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.

Abstract-This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into a list of abnormaly performing aircraft, abnormal flight-to-flight trends, and individual flight anomalies by fitting a large scale multi-level regression model to the entire data set. The model takes into account fixed effects: flight-to-flight and vehicle-tovehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, a multi-terabyte airline data set with a half million flights was processed in a few hours. The anomalies found include wrong values of computed variables such as aircraft weight and angle of attack as well as failures, biases, and trends in flight sensors and actuators. These anomalies were missed by the FOQA data exceedance monitoring currently used by the airline.

By tradition public administration is regarded as a division of political science. Woodrow Wilson set the stage for this concept in his original essay identifying public administration as a subject worthy of special study, and spokesmen for both political science and public administration have accepted it since. Thus Leonard White, in his 1930 article on the subject in the Encyclopedia of the Social Sciences, recognizes public administration as “a branch of the field of political science.” Luther Gulick follows suit, observing in 1937 that “Public administration is thus a division of political science ….” So generally has this word got around that it has come to the notice of the sociologists, as is indicated in a 1950 report of the Russell Sage Foundation which refers to “political science, including public administration….” “Pure” political scientists and political scientists with a public administration slant therefore are not alone in accepting this doctrine, which obviously enjoys a wide and authoritative currency.But if public administration is reckoned generally to be a child of political science, it is in some respects a strange and unnatural child; for there is a feeling among political scientists, substantial still if mayhap not so widespread as formerly, that academicians who profess public administration spend their time fooling with trifles. It was a sad day when the first professor of political science learned what a manhole cover is! On their part, those who work in public administration are likely to find themselves vaguely resentful of the lack of cordiality in the house of their youth.

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The worldwide civilian aviation system is one of the most complex dynamical systems ever created. Most modern commercial aircraft have onboard flight data recorders (FDR) that record several hundred discrete and continuous parameters at approximately 1 Hz for the entire duration of the flight. This data contains information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper we discuss recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents. The data mining techniques include scalable multiple kernel learning for largescale distributed anomaly detection. A novel multivariate time series search algorithm is used to search for signatures of discovered anomalies on massive data sets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high dimensional Flight Operations Quality Assurance (FOQA) data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complimenting the state-of-theart human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper we discuss the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data.