Bioinspired‐Algorithmic Synergistic Sensing System toward Broadband Vibration Perception and Compound Fault Diagnosis for Rotating Machinery

Abstract Inspired by the biological mechanism of spiderweb vibration perception, this study designs a spider‐web topology triboelectric vibration sensor (SWT‐TVS). Through bioinspired topological parameter space exploration and machine learning regression optimization, it achieves ultra‐broadband effective perception (5–2000 Hz) and high‐sensitivity response. A physical analytical model for eccentricity vectors was constructed through the differential electrode design. Combined with the adaptive decoupling mechanism of the ResNet‐embedded dual‐branch feature fusion network (DBFFN+ResNet), it eliminated the coupling effect of variable operational disturbances on fault characteristics, achieving 100% identification accuracy for shaft eccentricity parameters under strong disturbances.Through compound fault diagnosis tests on a gearbox, an overall recognition rate of 98.88% is achieved for eight types of compound bearing and gear faults. Furthermore, under multi‐speed conditions (600–1600 rpm), the proposed method attains an average recognition rate of 98.6% for seven types of compound faults in full operational tasks, and maintains 92.1% accuracy in cross‐speed tasks. This bioinspired structure establishes a physical foundation for high‐sensitivity, broadband vibration perception. In synergy with intelligent diagnostic algorithms, it provides a highly reliable solution for vibration monitoring and fault diagnosis in high‐end equipment such as aero‐engines and Computer Numerical Control (CNC) machine tools.