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

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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.