Michael J. Roemer

Bio: Michael J. Roemer is an academic researcher from University of Rochester. The author has contributed to research in topics: Prognostics & Fault detection and isolation. The author has an hindex of 24, co-authored 77 publications receiving 1899 citations.

Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]

Abstract: In recent years, numerous machinery health monitoring technologies have been developed by the US Navy to aid in the detection and classification of developing machinery faults for various Naval platforms. Existing Naval condition assessment systems such as ICAS (Integrated Condition Assessment System) employ several fault detection and diagnostic technologies ranging from simple thresholding to rule-based algorithms. However, these technologies have not specifically focused on the ability to predict the future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. An advanced prognostic capability is desired because the ability to forecast this future condition enables a higher level of condition-based maintenance for optimally managing total life cycle costs (LCC). A second issue is that a framework does not exist for "plug-and-play" integration of new diagnostic and prognostic technologies into existing Naval platforms. This paper outlines such prognostic enhancements to diagnostic systems (PEDS) using a generic framework for developing interoperable prognostic "modules". Specific prognostic module examples developed for gas turbine engines and gearbox systems are also provided.

Prognostic Enhancements to Diagnostic Systems for Improved Condition-Based Maintenance

Abstract: : In recent years, numerous machinery health monitoring technologies have been developed by the U.S. Navy to aid in the detection and classification of developing machinery faults for various Naval platforms. Existing Naval condition assessment systems such as ICAS (Integrated Condition Assessment System) employ several fault detection and diagnostic technologies ranging from simple thresholding to rule-based algorithms. However, these technologies have not specifically focused on the ability to predict the future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. An advanced prognostic capability is desired because the ability to forecast this future condition enables a higher level of condition-based maintenance for optionally managing total Life Cycle Costs (LCC). A second issue is that a framework does not exist for 'plug 'n play' integration of new diagnostic and prognostic technologies into existing Naval platforms. This paper will outline such Prognostic Enhancements to Diagnostic Systems (PEDS) using a generic framework for developing interoperable prognostic 'modules'. Specific prognostic module examples developed for gas turbine engines and gearbox systems are also provided.

Defining PHM, A Lexical Evolution of Maintenance and Logistics

Abstract: Prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient fault detection, and prediction of impending faults. Coupled with autonomic logistics for unprecedented responsiveness, cost effectiveness, and mission availability, PHM is largely automated in its application. Incorporating the principles of condition-based maintenance (CBM) along with the tenets of reliability-centered maintenance (RCM), the PHM paradigm extends these capabilities and provides a robust environment to optimize maintenance and logistics for increased operational availability (A0), and reduced life-cycle costs (LCC) while potentially increasing the reliability and life expectancy of mechanical, structural, and electronic systems. Driven by a demand for greater reliability at reduced cost and fueled by technological advancements, the PHM contribution to an already robust and confounding vocabulary surrounding maintenance and logistics is significant. As adopters of PHM technology attempt to define requirements and performance parameters, difficulties encountered with various non- standardized terminology indicate that the PHM vocabulary merits a lexical review. This paper will provide a compendium of PHM terminology along with definitions and examples, derived from the authors' experience in the implementation of PHM systems. Coalescing existing vocabularies and introducing, formally, the new lexicon of maintenance and logistics, the authors seek to aid in clarification of the emerging dialogue of life-cycle support.

Advanced diagnostics and prognostics for gas turbine engine risk assessment

Abstract: Real-time, integrated health monitoring of gas turbine engines that can detect, classify, and predict developing engine faults is critical to reducing operating and maintenance costs while optimizing the life of critical engine components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technologies presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices. This paper describes some novel diagnostic and prognostic technologies for dedicated, real-time sensor analysis, performance anomaly detection and diagnosis, vibration fault detection, and component prognostics. The technologies have been developed for gas turbine engine health monitoring and prediction applications which includes an array of intelligent algorithms for assessing the total 'health' of an engine, both mechanically and thermodynamically.

Assessment of data and knowledge fusion strategies for prognostics and health management

Abstract: Various data, feature and knowledge fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of anomaly, diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and outputs from various diagnostic and prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. The data fusion strategies discussed are principally probabilistic in nature and are used to aid in directly identifying confidence bounds associated with specific component fault identifications and predictions. Dempster-Shafer fusion, Bayesian inference, fuzzy-logic inference, neural network fusion and simple weighting/voting are the algorithmic approaches that are discussed. Data fusion architectures such as centralized fusion, autonomous fusion, and hybrid fusion are described in terms of their applicability to fault diagnosis and prognosis. The final goal is to find the optimal combination of measured system data, data fusion algorithms, and associated architectures for obtaining the highest overall prediction/detection confidence levels associated with a specific application. To achieve this goal, a set of metrics has been developed for gauging the performance and effectiveness of a fusion strategy. Specifically, this paper demonstrates how various metrics are used for assessing individual and fused vibration-based diagnostic algorithms. Evaluation of the diagnostic strategies was performed using gearbox seeded-fault and accelerated failure data.

Smart Dust: communicating with a cubic-millimeter computer

Abstract: Domain-specific integrated development environments can help capture specifications in the form of domain models. These tools support the design process by automating analysis and simulating essential system behavior. In addition, they can automatically generate, configure, and integrate target application components. The high cost of developing domain-specific, integrated modeling, analysis, and application-generation environments prevents their penetration into narrower engineering fields that have limited user bases. Model-integrated computing (MIC), an approach to model-based engineering that helps compose domain-specific design environments rapidly and cost effectively, is particularly relevant for specialized computer-based systems domains-perhaps even single projects. The authors describe how MIC provides a way to compose such environments cost effectively and rapidly by using a metalevel architecture to specify the domain-specific modeling language and integrity constraints. They also discuss the toolset that implements MIC and describe a practical application in which using the technology in a tool environment for the process industry led to significant reductions in development and maintenance costs.