A Method for Anomaly Detection and Fault Diagnosis of Elevator Door Machine

doi:10.12068/j.issn.1005-3026.2024.06.001

Abstract

Abstract:

A method for anomaly detection and fault diagnosis of elevator door machine operation is proposed. Firstly， the opening and closing door curve is spearated from the operation data of the elevator door machine and divided it into 10 operating segments， and the data characteristics of each section is extracted. Secondly， an anomaly detection method based on boxplot is proposed， and the accumulated data characteristics of each running section are used for anomaly diagnosis. In order to prevent the diagnostic error caused by the data not satisfying the normal distribution， the normality test method of the feature data of the door machine is added， and the Box-Cox transformation is performed on the data that does not meet the normal distribution. Finally， the segmented feature data of the elevator door is extracted， and the extreme learning machine（ELM） is used to train the classification model for three faults： door knife jamming fault， overall resistance increase fault and synchronous belt loosening fault. Experiments have verified that the proposed anomaly detection method and fault diagnosis method have high accuracy and value of application and promotion.

Key words: elevator, anomaly detection, boxplot, Box-Cox transformation, fault diagnosis, ELM（extrame learning machine）

CLC Number:

Yu-chuan FAN, Bo FAN, Zhuo CHEN, Xiao-shun ZHANG. A Method for Anomaly Detection and Fault Diagnosis of Elevator Door Machine[J]. Journal of Northeastern University(Natural Science), 2024, 45(6): 761-768.

Figures/Tables 13

Fig.1 Opening/closing pulse signal and opened/closed pulse signal

Fig.2 Sectional diagram of door opening and closing curve

Fig.3 Schematic diagram of boxplot anomaly detection principle

Fig.4 Flow chart of elevator door machine anomaly detection algorithm

Fig.5 Flow chart of the benchmark dataset generation method

Fig.6 Network structure diagram of ELM

Fig.7 Elevator door test bench

Fig.8 Test scheme of elevator door knife jamming fault

Fig.9 Test scheme for overall resistance increase of elevator door knife

Fig.10 Test scheme of elevator door machine synchronous belt looseness

Table 1 Data classification results

数据集	分类准确率
训练集	97.63
测试集	99.53

Fig.11 Test set diagnostic results

Fig.12 Effect of the number of hidden layer neurons on ELM performance

References 16

1	刘秀华，张兴法.电梯门系统主要事故的分析与预防［J］.中国特种设备安全，2006，22（1）：13-14.
	Liu Xiu‑hua， Zhang Xing‑fa.Analysis and prevention of main accidents in elevator door system［J］.China Special Equipment Safety，2006，22（1）：13-14.
2	Coste S D， Schienda G A.Elevator car door and motion sequence monitoring apparatus and method：US4750591［P］. 1988-06-14.
3	Pekka P， Riitta P J， Seppo S A， al et，Elevator door monitoring method that uses internal control signals of the door system to determine its need for maintenance W 0，200236476-A1［P］.2002-04-10.
4	乔久鹏.电梯故障检测及诊断系统研究［D］.哈尔滨：哈尔滨工业大学，2007.
	Qiao Jiu‑peng.Research on detection and diagnosis system for elevators fault［D］.Harbin：Harbin Institute of Technology，2007.
5	冷子文，卜永刚，朱文杰.神经网络在电梯故障诊断中的应用［J］.中国电梯，2019，30（19）：70-72.
	Leng Zi‑wen， Bu Yong‑gang， Zhu Wen‑jie.Application of the neural network to elevator fault diagnosis［J］.China Elevator，2019，30（19）：70-72.
6	宗群，陈东航，牙淑红.基于故障树分析法的电梯远程监控系统故障诊断［J］.制造业自动化，2003，25（11）：45-48.
	Zong Qun， Chen Dong‑hang， Shu‑hong Ya.Fault diagnosis of remote elevator monitor system based on fault tree analytical method［J］.Manufacturing Automation，2003，25（11）：45-48.
7	郑淑娟.电梯门系统状态监控和实验研究［D］.上海：上海交通大学，2009.
	Zheng Shu‑juan.State monitor and experimental research of elevator door system［D］.Shanghai：Shanghai Jiao Tong University，2009.
8	Luo M， Li C S， Zhang X Y，et al.Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings［J］.ISA Transactions，2016，65：556-566.
9	Isham M F， Leong M S， Lim M H，et al.Intelligent wind turbine gearbox diagnosis using VMDEA and ELM［J］.Wind Energy，2019，22（6）：813-833.
10	Mirzargar M， Whitaker R T， Kirby R M.Curve boxplot：generalization of boxplot for ensembles of curves［J］.IEEE Transactions on Visualization and Computer Graphics，2014，20（12）：2654-2663.
11	Fiorentini G， Sentana E， Calzolari G.On the validity of the Jarque‑Bera normality test in conditionally heteroskedastic dynamic regression models［J］.Economics Letters，2004，83（3）：307-312.
12	田亚茹.基于Box-Cox变换结合多种算法的风电机组数据预处理与故障预警的研究［D］.保定：河北大学，2022.
	Tian Ya‑ru.Research on data preprocessing and fault warning of wind turbine based on Box-Cox transform and multiple algorithms［D］.Baoding：Hebei University，2022.
13	Osborne J W.Improving your data transformations：applying the Box-Cox transformation［J］.Practical Assessment，Research and Evaluation，2010，15（12）：1-9.
14	Huang G B， Zhu Q Y， Siew C K.Extreme learning machine：theory and applications［J］.Neurocomputing，2006，70（1/2/3）：489-501.
15	Huang G B， Wang D H， Lan Y.Extreme learning machines：a survey［J］.International Journal of Machine Learning and Cybernetics，2011，2（2）：107-122.
16	Huang G B， Zhou H M， Ding X J，et al.Extreme learning machine for regression and multiclass classification［J］.IEEE Transactions on Systems，Man，and Cybernetics Part B，Cybernetics：a Publication of the IEEE Systems，Man，and Cybernetics Society，2012，42（2）：513-529.

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