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首页> 外文期刊>IEEE transactions on industrial informatics >Fault Prediction via Symptom Pattern Extraction Using the Discretized State Vectors of Multisensor Signals
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Fault Prediction via Symptom Pattern Extraction Using the Discretized State Vectors of Multisensor Signals

机译：使用多传感器信号的离散状态向量通过症状模式提取进行故障预测

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Fault prediction and early degradation detection have received considerable attention in many engineering disciplines. Fault symptoms can be identified by abnormal values or unusual trends in the monitored sensor signals over a certain period prior to fault occurrence. However, how to extract abnormal pattern, particularly those with conditional relations among multiple sensor signals, remains unclear. Pattern extraction is further difficult particularly when there is no gradient relationship between measurements and operational states due to highly scattered data and unclear boundaries for distinguishing operational states. Additionally, defining the time period for symptom periods is challenging. To resolve these issues, we define the terms symptom pattern and symptom period, and then present a symptom pattern extraction method that collects all evidence of potential fault occurrence from multiple sensor signals. We postulate that, given time markers of fault occurrences, a symptom period precedes the occurrence of a fault. Symptom patterns are defined as either only found in the symptom periods or not found in the given time series, but similar to fault patterns. We further discuss an iterative search procedure for determining the length of symptom periods and propose a severity assessment method for symptom patterns. Finally, we apply the symptom pattern extraction and severity assessment methods to an online fault prediction procedure. By assessing the total severity of patterns in the monitoring window, early warning decision can be made. The procedure is tested in the early detection of abnormal cylinder temperature in a marine diesel engine and automotive gasoline engine knocking.
机译：故障预测和早期退化检测已在许多工程学科中引起了广泛关注。故障症状可以通过在故障发生之前的一定时期内监视的传感器信号中的异常值或异常趋势来识别。然而，如何提取异常模式，尤其是在多个传感器信号之间具有条件关系的异常模式，仍然不清楚。特别是当由于高度分散的数据和用于区分工作状态的边界不清晰而导致测量值和工作状态之间没有梯度关系时，模式提取将变得更加困难。另外，定义症状时期的时间段也是具有挑战性的。为解决这些问题，我们定义了术语“症状模式”和“症状周期”，然后提出了一种“症状模式提取”方法，该方法从多个传感器信号中收集所有可能发生故障的证据。我们假定，给定故障发生的时间标记，在故障发生之前有一个症状时期。症状模式定义为仅在症状周期中找到，或在给定的时间序列中找不到，但类似于故障模式。我们进一步讨论了确定症状周期长度的迭代搜索程序，并提出了症状模式的严重性评估方法。最后，我们将症状模式提取和严重性评估方法应用于在线故障预测程序。通过评估监视窗口中模式的总严重性，可以做出预警决策。该程序在船用柴油机和汽车汽油机爆震的早期气缸温度异常检测中进行了测试。

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