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Sensor fault management techniques for wireless smart sensor networks in structural health monitoring

机译：结构健康监测中无线智能传感器网络的传感器故障管理技术

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摘要

Faulty sensor data often occur in structural health monitoring (SHM) systems using wireless smart sensor networks (WSSN). Serious sensor faults in the raw data may negatively affect SHM analysis and subsequent informed decisions. This issue is more critical for WSSN running decentralized data acquisition, because raw data are not transmitted back, and hence, data quality assessment can be extremely difficult for users. Therefore, developing efficient techniques to autonomously detect, identify, and recover sensor faults is essential. In the vibration data collected from the Jindo Bridge, some data sets are corrupted with sensor faults, which can be categorized as one of three types: drift, spikes, and bias. Accordingly, this paper presents a three-stage strategy to address this problem in WSSN. First, a distributed similarity test is employed to detect sensor faults; this test is based on the similarity of the power spectral density of the data among sensors within a cluster of nodes. Second, an artificial neural network model is trained to identify the types of sensor faults. Third, sensor data are recovered from the identified faults by applying a correction function or replacing faulty data with estimated values. Subsequently, numerical analysis is performed using a set of field measurements collected from the Jindo Bridge. Decentralized system identification is conducted using the original data and data recovered using the proposed strategy. The three-stage strategy is shown to successfully detect, identify, and recover sensor faults, which improves the results of system identification. Finally, the benefits and limitations of this strategy are discussed.
机译：使用无线智能传感器网络（WSSN）的结构健康监测（SHM）系统中经常出现错误的传感器数据。原始数据中的严重传感器故障可能会对SHM分析和后续的明智决策产生负面影响。对于原始数据采集不回传的WSSN，此问题对于WSSN运行更为关键，因为原始数据不会传回，因此，对于用户而言，数据质量评估可能非常困难。因此，开发有效的技术来自主检测，识别和恢复传感器故障至关重要。在从Jindo桥收集的振动数据中，某些数据集因传感器故障而损坏，可以将其分类为三种类型之一：漂移，尖峰和偏置。因此，本文提出了一个三阶段策略来解决WSSN中的这一问题。首先，采用分布式相似性测试来检测传感器故障。该测试基于节点群集中传感器之间数据的功率谱密度的相似性。其次，训练人工神经网络模型以识别传感器故障的类型。第三，通过应用校正功能或将故障数据替换为估计值，从已识别的故障中恢复传感器数据。随后，使用从Jindo桥收集的一组现场测量值进行数值分析。使用原始数据进行分散式系统识别，并使用建议的策略进行数据恢复。所示的三阶段策略可成功检测，识别和恢复传感器故障，从而改善了系统识别的结果。最后，讨论了该策略的好处和局限性。

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