A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Data/Information Fusion <s> The most common method for knowledge representation in an expert system is the production rule [Waterman 1986]. Unfortunately, the modularity inherent in a rule-based system is limiting, especially in an uncertain environment [Morawski 1989]. A fuzzy belief network (FBN) provides a more holistic, graphical approach and lends itself well to implementation in expert systems on personal and small computers. <s> BIB001 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Data/Information Fusion <s> Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation Data Fusion Algorithms and Architectures Bayesian Inference Dempster-Shafer Algorithm Artificial Neural Networks Voting Fusion Fuzzy Logic and Neural Networks Passive Data Association Techniques for Unambiguous Location of Targets. Appendices: Planck Radiation Law and Radiative Transfer Voting Fusion With Nested Confidence Levels. <s> BIB002 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Data/Information Fusion <s> Describes the methodology for usage of Bayesian belief networks (BBNs) in fault detection for aircraft gas turbine engines. First, the basic theory of BBNs is discussed, followed by a discussion on the application of this theory to a specific engine. In particular, the selection of faults and the means by which operating regions for the BBN system are chosen are analyzed. This methodology is then illustrated using the GE CFM56-7 turbofan engine as an example. <s> BIB003 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Data/Information Fusion <s> Aircraft gas-turbine engine data is available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C-17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM * . Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples. <s> BIB004
The value of individual sensors may become less intrinsic and more dependent on synergies gained from data fusion across diverse sensors as more sophisticated diagnostic and engine health management techniques gain acceptance, particularly in compensating for the unacceptably high false positive rates of many sensors and algorithms. A wealth of aircraft turbine engine data is available from a variety of sources including on-board sensor measurements, operating histories, and component models. Furthermore, additional data will become available as advanced prognostic sensors are incorporated into next generation gas turbine engine systems. The challenge is how to maximize the meaningful information extracted from these disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine. One approach to addressing this challenge is through the application of data fusion techniques. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single source alone. It draws from a wide range of technology areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. Under a NASA-funded effort, data fusion techniques are being applied for aircraft gas turbine engine health management purposes. BIB004 This effort is closely aligned with the ongoing C-17 Propulsion Health Management Flight Test Program led by NASA Dryden and is being applied to the Pratt & Whitney F117 engine which powers the C-17 aircraft. An overview of the Data Fusion architecture developed under this activity is shown in figure 13 . It is a hierarchical architecture, which monitors engine sensor information, performs signal processing, data analysis and health assessment, and provides maintenance action recommendations to the end user. It is also an open and modular architecture which allows additional sensor information and diagnostic modules to be added as they become available. In figure 13 a variety of information sources can be seen including sensor measurements, FADEC fault codes, maintenance and pilot observations, and engine maintenance history information. While these information sources are rich in content, they exist in vastly different formats and sample rates. For example advanced vibration sensor measurements may be sampled in the 10's of kilohertz range whereas gas path measurements may be sampled in the 10's of hertz range. Due to the disparate sample rates among system measurements, a necessary function to be performed prior to subsequent fusion is data alignment, which time synchronizes all system information to a common sampling rate. This is accomplished by up and down sampling the raw information. Converting the information to a common time frame helps support the distinction between correlated and non-correlated system events. The point in the process at which data alignment is performed on the information is significant, particularly for any high frequency measurements as the user wants to avoid the loss of any critical health information contained within the raw data. As shown in figure 13 , data analysis and feature extraction of the high frequency sensor measurements occurs prior to data alignment. The analysis modules perform signal processing and feature extraction. Example modules are vibration analysis, lubrication system monitoring, gas path performance monitoring, and fault codes generated by the FADEC. After data alignment and health feature extraction has been performed the information is provided to the information fusion module. The principle objective of the information fusion module is to convert multiple sources of information into a unified engine health assessment. This process requires a fundamental understanding of engine malfunctions, as well as a methodology for inserting evidence to support (or refute) a specific root cause of a malfunction. There are a variety of information fusion techniques that can be applied. BIB002 Stochastic data fusion approaches, such as a Bayesian Belief Network (BBN), provide a confidence level in the existence of a particular fault. BIB003 This can be useful in setting detection thresholds to obtain acceptable performance in terms of false alarms and missed detections. Stochastic data fusion approaches do however require a priori conditional probabilities for fault occurrence, fault detection, and fault missed detections to be specified. For aircraft gas turbine engine health management applications precise conditional probability information is often unavailable. Also, as the number of fault types and the number of fused diagnostic modules grows, the required number of conditional probabilities that must be specified grows exponentially. An alternate fusion approach is the Fuzzy Belief Network (FBN). BIB004 BIB001  FBN's use fuzzy belief functions to assign confidence levels in lieu of exact conditional probabilities. They are also more computationally tractable than BBN's. FBN's do require expert knowledge to generate the parametric interrelationships and assign fuzzy levels of confidence. The final step in the above information fusion architecture is the fault isolation reasoner module which processes the engine health assessment information along with past maintenance and pilot observations, and makes recommended maintenance actions. It consists primarily of a Failure Modes Effects and Criticality Analysis (FMECA) model of the engine along with a line maintainers' Fault Isolation Manual (FIM) procedure. BIB004 Applying information fusion techniques to the aircraft gas turbine engine health management application does hold much promise. Areas for future enabling work include instituting the necessary process to collect and maintain a suitable aircraft engine fault database. Such a fault database would be critical for providing the necessary conditional probability information required by stochastic data fusion techniques. Another requirement for an effective information fusion architecture is to have a rich source of information and diagnostic methods. Orthogonal information and diagnostic methods are ideal, as they will be complimentary in corroborating or refuting a broad range of fault scenarios. On the other hand, the fusion of information and/or methods that are either highly correlated or uncorrelated in their fault coverage will provide little benefit versus using the diagnostic techniques independently. Thus effective system studies need to be performed to include within the fusion architecture the correct combination of sensors and algorithms to address the faults of interest.