detection of weak fault signals. 

Based on the strong wave compression effect coupled with the equivalent medium theory, this paper proposes a series of acoustic enhancement and weak signal detection methods for optimizing GAM. The feasibility of the structural design was verified through simulation and experiments, providing a new solution for the detection of weak fault signals. Finally, based on the gear frequency modulation/amplitude modulation principle and the improved frequency selection characteristics of GAM, the enhanced detection of gear fault signals was achieved, which has broad engineering application prospects.

Professor Jianning Han received his Bachelor's degree in Electronic Information Engineering from North University of China in 2003. He subsequently obtained his Master's degree in Information Processing and Reconstruction in 2010 and his Ph.D. in Signal and Information Processing in 2015, both from North University of China. From April 2018 to April 2019, he served as a senior visiting scholar at the University of South Florida, USA, and in 2015, he was a visiting scholar at Saitama Institute of Technology, Japan. He is a peer review expert for the National Natural Science Foundation of China (NSFC) and a registered expert in the National Science and Technology Expert Database. From July 2003 to 2023, he held academic positions as Lecturer, Associate Professor, and Professor at the School of Information and Communication Engineering, North University of China. Since September 2023, he has been serving as a Professor and Vice Dean at College of Physics and Information Engineering, Shanxi Normal University. He has published over 80 academic papers, including 28 indexed in SCI and EI journals. His research interests include signal processing techniques, acoustic metamaterial design and sound field (signal) manipulation, as well as vibration and noise control.