Fault diagnosis method and device and electronic equipment

Abstract

The invention provides a fault diagnosis method and device and electronic equipment. The method comprises the following steps: acquiring characteristic parameters of a fault; the feature parameters are input into a neural network model, a target fault type is output, the neural network model is obtained through training of multiple sets of data, and each set of data in the multiple sets of data comprises the feature parameters of the sample fault data and labels used for identifying the fault types corresponding to the feature parameters of the sample fault data; the target fault type is obtained from the fault tree set to serve as a target fault tree of a child node, the target fault tree comprises a plurality of leaf nodes, and the leaf nodes correspond to a plurality of fault causes; according to a preset traversal sequence, the child nodes of the target fault tree are diagnosed, and a diagnosis result is obtained; and determining a fault reason corresponding to the fault according to the diagnosis result. According to the method, the fault tree and the neural network are combined, rapid and accurate positioning of the fault source can be realized, and maintenance personnel can find the fault in time and take precautions against calamity.

Description

technical field [0001] The present invention relates to the field of fault diagnosis, in particular to a fault diagnosis method, its device and electronic equipment. Background technique [0002] With the development of the economy and the continuous expansion of the city scale, high-rise residences, hotels, hotels, office buildings, etc. are constantly increasing, so the number of building electromechanical equipment is also increasing. Among them, the total number of elevators in the country has reached 5.6 million. [0003] While the use of building electromechanical equipment brings convenience to people entering and exiting high-rise buildings, the problems of operation, maintenance and safety of building electromechanical equipment are becoming more and more prominent. However, the number of construction mechanical and electrical equipment technicians has not increased correspondingly. The domestic construction mechanical and electrical equipment industry is facing the...

AI Technical Summary

Problems solved by technology

At present, most of the maintenance personnel rely on their own experience and product manuals to deal with the failure of building mechanical and electrical equipment. Through manual operation and thinking about maintenance failures, the maintenance efficiency is heavily dependent on the technical ability and experience of the maintenance personnel.

As far as fault diagnosis of building electrical systems in the past is concerned, due to the limitations of traditional concepts and technical levels, most of them choose to use manual detection, which requires relatively high manpower, material and financial resources, and the accuracy of the detection results cannot be guaranteed.

Although the failure technology of building mechanical and electrical equipment is constantly being updated, overall, the stability of the system operation will still be affected by various factors and failures will occur, and the accuracy of the diagnosis results is relatively low, which can provide limited help for troubleshooting.

In addition, in the implementation of all fault diagnosis technologies, there are some algorithms that cannot meet the actual needs, and have a certain impact on the accuracy of diagnosis results.

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