基于大数据的电梯故障诊断与预测研究

Research of big-data-based elevator fault diagnosis and predication

为了研究大数据分析方法在电梯故障诊断与预测中的可行性,首先收集了海量的电梯检验数据,并对其中有关电梯轿厢振动的数据进行了特征参数的提取;然后构建了采用大数据分析方法诊断与预测电梯故障隐患的总体方案,通过监督学习与非监督学习的数据挖掘手段,对所提取到的电梯轿厢振动特征参数进行了充分的数据挖掘与分析,找出了电梯机械系统各种故障隐患与电梯运行时轿厢振动监测信号之间的内在关系,最终根据电梯轿厢振动特征量的大小及其变化趋势,诊断和预测电梯机械系统的各种故障隐患。研究结果表明:大数据分析方法可准确地对电梯机械系统故障进行诊断与预测。

Aiming at applying big data analysis methods into elevator fault diagnosis and prediction,first,a large number of elevator inspection data was collected,and feature parameters related to car vibration were extracted from the inspection data. Then,the overall scheme was presented. By using supervised and unsupervised learning methods,data mining and analysis were conducted on the extracted feature parameters to find out the intrinsic relationship between the faults of elevator mechanical system and the car vibration signal. Finally,the elevator mechanical system faults could be diagnosed and predicted according to the magnitude of vibration characteristics and their changing trends.The results indicate that big data analysis method can be used to accurately diagnose and predict the failure of elevator mechanical system.