Demonstrating semantic interoperability of diagnostic models via AI-ESTATE (original) (raw)

Standard Diagnostic Services for the ATS framework

2009 IEEE AUTOTESTCON, 2009

The US Navy has been supporting the demonstration of several IEEE standards with the intent of implementing these standards for future automatic test system procurement. In this paper, we discuss the second phase of a demonstration focusing on the IEEE P1232 AI-ESTATE standard. This standard specifies exchange formats and service interfaces for diagnostic reasoners. The first phase successfully demonstrated the ability to exchange diagnostic models through semantically enriched XML files. The second phase is focusing on the services and has been implemented using a web-based, service-oriented architecture. Here, we discuss implementation issues and preliminary results.

Implementing an AI-ESTATE based diagnostic engine component

2000

This paper focuses on the construction of a Diagnostic Engine Component (DEC) based on the IEEE 1232 Standard known as "AI-ESTATE as part of a new approach to constructing and using Automatic Test System (ATS) software in the support of the modern digital avionics maintenance program. Traditional ATS have been constructed of single monolithic software programs that run on single machines. The design approach discussed in this paper is based on decomposing the monolithic software program into primary elements then reengineering the elements into a new type of software component. While the construction of the DEC is detailed, the roles of other components are discussed along with deployment and how the test engineer develops the diagnostic models and the test program set.

DiagML - an interoperability platform for test and diagnostics software

2002

The integration of diagnostic software and test execution environments developed by different vendors requires a common data exchange format. The paper describes a new format based on XML, called the Diagnostic Modeling Language (DiagML). This format is currently used for integrating software applications developed by DSI International and TYX Corporation. The design-to-test process supported by DiagML is illustrated through a diagnostic application for a simple analog circuit.

Knowledge engineering and diagnostics - today and tomorrow

Diagnostyka, 2004

The paper addresses several issues concerning knowledge engineering in the context of technical diagnostics. The goal consists in preparing, verifying, validating and then implementing domain-specific knowledge, capable of aiding diagnosticians and other personnel in operating machinery and equipment. New trends in knowledge engineering focus our attention on discovering useful knowledge from huge databases that collect process data acquired from machinery, and making this knowledge even more easily available for humans. The paper concludes with discussion about prospective potential issues for next decades of this century.

Semantic Rule-Based Equipment Diagnostics

Lecture Notes in Computer Science, 2017

Industrial rule-based diagnostic systems are often data-dependant in the sense that they rely on specific characteristics of individual pieces of equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. In this work we address these problems by relying on Ontology-Based Data Access: we use ontologies to mediate the equipment and the rules. We propose a semantic rule language, sigRL, where sensor signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system, deployed it in Siemens, and conducted experiments to demonstrate both usability and efficiency.

An ontology-driven, diagnostic modeling system

Objectives To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. Materials and methods We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. Results The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. Discussion Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. Conclusions The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.

Semantic Rules for Machine Diagnostics

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Rule-based diagnostics of equipment is an important task in industry. In this paper we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages used in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We study computational complexity of SigRL: execution of diagnostic programs, provenance computation, as well as automatic veri cation of redundancy and inconsistency in diagnostic programs.

The Object-Oriented Design of Intelligent Test Systems

AUTOTESTCON-IEEE-, 1993

Recent IEEE and Department of Defense (DoD) initiatives to standardize automatic test equipment (ATE) architectures are providing unique opportunities to improve the development of test systems. The purpose of A Broad Based Environment for Test (ABBET) initiative is to provide the next generation in ATE and test program set (TPS) development through the standardization of test services and test development tools. The Artificial Intelligence and Expert System Tie to Automatic Test Equipment (AI-ESTATE) initiative will ...

Information-based standards and diagnostic component technology

2000 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. Future Sustainment for Military Aerospace (Cat. No.00CH37057), 2000

Software development methods have evolved over the years from structured design of procedural code, to object oriented design, to component-based design. Recent requirements by industry and government have resulted in the development of interface specifications and standards designed to facilitate acquisition of large systems based on the concepts of component technology. In this paper, we discuss the development of information-based standards for diagnostic information and diagnostic reasoning intended to provide the definition of diagnostic components within a larger test or health management envir onmen t.

Knowledge Components Using Agent Technology for a Diagnostic System

The paper presents an agent-based platform dedicated for the development of decentralised knowledge-based systems. At conceptual (virtual) level such a system may be treated a set of knowledge components, which represent well-structured, reusable pieces of knowledge together with describing it ontology. Integration of the components is possible only if the knowledge of any component may be accessed via ontology known to the others. At physical (real) level the ralization of the system is based on agent technology, which should allow for interoperability between heterogeneous entities (built on diffrent platforms, using its own knowledge representation and reasoning strategy, etc.). The considerations are illustrated by a particular application of the above approach to a decentralised expert system for casting defects diagnosis.