从传感器时序数据中自动识别工单编号，解决工业流程挖掘数据难题
Case ID detection based on time series data -- the mining use case
- 基于变量短期均值突变检测工单边界，规则驱动无需大量标注
- 在矿业数据上达96.8%准确率，含异常值场景仍保持97%高精度
- 适用于工业流程挖掘，尤其适合无明确工单标识的传感器数据
流程挖掘在工业分析中日益流行，但需事件日志格式，包含工单标识（case ID）、活动名称与时间戳。工业数据常以低层级传感器读数的时序形式提供，无法直接用于流程挖掘，因缺乏显式活动标记且常缺工单编号。本文提出一种基于规则的算法，通过检测特定变量短期均值显著变化来识别工单边界。以矿业场景为例验证，将算法结果与专家标注对比，实验显示在有无异常值的数据集上分别达到96.8%和97%的F1分数。另在制造领域数据集上测试，F1得分达92.6%。
原文摘要 · Abstract (English)
Process mining gains increasing popularity in business process analysis, also in heavy industry. It requires a specific data format called an event log, with the basic structure including a case identifier (case ID), activity (event) name, and timestamp. In the case of industrial processes, data is very often provided by a monitoring system as time series of low level sensor readings. This data cannot be directly used for process mining since there is no explicit marking of activities in the event log, and sometimes, case ID is not provided. We propose a novel rule-based algorithm for identification patterns, based on the identification of significant changes in short-term mean values of selected variable to detect case ID. We present our solution on the mining use case. We compare computed results (identified patterns) with expert labels of the same dataset. Experiments show that the developed algorithm in the most of the cases correctly detects IDs in datasets with and without outliers reaching F1 score values: 96.8% and 97% respectively. We also evaluate our algorithm on dataset from manufacturing domain reaching value 92.6% for F1 score.
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