Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.
As the space shuttle ages, it is experiencing wiring degradation problems, including arcing, chaffing, insulation breakdown and broken conductors. A systematic and comprehensive test process is required to thoroughly test and QA the wiring systems. The NASA Wiring Integrity Reseach (WIRe) team recognized the value of a formal model based analysis for risk assessment and fault coverage analysis using our TEAMS toolset and commissioned a pilot study with QSI to explore means of automatically extracting high fidelity multisignal models from wiring information databases. The MEC1 Shuttle subsystem was the subject of this study. The connectivity and wiring information for the model was extracted from a Shuttle Connector Analysis Network (SCAN) electronic wirelist. Using this wirelist, QSI concurrently created manual and automatically generated wiring models for all wire paths associated with connector J3 on the MEC1 assembly. The manually generated model helped establish the rules of modeling. The complete MEC1 model was automatically generated based on these rules, thus saving significant modeling cost. The methodology is easily extensible to the entire shuttle wiring system. This paper presents our modeling and analysis results from the pilot study along with our proposed solutions to the complex issues of wiring integrity assessment problem.
Modern systems such as nuclear power plants, the Space Shuttle or the International Space Station are examples of mission critical systems that need to be monitored around the clock. Such systems typically consist of embedded sensors in networked subsystems that can transmit data to central (or remote) monitoring stations. At Qualtech Systems, we are developing a Remote Diagnosis Server (RDS) to implement a remote health monitoring systems based on telemetry data from such systems. RDS can also be used to provide online monitoring of sensor-rich, network capable, legacy systems such as jet engines, building heating-ventilation-air-conditioning systems, and automobiles. The International Space Station utilizes a highly redundant, fault tolerant, software configurable, complex, 1553 bus system that links all major sub-systems. All sensor and monitoring information is communicated using this bus and sent to the ground station via telemetry. It is, therefore, a critical system and any failures in the bus system need to be diagnosed promptly. We have modeled a representative section of the ISS 1553 bus system using publicly accessible information. In this paper, we present our modeling and analysis results, and our Telediagnosis solution for monitoring and diagnosis of the ISS based on Telemetry data.
When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.
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System Diagnosis and Prognosis: Security and Condition Monitoring Issues III