Soft Measurement Modeling of Production Line Equipment Based on ALGWO-Elman

Due to the complexity and diversity of the devices and the limitations of the range and cost of sensor applications. Fault detection information cannot be obtained completely and accurately. To address the above problems, Grey wolf optimization algorithm for adaptive leadership Elman network (ALGWO-Elman) is proposed. The Elman network is first used as the infrastructure for the soft measurement technique. The Grey wolf optimization algorithm (GWO) is combined with Elman network to improve the learning ability and generalization of soft measurement techniques. Nonlinear convergence factor and changing the number of head wolves with iteration stages were introduced into the grey wolf algorithm to improve the convergence speed and prediction accuracy of the algorithm. Simulation analysis shows that the ALGWO-Elman prediction accuracy reaches 95.9%, which effectively improves the accuracy of equipment fault prediction compared to other intelligent algorithms.