The operations of aircraft fleets typically result in large volumes of data collected during the execution of various operational and support processes. This paper reports on an Army-sponsored study conducted to research the applicability of data mining for processing such data. The study focused on three aspects: (1) understanding the aviation operations, maintenance environment, and data collection system; (2) investigating data analysis approaches with the purpose of identifying promising methods pertinent to aircraft health management; and (3) defining requirements for a tool to support the aviation maintenance planners and fleet managers. Results of preliminary analyses of two maintenance data and flight data sets are presented. An architecture for managing and mining aviation maintenance data and using results to update models used by diagnostic modules for fault isolation during maintenance activity is also presented.
The ability to understand a system's behavior in both normal and failed conditions is fundamental to the design of error-tolerant systems as well as to the development of diagnostics. The System Analysis for Failure and Error Reduction (SAFER) Project seeks to provide designers with tools to visualize potential sources of error and their effects early in the design of human-machine systems. The project is based on an existing technology that provides a failure-space modeling environment, analysis capabilities for troubleshooting, and error diagnostics using design data of machine systems. The SAFER Project extends the functionality of the existing technology in two significant ways. First, by adding a model of human error probability within the tool, designers are able to estimate the probabilities of human errors and the effects that these errors may have on system components and on the entire system. Second, the visual presentation of failure-related measures and metrics has been improved through a process of user-centered design. This paper will describe the process that was used to develop the human error probability model and will present novel metrics for assessing failure within complex systems.
Diagnosis and prognosis are processes of assessment of a system's health - past, present and future - based on observed data and available knowledge about the system. Due to the nature of the observed data and the available knowledge, the diagnostic and prognostic methods are often a combination of statistical inference and machine learning methods. The development (or selection) of appropriate methods requires appropriate formulation of the learning and inference problems that support the goals of diagnosis and prognosis. An important aspect of the formulation is modeling - relating the real system to its mathematical abstraction. The models, depending on the application and how well it is understood, can be either empirical or scientific (physics based). The expression of the model, too, tends to be statistical (probabilistic) to account for uncertainties and randomness. This paper explores the impact of diagnostic and prognostic goals on modeling and reasoning system requirements, with the purpose of developing a common software framework that can be applied to a large class of systems. In particular, the role of failure-dependency modeling in the overall decision problem is discussed. The applicability of Qualtech Systems' modeling and diagnostic software tools to the presented framework for both the development and implementation of diagnostics and prognostics is assessed. Finally, a potential application concept for advancing the reliability of Navy shipboard Condition Based Maintenance (CBM) systems and processes is discussed.