Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

Abstract

The sensor faults in electromechanical equipment are diagnosed and predicted to accelerate the industrial automation and stable operation of electromechanical equipment. Firstly, several basic fault prediction methods are introduced, and the data of electromechanical equipment during production can be well diagnosed and processed through data-driven technology. Secondly, the principal component analysis (PCA) method is introduced, and accordingly, the optimized dynamic principal component analysis (DPCA) method and several common sensor faults are proposed. Thirdly, the specific steps of the PCA and DPCA methods are discussed in sensor faults diagnosis, respectively. The PCA method can study the multivariate nonlinear data concurrently. On this basis, the optimized DPCA method can analyse the dynamic features of the real-time data collected by sensors. Finally, the fault type that produces a fixed deviation is studied. Then, the experiment of the draw-wire displacement sensor is simulated through the optimized PCA and DPCA. The square prediction error (SPE) and prediction-error rate are compared with the error. The results show that both methods can predict the fault of the sensor. The results of the T 2 statistics obtained through the PCA show that the sensor fault can be effectively predicted, but the prediction-error rate and fault-omission rate are also high. In the DPCA algorithm, the SPE statistics show that the DPCA algorithm can predict the fault with a small prediction-error rate and faultomission rate, which is far lower than the PCA algorithm. Therefore, dynamic factors analysis can improve sensor fault prediction, which is of great significance to the acceleration of industrial automation and equipment intelligence.