Knowledge-Based Architecture for Integrated Condition Based Maintenance of Engineering Systems

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Saxena, Abhinav

Advisor dc:contributor.advisor

• Vachtsevanos, George J.

Committee members dc:contributor.committeemember

• Egerstedt, Magnus
• Ferri, Aldo A.
• Howard, Ayanna
• Saad, Ashraf

• Human in the loop learning
• Dynamic case-based reasoning
• Industrial language processing
• Evolving knowledgebase
• Condition based maintenance
• Knowledge management

Handle dc:identifier.uri

http://hdl.handle.net/1853/16125

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/16125