电梯门机异常检测和故障定位方法

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

提出一种电梯门机运行异常检测和故障定位方法.首先,从电梯门机的运行数据中分离出开关门曲线并将其划分为10个运行段,提取各段的数据特征.其次,提出基于箱线图的异常检测方法并利用各运行段的累积数据特征进行异常诊断;为防止数据不满足正态分布而造成的诊断误差,加入了门机特征数据的正态性检验方法,并对不满足正态分布的数据进行Box-Cox变换,使其满足正态分布.最后,提取电梯门机分段特征数据,采用极限学习机(ELM)对门刀卡阻故障、整体阻力增大故障和同步带松脱故障3种故障进行分类模型训练.经实验验证,提出的异常检测方法和故障定位方法准确度高,具有较高的应用推广价值.

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.