Research on monitoring and anomaly detection algorithm for human operation process of smart mobile phone devices based on hand eye clothing

In order to improve the fault detection capability of flexible load grasping robots, a neural network-based automatic diagnosis method for concurrent faults in robots is proposed. Using high-resolution intelligent sensor information recognition technology, combined with analysis of mechanical structural characteristics such as stiffness and strength, a fault information collection model for flexible load grasping robots is constructed. The principle of variable stiffness is adopted to extract the oscillation information characteristics of the flexible load grasping robot. Spectral feature detection and dynamic information fusion are used to perform multi-resolution fusion and feature clustering processing of the fault information of the flexible load grasping robot. By analyzing the estimated values of the parameters of the fault sample information data, the information data is reorganized, and the fault category is preliminarily judged and identified based on the differences in the sampled information. Using BP neural network technology, fault features are extracted through feature distribution functions for optimized diagnosis and adaptive learning of concurrent faults in robots, improving the effective detection and recognition capabilities of concurrent faults in robots. The simulation results show that using this method for robot concurrent fault diagnosis has good adaptability, strong feature identification ability, and good fault monitoring and pattern recognition capabilities.