Elevator fault detection method based on gating circulation networks and canonical correlation analysis

A typical correlation analysis and fault detection technology, applied in the field of elevator safety detection, can solve the problems of large demand for fault data sets and difficulty in obtaining fault data, and achieve the effect of improving reliability

Active Publication Date: 2021-07-09
CENT SOUTH UNIV
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Problems solved by technology

Moreover, other existing neural network-based elevator fault detection methods have a relatively large demand for fault data sets in order to train a better network, but it is difficult to obtain a large amount of fault data in practice.

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  • Elevator fault detection method based on gating circulation networks and canonical correlation analysis
  • Elevator fault detection method based on gating circulation networks and canonical correlation analysis
  • Elevator fault detection method based on gating circulation networks and canonical correlation analysis

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Embodiment Construction

[0030] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0031]The invention provides a vertical elevator fault detection method based on the gated cyclic unit neural network and typical correlation analysis, and detects four current data and vibration data in three directions of the elevator running motor current, brake current, safety circuit current and car door motor current ; After preprocessing the offline data, input two gated recurrent unit neural networks for training at the same time, so that the correlation coefficient obtained after the output of the two gated recurrent unit neural networks is the largest after the canonical correlation analysis; the online data is preprocessed Finally, it is input into the two trained networks, and the correlation coefficient is compared with the threshold to realize fault detection.

[0032] The overall algorithm principle flow process...

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Abstract

The invention discloses an elevator fault detection method based on gating circulation networks and canonical correlation analysis. The method is a vertical elevator fault detection method based on gating circulation unit neural networks and canonical correlation analysis, and four kinds of current data of motor current, brake current, safety circuit current and car door motor current and vibration data in three directions of elevator operation are detected; off-line data are pre-processed, input into the two gating circulation unit neural networks respectively, and trained at the same time, so that the correlation coefficient obtained after canonical correlation analysis is carried out on the output of the two gating circulation unit neural networks is maximum; and online data are pre-processed and input into the two trained networks, and the correlation coefficient is compared with a threshold value to realize fault detection.

Description

technical field [0001] The invention relates to the technical field of elevator safety detection, in particular to an elevator fault detection method based on a gated loop network and canonical correlation analysis. Background technique [0002] With the rapid development of elevator technology, elevators have been installed in large numbers in buildings, factories, freight terminals and other places. As an important tool for transporting passengers and goods, its reliability is crucial to human safety and economic development. How to monitor the running state of the elevator and ensure the safety and reliability of the elevator is of great significance. The elevator is a system that is closely integrated with electromechanical. It is bound to be accompanied by mechanical vibration during operation, and the vibration data contains a wealth of elevator operating status information. By detecting the elevator vibration data, the elevator's operating status can be well monitore...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B66B5/00
CPCB66B5/0006
Inventor 樊欣宇贾丽君李晨阳王美蓉李世情许凡凡
Owner CENT SOUTH UNIV
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