Diagnosis for load cells in truck scale based on information fusion
基于信息融合的汽车衡称重传感器故障诊断

Conventional truck scale without fault diagnosis will be disabled when anyone load cell is going wrong in operation. A fault-diagnosis method for load cells is proposed based on information-fusion technique. The radial-basisfunction- neural-network(RBFNN) with a training algorithm is employed to approximately model the internal relations among load cells for predicting their outputs. The prediction outputs together with the real outputs of the load cells are sent to a fusion-detection model developed by us. This model employs the criterion of voting-fusion-diagnosis to generate the fusion-diagnosis results, which include locations of faulty load cells, the types and the degrees of faults, the estimated outputs of faulty load cells in normal operating condition. Field tests show that the truck scale installed with the proposed diagnostic facilities discriminates load cells precisely. In the case of one faulty cell, its maximum weighing error is less than 0.7%, exhibiting a performance better than that of a 4th class scale under normal operating condition.