Abstract
The article is devoted to the problem of using artificial neural networks to assess the risk of developing emergencies during the operation of lifting crane equipment. The data sources are telemetric measurements from microcontroller load limiters, as well as data from technical and daily inspections of equipment condition, in the last case the data may be fuzzy.
Highlights
The purpose of analysis of the telemetric data received on equipment and similar devices of a lifting crane, as well as indicators of devices from the local security system of the facility, is to develop and implement the technology for assessing any risk of emergencies at the current moment and predicting the risk for the near future
Given a weak formalism of the input information, as well as a dynamic discrete nature of information flow from safety equipment, and based on the examples of the best known practices, the best performance can be achieved with the application of machine learning techniques, neural networks, processing and clustering big data combined with fuzzy data analysis methods for decision making
The process of artificial neural networks studying for solving the general problem on predicting the risk of emergencies for lifting crane devices has the following stages of implementation: Structuring and analysis of the input information; Assessment of mechanisms for transforming the input information into target indicators for solving the problem of predicting the risk of emergencies; Determining the type and structure of the artificial neural network (ANN) elements; Learning neural network on the reference data set; Neural network performance check
Summary
The purpose of analysis of the telemetric data received on equipment and similar devices of a lifting crane, as well as indicators of devices from the local security system of the facility, is to develop and implement the technology for assessing any risk of emergencies at the current moment and predicting the risk for the near future. The process of artificial neural networks studying for solving the general problem on predicting the risk of emergencies for lifting crane devices has the following stages of implementation: Structuring and analysis of the input information; Assessment of mechanisms for transforming the input information into target indicators for solving the problem of predicting the risk of emergencies; Determining the type and structure of the artificial neural network (ANN) elements; Learning neural network on the reference data set; Neural network performance check.
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