MALFUNCTION EARLY-WARNING METHOD FOR PRODUCTION LOGISTICS DELIVERY EQUIPMENT

摘要

Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction. The method enables a real-time malfunction early-warning technique for production logistics delivery equipment, thereby providing reference for timely equipment maintenance and avoiding economic damages caused by equipment malfunction and non-operation.