Research on Fault Diagnosis of Hoist Dangerous Load Based on DBN

Abstract

At present, hoisting accidents caused by dangerous loads such as overloading, incomplete unloading, conveyance jamming, and rope loosening still occur. Therefore, this paper proposes a fault diagnosis method for dangerous loads of hoists based on the Deep Belief Network (DBN). With the stator current of the hoisting motor as the signal source, the 31-dimensional eigenvalues of the stator current of the hoisting motor in the time, frequency, and time-frequency domains are taken as the input parameters of the deep belief network. The restricted Boltzmann machine is used for unsupervised pre-training through layer-by-layer transmission and layer-by-layer training. The BP neural network, as the top-level classifier, is used for supervised fine-tuning training to finally complete the whole training process of the depth belief network. Relying on the advantages of the depth belief network, which has fast training speed and strong feature extraction ability. It makes up for the deficiency of the traditional algorithm in feature extraction and classification separation, resulting in the loss of details, and finally realizes the overload Classification of four types of dangerous loads, i.e., unloaded, blocked, and slackened. The results show that the method based on DBN has higher identification and better diagnosis effect.