Predictive Health Diagnosis Model for Industrial Equipment Integrating Global Dynamic Perception and Collaborative Optimization

Abstract

Industrial equipment poses significant challenges to industrial operations and predictive maintenance due to its high failure rate under complex operating conditions. Existing AI-based diagnostic methods ignore endpoint effects in nonstationary signals and fail to comprehensively quantify the semantic correlation features extracted by the model, resulting in a lack of fine-grained features. To address these issues, a diagnostic framework integrating global dynamic perception and collaborative optimization is proposed, aiming to develop a synergistic optimization framework combining adaptive exponential decay particle swarm optimization with empirical mode decomposition. Furthermore, a multiscale entropy-based attention mechanism is employed to enhance perception of fine-grained feature information, enabling predictive diagnostics. Specifically, by minimizing endpoint fluctuation metrics and rationalizing kurtosis indicators as objective functions, the collaborative optimization framework adaptively adjusts the inertia weight and learning factor combination of the regression model to suppress endpoint effects in signal decomposition. Additionally, a global dynamic perception attention mechanism is proposed by integrating wavelet entropy and sample entropy to construct the extraction of feature semantic association information. Experiments demonstrate that the proposed method achieves an accuracy rate of 99.2% in health diagnosis tasks, showcasing the advantages of AI-based diagnostics.