Elevator fault pre-warning method based on bidirectional gating circulating neural network

A cyclic neural network, two-way door technology, used in elevators, transportation and packaging, etc., can solve problems such as the inability to reflect the health status of elevators in real time, and the inability to predict failures in advance, so as to reduce adverse effects, compress rescue time, and improve rescue success rate. Effect

Active Publication Date: 2018-09-25
XIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide an elevator fault prediction method based on a two-way gated cyclic neural network, which solves the problem that the prior art cannot predict the occurrence of faults in advance, and can only be rescued passively. Questions that reflect the health of the elevator

Method used

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Experimental program
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Embodiment

[0089] 1. Parameter setting and application of bidirectional gated recurrent neural network prediction model.

[0090] The elevator waveform sequence is used as the data set, and each cycle of the waveform sequence contains 256 sampling points. Here, 100,000 sequences are used as the overall samples, 70% of which are used as training samples and 30% are used as test samples. Using 7 layers of neural network layers, the Relu function is used as the activation function between neurons in each layer, the input layer is a fully connected layer, the hidden layer is four layers of bidirectional gated recurrent neural network layers and a fully connected layer , the output layer is a softmax layer, and the final classification is extracted from the softmax layer. The input sequence shape is [70000,256,1], that is, 70,000 samples, 256 steps correspond to 256 sampling points, and the input data dimension is 1. The output sequence sample is [70000,256,1], that is, the output data is co...

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Abstract

The invention discloses an elevator fault pre-warning method based on a bidirectional gating circulating neural network. The elevator fault pre-warning method comprises the following steps: (1) sampling elevator vibration waveform data; (2) converting elevator vibration waveform samples into a sequence form; (3) classifying serialized samples into a training set and a testing set; (4) constructinga bidirectional gating circulating neural network framework; (5) training the bidirectional gating circulating neural network framework; (6) carrying out prediction testing by virtue of the testing set, so as to obtain a bidirectional gating circulating neural network prediction model; and (7) carrying out elevator fault prediction classification by virtue of the bidirectional gating circulatingneural network prediction model, so as to obtain a predicted elevator waveform result. The invention further discloses an elevator fault diagnosis method based on the bidirectional gating circulatingneural network. When a judgment result shows that an elevator is in a fault state, an alarm is given out. The two methods disclosed by the invention are extremely high in accuracy rate and instantaneity on the diagnosis and prediction of elevator conditions.

Description

technical field [0001] The invention belongs to the technical field of elevator fault detection and early warning, relates to an elevator fault prediction method based on a bidirectional gating cyclic neural network, and also relates to an elevator fault diagnosis method based on a bidirectional gating cyclic neural network. Background technique [0002] In recent years, with the continuous development of social economy, urban high-rise buildings have increased rapidly, elevators have been widely used, and the number of elevators has continued to increase. In the future, the elevator update market and after-sales service market will have huge space. However, in recent years, elevator failures have occurred frequently. On the one hand, the high incidence and severity of accidents often cause considerable casualties and large economic losses; Conditions and maintenance records are kept unevenly, making it difficult to verify a lot of real data, and burying a lot of safety haza...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B66B5/00B66B1/06B66B5/02B66B3/00
CPCB66B1/06B66B3/00B66B5/0018B66B5/027
Inventor 邓亚平王璐徐敬一贾颢刘岚李琳
Owner XIAN UNIV OF TECH
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