Safety evaluation method of hoisting machinery based on neural network

Hoisting machinery as a material handling equipment, widely used in the national economy departments, in the national “safe, efficient, green and harmonious” under the application requirements, to improve the intrinsically safe hoisting machinery, a complex system, in this paper, the affecting the safe operation of the hoisting machinery hazards, summary and analysis based on the intrinsic safety theory and correlation analysis method, on the nature of the hoisting machinery safety assessment model is established. The theory of information entropy and fuzzy mathematics, the safety evaluation method of hoisting machinery based on neural network is studied. Through summarizing the hazard factor of hoisting machinery, lifting machinery design, manufacture, installation, alteration, use, and management and so on, this paper analyzes advantages and disadvantages of commonly used safety assessment or prediction method, based on the “human–environment” of safety evaluation of ideas, will influence of lifting machinery into the ontology equipment hazards, organizational security hazards, essence of safety culture and emergency fault handling of hazards. In the paper, two neural networks are used to predict the failure rate, and the accuracy of the two methods is compared. Firstly, BP neural network is optimized by genetic algorithm for prediction. BP neural network optimized by genetic algorithm is the most widely used neural network for prediction. Secondly, Elman neural network is used for prediction. Two neural networks are used to predict the failure rate, study the structural weight of neural network, obtain the prediction result graph and prediction error graph of neural network, and analyze the results, so as to judge the availability of using neural network method to predict the failure rate.