Elevator equipment fault prediction method based on depth learning

A deep learning, equipment failure technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as consuming large analysis time

Active Publication Date: 2018-12-18
TAIYUAN UNIV OF TECH
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Problems solved by technology

Most of the current elevator fault diagnosis and detection methods combine the structure and principle of the elevator, and propose detection methods for the mechanical system, electrical control system and safety protection system of the elevator, but this requires a lot of analysis time

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  • Elevator equipment fault prediction method based on depth learning
  • Elevator equipment fault prediction method based on depth learning
  • Elevator equipment fault prediction method based on depth learning

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Embodiment Construction

[0081] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0082] Such as figure 1 As shown, the elevator equipment fault prediction method based on deep learning of the present invention comprises the following steps:

[0083] Step 1. Establish a real-time elevator fault information database. According to the four principles of fault information correlation, complete information, non-repetitive information, and non-human error in fault information, effective elevator fault information is selected as the preprocessing information sequence of the network. Elevator fault information includes fault Record information and elevator basic information, where the fault record information includes: elevator fault type, fault cause and fault time, and elevator basic information includes: elevator production date, elevator location, elevator model and elevator life.

[0084] Step 2, construct time series, comprise two kinds of...

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Abstract

The invention relates to an elevator equipment fault prediction method based on depth learning, belonging to the technical field of elevator fault prediction. The technical problem to be solved is toprovide a timely and accurate method to predict the type and time of elevator failure. In order to solve the technical problems, the concrete steps of the invention are summarized as follows: firstly,the elevator fault record information is collected and a real-time elevator fault information database is established; then the elevator fault information is processed into event sequence and time sequence; the event sequence and time sequence are used as the input data of double LSTM respectively, and the output embedding of the two sequences is obtained by iterative training of the loop neuralnetwork; then, the background knowledge and the nonlinear representation of the historical influence of the intensity function are obtained through joint player combined with two output embedding; finally, according to the characterization results of the strength function, the fault type and time of elevator are predicted. The invention can assist elevator maintenance personnel to take relevant preventive measures as early as possible to avoid occurrence of failure events.

Description

technical field [0001] The invention discloses a method for predicting faults of elevator equipment based on deep learning, and belongs to the technical field of fault prediction of elevators. Background technique [0002] With the continuous increase of high-rise buildings, the quality of elevators has attracted people's attention. Elevators that fail to stop, run poorly or even have accidents have affected people's daily life. To reduce the failure rate of elevators, the method of timely and accurate detection and troubleshooting needs further research. The current elevator fault diagnosis and detection methods mostly combine the elevator structure and principle, and propose detection methods for the elevator's mechanical system, electrical control system and safety protection system, but this requires a lot of analysis time. Contents of the invention [0003] The present invention overcomes the deficiencies existing in the prior art, and the technical problem to be so...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/04
CPCG06N3/049G06F2216/03G06N3/044
Inventor 王莉江海洋
Owner TAIYUAN UNIV OF TECH
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