Mark David Osborn

Predictive Heat Exchanger Efficiency Monitoring

The performance of heat exchangers degrades with time due to fouling or deposition of material on the heat transfer surface. The fouling of critical exchangers in manufacturing plants results in a significant cost impact in terms of production losses, energy efficiency, and maintenance costs. While most plants monitor their exchangers to some degree, the ability to effect real and sustainable improvements requires four components: (1) real time monitoring; (2) an advance warning mechanism; (3) the ability to diagnose the cause of fouling; and (4) the ability to treat the cause in order to slow or reverse the degradation. CHeX is a comprehensive tool which monitors, predicts, and diagnoses heat exchanger performance. The unique features of this advanced technology include: numerous data cleaning steps to improve data quality and isolate a net fouling trend, an adaptive model which learns from the past to predict performance three years in advance, and knowledge-based diagnostics which identify the probable cause(s) of fouling and recommend corrective actions. The final control action is performed by a field engineer in adjusting the fouling treatment. The scope of the current paper includes only the detection and prediction features. To date, CHeX has been validated at three chemical processing plants, for fourteen exchangers. Selected case studies shall be presented to demonstrate the power of its algorithms over traditional calculations.© 2005 ASME

Information Fusion Strategy for Aircraft Engine Health Management

Modern aircraft engines are equipped with sophisticated sensing instruments to enable proactive condition monitoring and effective health management capability. Development of intelligent systems that efficiently process sensor and operational data both onboard and off-board, to provide maintenance personnel with timely decision support, is the key to minimize flight service disruption and reduce engine ownership cost. The goal of this research is to develop a practical approach and strategy to leverage various available information sources and modeling techniques to streamline the engine health management process and maximize system accuracy and efficiency. This paper demonstrates a flexible fusion architecture that encapsulates the key elements of the engine monitoring and diagnostic process, i.e., sensor trend analysis module for anomaly detection, feature selection and fault isolation module for root cause identification, a decision module for diagnostic model fusion and action determination, and finally, a feedback module for knowledge validation and continuous learning. At the core of this engine health management system is a diagnostic fusion model designed around a common fault hierarchy which captures both a priori probabilities and interactions among various engine faults isolated by different classification models. The fusion model will resolve conflicting assessments from individual diagnostic models and provide a more accurate and comprehensive engine state estimate.© 2007 ASME

Decision Support for Remote Monitoring and Diagnostics of Aircraft Engine Using Influence Diagrams

FAA regulations require the monitoring of all commercial aircraft engines to ensure airworthiness. In doing so, it provides economic advantages to engine owners to monitor engine performance and resolve identified issues in a timely manner to reduce operational costs or avoid secondary damage. Various remote monitoring and diagnostics service providers exist in the marketplace. However, a common understanding among most of them is that given limited time and information, it is an extremely difficult task to make quick and optimized decisions. Difficulties arise from the fact that an aircraft engine is a complex system and demands considerable expertise to diagnose, but also due to the uncertainty in estimating an engine’s true physical state because of measurement and process noise. Therefore, it is often difficult to decide what action to take in order to achieve the most desirable outcome. In this paper, a cost sensitive engine diagnostic and decision making methodology is described. Diagnostic tool performance at various decision thresholds is estimated over a large set of validated historical cases to evaluate sensitivity, specificity and other quality indices. These quality indices and a set of cost functions are utilized in influence diagrams to derive the optimized decision model in order to minimize costs given the uncertain engine condition and noisy parametric data.Copyright

Predictive Performance Assessment of Heat Exchangers for Proactive Remediation

Efficiency of industrial heat exchangers degrades over time as a result of deposition of the extraneous matter present in the heat-exchanging medium. Many times the degradation is rapid enough to take a heavy toll on the overall plant economy. It is therefore important to keep a closer watch on heat exchanger performance so that a suitable corrective action can be initiated the moment any abnormality is detected. While most plants monitor their exchangers to some degree, ability to effect real and sustainable improvements requires four components: (1) near-real time detection capability; (2) an early warning system (3) capability to diagnose the cause of fouling; and (4) right expertise to treat the cause in order to slow or reverse the degradation. CHeX2 is a comprehensive tool that we have developed, which monitors, predicts, and diagnoses heat exchanger performance. This tool has been designed to cover a wide range of heat transfer equipments namely simple heat exchangers, condensers and partial condensers with both the shell and tube-side condensation. The unique features of this advanced tool include: numerous data cleaning steps to enhance data quality and isolate a net fouling trend, an adaptive model which learns from the past to predict performance three years in advance, and knowledge-based diagnostics which identify the probable cause(s) of fouling and recommend corrective actions. A trained field engineer takes the final control action by adjusting the fouling treatment. The scope of the current paper includes only the detection and prediction features. To date, CHeX has been validated at three chemical processing plants, for fourteen exchangers. Selected case studies shall be presented to demonstrate the power of its algorithms over traditional calculations.