Generation Diagnosis/Prognosis in Automatic Test Systems

John Sheppard, ARINC Incorporated

The aerospace and defense industries have made use of so-called ?automatic test equipment? for over 40 years to test and maintain their systems. An ATE comprises a suite of electronic test instruments (e.g., multi-meters, waveform generators, and oscilloscopes), power supplies, a test control computer, a switching matrix, and an interface device to connect to a unit under test (UUT). The test control computer runs a program that controls both the UUT and the test instruments, sequencing a set of tests that are run against the UUT. Ultimately, the purpose of the ATE is to detect and isolate faults in the UUT so that the UUT can subsequently be repaired.

Until recently, the architecture of the ATE largely utilized brute-force interfaces and algorithms to test the UUT, and many ATE are configured for a specific UUT or class of UUTs. Due to the high cost of developing and maintaining ATE, current requirements by ATE users, including the department of defense and the commercial airlines, demand the development of systems that are interoperable, adaptable, and capable of handling the complexities of diagnosis and prognosis of multiple UUTs.

In this talk, I will discuss recent research in developing diagnostic test programs that address these demands. Specifically, I will describe an emerging automatic test system (ATS) framework for defining ATS architectures and place diagnostics and prognostics within that framework. I will then describe the evolution of diagnosis within the ATS and present recent research in the application of Bayesian networks to diagnosis and prognosis. Note that while Bayesian diagnosis has been around for some time, it has never been applied in the ATE environment. Note also that only reliability-based prognosis has been applied to electronic systems, and this method is known to be inadequate. It is believed that Bayesian methods (with the recent emergence of ?dynamic? Bayesian networks) offer some hope in addressing the prognosis problem and thus offer tremendous potential for the diagnosis problem as well.

Speaker Biography

John Sheppard is a corporate fellow at ARINC Incorporated, a company privately owned by the commercial airlines dedicated to solving complex problems for both the airline and defense industries. He has been performing research and development in model-based diagnosis and machine learning for almost 20 years and is recognized as a leading expert in system-level diagnosis within the defense industry. Dr. Sheppard received his PhD in computer science from Johns Hopkins University in 1997 and has been affiliated with JHU, either as a student or a part time member of the faculty, since 1988. He served as the technical program chair for AUTOTESTCON (the only conference on system-level test and diagnosis) in 2001 and is the 2007 technical program chair. He is also the vice chair of the IEEE Standards Coordinating Committee 20 on Test and Diagnosis of Electronic Systems and the past chair of the Diagnostic and Maintenance Control subcommittee of SCC20.