Costs and Benefits of Model-based Diagnosis

Abstract

Over the past 20 years, there has been much work in the area of model-based diagnosis (MBD). By this we mean diagnosis systems arising from Computer Science or Artificial Intelligence approaches where a generic software engine is developed to address a large class of diagnosis problems [1], [2]. Later, models are created to apply the engine to a specific problem. These techniques are very attractive, suggesting a vision of machines that repair themselves, reduced costs for all kinds of endeavors, spacecraft that continue their missions even when failing, and so on. This promise inspired a broad range of activity, including our involvement over several years in flying the Livingstone and Livingstone 2 on-board model-based diagnosis and recovery systems as experiments on two spacecraft [3], [4], [5], [6], [7].While a great deal was learned through a variety of applications to simulators, testbeds and flight experiments, no project adopted the technology in operations and the expected benefits have not yet come to fruition. This led us to ask what are the costs of using MBD for the operational scenarios we encountered, what are the benefits, and how do we approach the question of whether the benefits outweigh the costs? How are missions today approaching fault diagnosis and recovery during operations? If we characterize the cost and benefits of using MBD, how would it compare with traditional ways of making a system more robust? How did expectations for MBD compare to benefits seen in the field and why?The literature does provide existing cost models for related endeavors such as integrated vehicle health management [8], [9], [10]. It also provides excellent narratives of why projects chose not to use MBD after considering it [11]. However, we believe that this paper is the first to unpack and discuss the cost, benefit and risk factors that impact the net value of model-based diagnosis and recovery. We use experience with systems such as Livingstone as an example, so our focus is on-board model-based diagnosis and recovery, but we believe many of the insights and remaining questions on the costs and benefits are applicable to other diagnosis applications.While the analysis is not yet mature enough to provide a 1-4244-1488-quantitative model of when on-board model-based diagnosis would be an effective choice, it lays out the cost/benefit proposition and identifies several disconnects that we believe prevent adoption as an operational tool. While we do not suggest metrics for every cost, benefit and risk factor we identify, we do discuss where each factor arises in development or operations and how model-based diagnosis and recovery tends to leverage or exacerbate each. As such we believe the analysis is of use to those developing MBD or related techniques and those who may employ them. It also serves as one example of how honest expectations based on technical capability can come to differ from the net impact on customer problems.In this paper we present a cost/benefit analysis for MBD, using expectations and experie...

Verification and Validation of Hierarchical Fault Diagnosis in Satellites Formation Flight

“…As explained previously, h p, 1 1,1 has the form in Proposition 3.1 and κ 1 is a real number. Consequently, P (X l p = x 0 |X l−1 1,...,N = f ) in (19) can be expressed as follows:…”

“…Therefore, the effects of δ are to be considered as negligible in (14). Furthermore, since the hierarchical agreement factor h p, 1 1,1 in (14) is a ratio of two accuracies (as described in Section II-B), and the decreased accuracy at level l−1 satisfies Proposition 3.1, the probability P (X l p = x 1 |X l−1 1 = f 1 ) can be expressed as follows:…”

“…being in its state X l−1 1 = f 1 given an identification f 1 by the diagnosis algorithm deployed at C l−1 1 . In terms of the changed confusion matrix elements in Table I, the parameter of our interest is θ = (c 1,…”

“…Since this is a case of disagreement between the two levels, the belief adjustment factor (1 − h p, 1 1,1 ) is used in the calculations. Furthermore, since the state X l p = x 0 is dependent on all the parent subcomponents (Observation 2.1), the probabilities that are related to all the subcomponents' healthy states are taken into account (see Section II-B and [3] for further details).…”

“…When these algorithms are employed independently and in isolation for diagnosing a specific component or subsystem, correlating faults that are identified at separate locations will lead to difficulties in assessing the overall system health. Therefore, a decision support system should be developed that would provide the ground personnel with an ability to perform diagnostic reasoning, coherently, by utilizing the telemetry data efficiently [1], [2]. In [3], we developed a hierarchical fault diagnosis model, namely a component dependence model (CDM), for ground-station-based diagnosis of complex multivehicle systems, such as those in formation flight of satellites where telemetry data are available, and access to precise mathematical models of the system under consideration is limited.…”