Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

中文翻译：

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利用机器学习对传感器故障进行上下文感知的故障诊断方案

传感器作为网络物理系统的重要组成部分而存在，使其容易因复杂的环境，低质量的生产和老化而发生故障。出现故障时，传感器会停止通讯或传达不正确的信息。这些不稳定的情况威胁着系统的安全性，经济性和可靠性。这项研究的目的是在有限的能源，内存和无线传感器网络（WSN）的计算范围内构建基于轻量级机器学习的故障检测和诊断系统。本文提出了一种基于上下文学习的故障诊断（CAFD）方案，该方案基于称为Extra-Trees的集成学习算法。为了评估所提议方案的性能，一个由湿度和温度传感器观测值组成的实际WSN场景，被复制为极端低强度的断层。考虑了六种常见的传感器故障类型：漂移，硬化/偏置，尖峰，不稳定/精度下降，卡住和数据丢失。提出的CAFD方案揭示了及时准确地检测和诊断低强度传感器故障的能力。此外，通过与最先进的机器学习算法（支持向量机和神经网络）进行比较，证明了Extra-Trees算法在诊断准确性，F1-得分，ROC-AUC和训练时间方面的效率。提出的CAFD方案揭示了及时准确地检测和诊断低强度传感器故障的能力。此外，通过与最先进的机器学习算法（支持向量机和神经网络）进行比较，证明了Extra-Trees算法在诊断准确性，F1-得分，ROC-AUC和训练时间方面的效率。提出的CAFD方案揭示了及时准确地检测和诊断低强度传感器故障的能力。此外，通过与最先进的机器学习算法（支持向量机和神经网络）进行比较，证明了Extra-Trees算法在诊断准确性，F1-得分，ROC-AUC和训练时间方面的效率。