You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems 
Chapter Title:  Diagnostics 
1. Abstract of Cited Paper (BIB001):  Monitoring systems on aircraft engines typically record many parameters, sampled frequently, over the duration of a flight. On some newer engines such data have been recorded, and stored, over hundreds of flights. But data alone are not sufficient, and are too unwieldy, to help the analyst understand the state of the engine's health. Automated data mining, for knowledge discovery, is being successfully used in several fields. One well-known technique is a decision tree, which is induced from a subset of a known training set of engine data and outcome. Since engine parameters are continuous-valued a splitting criterion, known as the minimum description length principle, is used to define the branch points of the tree. The decision tree is induced from a subset of a known training set of engine data and outcome. Issues such as overfitting the training data, and the problem of large, or randomly-chosen, training sets are also discussed in this paper. Copyright ©2000 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. BACKGROUND Many airlines have invested in engine monitoring systems (EMS) on their newer aircraft. These systems record many parameters, such as temperatures, pressures, rotor speeds, fuel flow and others. The hope is that, by tracking the engines over hundreds of flights, one can discern component degradations which lead to diminished performance or safety. The large numbers of parameters, and the large volumes of time-series data, pose a challenge to the analyst sifting through the data. Simple checks such as minimum/maximum parameter bounds often lead to false alarms. Relaxing these bounds could lead to missed alerts. Several NASA Ames research efforts are directed at exploring ways of using computers to monitor the health of physical systems. In a previous paper [8] surveying aircraft engine health monitoring systems, various machine learning-based techniques, that are being researched for engine data analysis, were outlined. In this paper we focus on using the decision tree method for learning to classify engine's health based on its sensor data. (c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. 
2. Abstract of Cited Paper (BIB002):  Abstract Over the last 25 years, the Computer Science community and particularly the Artificial Intelligence community, have developed a framework for system diagnosis, called Model-Based Diagnosis. This framework is extremely general and covers a broad range of capabilities including detecting malfunctions, isolating faulty components, handling multiple faults, identifying repair actions, and automatically generating embedded software. This field grew independently of the fault detection and isolation community (FDI) and has developed its own terminologies and conventions. This paper is an attempt to present the fundamental concepts of Model-Based Diagnosis (MBD) in onc place and in one consistent terminology, and thus make the field much more accessible to the FDI community. 
3. Abstract of Cited Paper (BIB003):  Abstract The diagnosis systems considered in this paper rely on the inconsistency between the actual process behaviour and its expected behaviour as described by an analytical model. The inconsistency is exhibited in signals called residuals. Two methods for residual generation are presented in a tutorial way: the parity space and the observer based approaches. Linear and nonlinear models are successively considered as a basis for the design of the residual generators.

Literature Review:
The various on-wing health monitoring systems of today, which are a collection of separate, unrelated technologies, provide a basic level of monitoring. Their capabilities are relatively limited and the information they provide is used mostly to initiate maintenance actions, not for real-time decision-making. One instance where the information is used on wing is for sensor validation. The controller has some simple logic to perform basic limit or rate of change checks on engine sensors and actuators. In some cases, on-board engine models are used in conjunction with the controller's own sensor voting scheme to help determine which sensor is correct when redundant sensors disagree. The controller has the additional responsibility of checking whether a speed red-line has been exceeded, and reducing fuel flow when it has. Current engine vibration monitoring systems sample at a relatively low frequency-too low to capture much significant or useful information on the vibratory modes of the system. They check the vibration magnitude to determine that it is within a normal range. Magnitudes that are too high might indicate a bearing failure or engine imbalance, magnitudes that are too low might indicate a faulty sensor or seized engine. Lubrication system monitoring is performed using a magnetic chip detector to determine the existence of ferrous debris in oil. This is an indication of part wear. Life cycle counts are performed on-wing. Engine parts, especially those in the hot section, may experience a maximum number of severe thermal transients before they must be retired. Each time the engine goes through a start-up transient, the life cycle count for each of the critical components is incremented. This way, part life is tracked as a function of use to facilitate scheduled maintenance. The objective of a diagnostic system on a turbofan engine is to avoid catastrophic failures, prevent costly damage to engine components, provide accurate fault detection and isolation to maintenance personnel, and reduce costs due to premature or past-due maintenance. Often, such intelligent systems architectures are simply an artifact of the capabilities of the underlying available tools. The opposite of this "invisible hand" design is an explicitly-designed architecture that guides the software system development. The high-level factors that affect the design are available technology (e.g., neural network or rule-based reasoning) and the target application. A flexible architecture will allow adaptation to new technologies, such as 64-bit processors, wireless sensor networks and newer compilers, while allowing optimizations of accuracy, speed, and cost of system health management. Health management involves the complete path from sensing the value of a particular measurement to the execution of a particular action that enables optimal performance. In general, techniques in classification, inference, projection, and decision making can be applied to areas such as diagnosis, prognosis, condition-based maintenance, and fleet management. Diagnosis can be performed using inductive learning such as decision trees, BIB002 case-based reasoning, rule-based or model-based approaches, BIB003 BIB004 explanation-based learning, genetic algorithms for search and optimization, neural networks, fuzzy learning and soft computing techniques. For engine health management, the specific tasks may include a subset of fault detection, isolation, identification, prediction, explanation, remediation and simulation. Usually the most important fault characterizations are how they manifest, what causes them, and what they affect. Faults can be broadly categorized as follows: 1) When they occur: during the initial phase, during the regular operation phase or end-of-mission phase (there is a usually bathtub curve for probability of fault occurrence versus time) 2) How long they last: whether permanent, temporary, or intermittent 3) How they manifest: whether discrete or continuous (e.g., leak), abrupt or gradual 4) How they relate to other faults: whether independent, correlated, cascading, or simultaneous faults with a common cause 5) What causes them: inherent in design (structural or functional), due to uncertainties in operating envelope, or due to external conditions 6) What they affect: the component, the subsystem or the system 7) How critical are they: non-critical, recoverable or mission-critical Expected failure rates for many system components are usually outlined in a Failure Modes and Effects Analysis (FMEA). Other domain knowledge, required to build a model of the system, comes from schematics, block diagrams, instrumentation list (sensor type, location, number, redundancy, sampling rates), telemetry list and the operations timeline. There may also be information from fault analyses-fault trees, event trees, and probabilistic risk assessment. Software quality assurance is critical, especially if the controller action depends upon the results of the diagnostic system. The implemented software system for aircraft engine control will be a safety-critical, real-time system. It must satisfy explicit response time constraints. In addition, it must integrate with external software (such as diagnostic software from other subsystems) and share information with it. To implement such high performance diagnostic software, appropriate software engineering practices must be followed right from the beginning. This includes all phases such as requirements specification (for the controller and for the software), executable specifications, reliable coding practices and code generation, performance analysis, and verification and validation of the system. Engine malfunctions constitute a significant percentage of in-flight problems. While Engine Monitoring Systems (EMS) are routinely used, BIB001 the abundance and ambiguity of data, combined with simple, threshold-based triggers, results in a high percent of "nuisance" alarms that are costly to address. An additional challenge is the ability to record, store on-board, transmit to ground, store on ground, and interpret vast amounts of diverse data, and the ability to distill relevant information and useful knowledge. On-board monitoring isn't an end goal, but an essential step in diagnosis, prognosis or intelligent control. The rest of this section discusses various aspects of engine system and component diagnostics that are currently under development.