An elevator fault prediction method for optimizing an Elman neural network based on an improved myxomycete algorithm comprises the steps that a sensor is used for collecting operation data of an elevator, the data are uploaded to a background through a WIFI module for data processing, the background optimizes an Elman neural network model through the improved myxomycete algorithm, the myxomycete algorithm is optimized through sine-tent chaotic mapping and added into a greedy step, and the Elman neural network model is optimized through the improved myxomycete algorithm. Outputting an optimization result with higher precision to the Elman neural network model so as to optimize a weight and a threshold value of the Elman model, and finally outputting a prediction result; after the operation data of the elevator are preprocessed and input into the optimized Elman model, faults possibly occurring during operation of the elevator in a period of time in the future can be predicted; according to the method, improvement is carried out through sine-tent chaotic mapping and greedy addition, and compared with an original algorithm, the improved colistia algorithm has better optimization searching capability, so that a better prediction result is finally output. According to the method, faults can be predicted in advance, elevator maintenance personnel can maintain the elevator more conveniently, and accidents are reduced.

    一种基于改进的黏菌算法优化Elman神经网络的电梯故障预测方法,利用传感器采集电梯的运行数据,通过WIFI模块将数据上传给后台进行数据处理,后台采用改进的黏菌算法优化Elman神经网络模型,黏菌算法经过sine‑tent混沌映射优化和加入贪心步骤优化,将具有更高精度的寻优结果输出到Elman神经网络模型,从而优化Elman模型的权值和阈值,最后输出预测结果;所述电梯的运行数据经过预处理输入优化的Elman模型后,可以对未来一段时间电梯运行时可能发生的故障进行预测;经过sine‑tent混沌映射和加入贪心步骤进行改进,改进之后的黏菌算法相比于原算法具有更好的寻优能力,进而使最后输出更佳的预测结果。本发明能提前预测故障,使电梯维护人员更方便的对电梯进行维护,减少事故的发生。


    Access

    Download


    Export, share and cite



    Title :

    Elevator fault prediction method for optimizing Elman neural network based on improved myxomycete algorithm


    Additional title:

    基于改进的黏菌算法优化Elman神经网络的电梯故障预测方法


    Contributors:
    FAN YUANYUAN (author) / MENG DIFEI (author) / SANG YINGJUN (author) / YANG YAN (author) / ZHANG MING (author) / ZHANG TAO (author) / LU QING (author) / WANG YEQIN (author)

    Publication date :

    2023-03-07


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / B66B Aufzüge , ELEVATORS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



    Fault Detection for Satellite Formation Flight Using Improved Elman Neural Network

    Wang, Jiayi / Chen, Hua / Cui, Jia et al. | TIBKAT | 2022


    Fault Detection for Satellite Formation Flight Using Improved Elman Neural Network

    Wang, Jiayi / Chen, Hua / Cui, Jia et al. | Springer Verlag | 2021


    Short-term traffic flow prediction method based on GWO-Elman neural network

    LIU YUNXIANG / WANG JUN / YUAN XINXIN | European Patent Office | 2023

    Free access

    Prediction of Urban Rail Transit Sectional Passenger Flow Based on Elman Neural Network

    Li, Q. / Qin, Y. / Wang, Z.Y. et al. | British Library Conference Proceedings | 2014


    In-use elevator fault prediction method based on neural network

    LIU QINGFU / CAO ZHIFENG / WANG XIUMIN et al. | European Patent Office | 2021

    Free access