Health-diagnosis of electromechanical system with a principal-component bayesian neural network algorithm

Abstract

This study proposes a cloud tele-measurement technique on an electromechanical system, and uses a neural network algorithm based on principal-component analysis (PCA) to quickly diagnose its performance. Three vibration, three temperature, electrical voltage, and current sensors were mounted on the electromechanical system, and the external braking device was used to provide different load-states to simulate the operating states of the motor under different conditions. Moreover, a single-chip multiprocessor was used through the sensor to instantly measure the various load-state simulations of the motor. The operating states of the electromechanical system were classified as normal, abnormal, and required-to-be-turned-off states using a principal-component Bayesian neural network algorithm (PBNNA), to enable their quick diagnosis. Furthermore, PBNNA successfully reduces the dimensionality of the multivariate dataset for rapid analysis of the electromechanical system's performance. The accuracy rates of health-diagnosis based on the Bayesian neural network algorithm and PBNNA models were obtained as 97.7% and 98%, respectively. Finally, the single-chip multiprocessor based on PBNNA is used to automatically upload the measurement and analysis results of the electromechanical system to the cloud website server. The establishment of this model system can optimize prediction judgment and decision-making based on the damage situation to achieve the goals of intelligence and optimization of factory reconstruction.