Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA’s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety.
The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures.
The method developed in
this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).
This work was done by Farrokh Vatan of Caltech for NASA’s Jet Propulsion Laboratory.
The software used in this innovation is available for commercial licensing. Please contact Daniel Broderick of the California Institute of Technology at
This Brief includes a Technical Support Package (TSP).
Model-Based Method for Sensor Validation
(reference NPO-47574) is currently available for download from the TSP library.
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Overview
The document is a Technical Support Package from NASA's Jet Propulsion Laboratory (JPL) detailing a Model-Based Method for Sensor Validation, identified as NPO-47574. It emphasizes the importance of sensor validation in aerospace systems, where accurate data is critical for operational safety and efficiency. The research was conducted under a contract with NASA and acknowledges the support of the California Institute of Technology.
At the core of the methodology is the concept of Analytical Redundant Relations (ARRs), which are used to assess the reliability of sensor data. The document outlines a System Model (SM) that consists of two main components: the Behavioral Model (BM) and the Observation Model (OM). The BM provides a component-based description of the system, detailing the functions of each component through Primary Relations (PRs) and their interconnections. The OM defines the relationships that govern the observations made by the sensors and the models associated with them.
An illustrative example is provided, where a discrepancy in the reading of a specific sensor (S5) prompts an investigation into whether the sensor itself is faulty or if there is an actual fault in the system. The methodology involves analyzing the domain of influence of the sensor and comparing it against the ARRs that do not depend on the sensor in question. If the ARRs are satisfied, it indicates that the sensor is likely faulty; otherwise, the sensor may be functioning correctly, and the fault lies within the system components it monitors.
The document also references various studies and papers that contribute to the theoretical foundation of the proposed method, highlighting its relevance in both artificial intelligence and automatic control perspectives. The research aims to enhance the reliability of sensor data, which is crucial for the successful operation of complex aerospace systems.
Overall, this Technical Support Package serves as a resource for understanding the innovative approaches to sensor validation, showcasing JPL's commitment to advancing aerospace technology and its applications. For further inquiries or assistance, the document provides contact information for the Innovative Technology Assets Management at JPL.
