Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

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Over the past decade, the number of Earth orbiters and deep space probes has grown dramatically and is expected to continue in the future as miniaturization technologies drive spacecraft to become more numerous and more complex. This rate of growth has brought a new focus on autonomous and self-preserving systems that depend on fault diagnosis. Although diagnosis is needed for any autonomous system, current approaches are almost uniformly "ad-hoc," inefficient, and incomplete. Systematic methods of general diagnosis exist in literature, but they all suffer from two major drawbacks that severely limit their practical applications. First, they tend to be large and complex and hence difficult to apply. Second and more importantly, in order to find the minimal diagnosis set, i.e., the minimal set of faulty components, they rely on algorithms with exponential computational cost and hence are highly impractical for application to many systems of interest.Ib this paper, we propose a two-fold approach to overcome these two limitations. Then we report the details of a new and powerful tool, Diagnosis Engine version 1.0, we have developed based on these techniques. First, we propose a novel and compact reconstruction of General Diagnosis Engine (GDE), as one of the most fundamental approaches to model-based diagnosis. We then present a novel algorithmic approach for calculation of minimal diagnosis set. Using a powerful yet simple representation of the calculation of minimal diagnosis set, we map the problem onto two well-known problems, that is, the Boolean Satisfiability and 011 Integer Programming problems. The mapping onto Boolean Satisfiability enables the use of very efficient algorithms with a super-polynomial rather than an exponential complexity for the problem. The mapping onto 0/1 Integer Programming problem enables the use of a variety of algorithms that can efficiently solve the problem for up to several thousand components. These new algorithms significantly improve over the existing ones, enabling efficient diagnosis of large complex systems. In addition, the latter mapping allows, for the first time, determination of the bound on the solution, i.e., the minimum number of faulty components, before solving the problem. This is a powerful insight that can be exploited to develop yet more efficient algorithms for the problem.At the end, we report the results of validating and benchmarking of our engine based on this technology.