Distinguishing Sensor Faults from System Faults, and optimal placement of redundant sensors.

自动故障检测和诊断(FDD)系统依赖于传感器读数的可靠性。传感器在特定操作条件下的读数不一致可能不一定是传感器本身的故障，而是被监控系统出现更严重故障的症状，反之亦然。因此，系统和传感器故障可能会出现相同的症状。准确识别故障来源的能力在系统监控中至关重要，因为根据故障是来自传感器还是来自系统，需要采取不同的纠正措施。在传感器和系统各自的故障检测和诊断方面有大量的文献。尽管诊断方案的实际应用很重要，但在监测和诊断文献中，区分传感器和系统故障似乎没有得到实质性的重视。目前大多数检测故障的研究和方法都做出了以下隐含的假设之一：-传感器是完全起作用的，因此故障可归因于系统。-系统功能齐全，故障可归因于传感器。在不了解系统特性的情况下，系统模型中传感器读数的差异可能被错误地解释为监控传感器中的潜在故障。例如，如果在不了解系统的情况下使用极限检查来验证传感器测量，则诊断可能被错误地视为传感器故障。相反，一个简单的传感器故障可能被诊断为系统故障并触发不必要的纠正操作。使用多个冗余传感器是改善这种情况的一种方法，它可以方便地区分传感器故障和系统故障。然而，成本、物理和实用方面的限制限制了冗余传感器的大量放置。虽然成本并不总是一个问题，但传感器的放置仍然应该是明智的，并基于科学的原则，以避免复杂性。为了解决这个问题，通过确定传感器的最小冗余度，我们使用了被监测变量之间物理关系的先验知识来验证现有传感器观测的可信度。随后，我们为变量可被建模为串联因果网络的系统开发了一种冗余传感器布局方法。其推演的推广表明，如果传感器的数目(基本的和冗余的)大于被监测变量的1.5倍，则可以确定地完成区分传感器和系统故障的任务。研究的目的是通过形式化的方法证明所提出的方法，并将其推广到具有多输入多输出的一般网络(系统框图)，而不限制互连的形式。

Automated Fault Detection and Diagnosis (FDD) systems depend on the reliability of sensor readings. An inconsistency in a sensor's readings under specific operating conditions may not necessarily be a fault in the sensor itself, but a symptom of a more serious fault in the monitored system, and vice versa. Hence, system and sensor faults might manifest themselves with the same symptoms. The ability to identify the exact source of faults is crucial in the monitoring of a system because different corrective actions are required depending on whether the fault is from a sensor or from the system. There is an abundance of literature on fault detection and diagnosis for both sensors and systems individually. Despite the importance of the practical application of diagnostic schemes, distinguishing between sensor and system faults does not appear to have received substantial attention in the monitoring and diagnosis literature. Most current studies and methodologies of detecting faults make one of the following implicit assumptions: - Sensors are fully functional, so faults are attributable to the system. - The system is fully functional, and faults are attributable to the sensors.Without knowledge of the system characteristics, the discrepancy of sensor readings from the system model may erroneously be interpreted as potential faults in the monitoring sensors. For instance, if limit checking is used to validate the sensor measurement without some knowledge of the system, the diagnosis might wrongly be taken as a sensor fault. Conversely, a simple sensor fault might be diagnosed as a system fault and trigger unnecessary corrective actions. Using multiple redundant sensors is one way of improving the situation, and it can facilitate distinguishing sensor faults from the system faults. However, cost, physical, and practical constraints limit generous placement of redundant sensors. Although cost is not always an issue, the sensor placement should still be judicious and based on scientific principles to avoid complexity. To address this issue, by aiming to identify the minimum degree of sensor redundancy, we have used a priori knowledge of physical relationships between the monitored variables to verify the credibility of the existing sensor observations. Subsequently, we have developed a redundant sensor placement methodology for systems whose variables can be modeled as a serially connected causal network. The generalization of which by deduction has revealed that if the number of sensors (essential and redundant) is greater than 1.5 times the number of monitored variables the task of distinguishing between sensor and system faults can be accomplished with certainty. The proposed research aims to prove the developed method through formal means and extend it for general networks (system block diagrams) with multiple-input and multiple-output without any restriction on the form of interconnections.