电磁阻拦装置记录的事件数据具有高采样率、多维耦合和非周期瞬态的特点,属于复杂非平稳时间序列.为此提出一种基于级联下界剪枝和全局约束下动态时间规整(dynamic time warping,DTW)提前终止策略的相似性搜索方法对事件数据进行挖掘,以实现快速故障诊断.首先提出一种基于小波熵和重要点筛选的改进分段聚合近似算法对事件数据进行压缩降维;然后依次采用3种级联的DTW下界距离函数进行剪枝,以剔除不相似序列,得到候选序列;最后在候选序列中执行精确搜索,通过提出的全局约束下提前终止策略优化DTW度量速度.在电磁阻拦装置原型的历史实验数据中开展实验,验证了该方法能够准确匹配工况及系统故障类别,效率相比主流的TS2BC算法提高了 2.14倍.
Fault Diagnosis Method of Electromagnetic Arresting Gear Based on Time Series Similarity Search
The event data recorded by electromagnetic arresting gear exhibits high sampling rate,multi-dimensional coupling,and aperiodic transient,making it a complex non-stationary time series.In this paper,a similarity search method based on cascading lower bound pruning and global constraint early-abandoned strategy of DTW(dynamic time warping)is proposed to mine the event data for efficient fault diagnosis.Firstly,an improved piecewise aggregation approximation algorithm based on wavelet entropy and important point filtering is proposed to compress and reduce the dimension of event data.Secondly,three cascaded DTW lower bound distance functions are sequentially employed for pruning,elimi-nating dissimilar sequences and generating candidate sequences.Finally,a precise search is performed within the candidate sequence,optimizing DTW measurement speed using the proposed global constraint-based early-abandoned strategy.Experiments were carried out in the historical test data of the prototype of the electromagnetic arresting gear,which verified that the method proposed in this paper can match the working conditions and determine the system fault category accurately,and the efficiency is improved by 2.14 times compared with the mainstream TS2BC algorithm.
electromagnetic arresting gearnon-stationary time seriessimilarity searchwavelet entropycascading lower boundDTW
万方数据
维普
