Engine Health Monitoring System Research Articles

In Situ Assessment of Oil Quality Sensor Performance in Engine Lubricant Flow

Assessing the functional quality of an engine lubricant through real-time sensing could pave the way for development of comprehensive engine health monitoring systems. In this study, a permittivity-based, commercial off-the-shelf (COTS) oil quality sensor was implemented in the lubricant flow of a diesel engine after detailed evaluation on a benchtop test facility. The sensor was mounted on the oil filter housing of the engine in the post-filter oil flow, and its implementation required no modifications to the engine block. Simultaneously, the lubricant flow was visualized by incorporating a novel test cell in the oil flow path. Both the sensor assembly and the flow visualization cell were fully characterized on the benchtop facility prior to implementation on the engine. In these experiments, fresh and used samples of the engine’s recommended oil were tested, and the sensor’s oil quality measurements showed noticeable differences between the engine and benchtop studies, a feature attributable to the observed presence of aeration intrinsic to the engine oil flow. These results prove that the adaptation of permittivity-based sensors for effective real-time engine lubricant quality monitoring will require comparative assessment of oil quality measurements in aerated and nonaerated flow fields.

Reliability Assessment of Nuclear Thermal Engine Configuration and Health Monitoring System

Today’s space nuclear technology programs are often confronted with two fundamental challenges early in the project life cycle: 1) development and testing will be more expensive than a non-nuclear alternative, and 2) the consequences of failure will be more severe. As a result, many space nuclear programs have been designed to minimize testing and maximize their probability of success: their reliability. The United States' Nuclear Engine for Rocket Vehicle Applications Program recognized these facts early on, and by 1961 the program’s primary objective set safety and reliability as the overriding considerations. This focus on reliability greatly influenced the engine's design towards minimizing the possible number of catastrophic failures modes. As such, the final configuration heavily relied on duplicate components for redundancy, including duplicate turbopumps. Despite these efforts, at program cancellation in 1972, the non-nuclear subsystem only achieved a mission predicted reliability of 33%. Some of the most significant contributions to rocket engine reliability in the last 50 years have been from advancements in the Health Monitoring System (HMS). Through rigorous instrumentation and control an engine's HMS has the potential to convert over 90% of a system's catastrophic failure modes to a safe shutdown situation. Due to this, many modern rocket engine designs no longer require redundant components and can prioritize performance-enhancing configurations over those that inherently minimize failure modes. Unfortunately, a standard liquid rocket engine HMS will likely not be compatible with a nuclear rocket engine (NRE). The HMS for a liquid rocket engine most often prevents catastrophic failure by shutting off flow to the combustion chamber. This would not work for a NRE, as removing propellant flow to the reactor could result in reactor overheating and meltdown. Maintaining flow to the reactor is often essential for safe NRE operation, such that reliance on an advanced HMS system alone may not be sufficient. This work investigates the feasibility of an NRE HMS by comparing the HMS designs for liquid rocket engines and terrestrial nuclear power plants and evaluates the necessity for redundant components to maximize overall system reliability. Keywords: Nuclear Thermal Propulsion, Nuclear Rocket Engine, Reliability, Fault Prevention, Fault Tolerance, Turbopump

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques.

Aero engine health monitoring, diagnostics and prognostics for condition-based maintenance: an overview

Abstract Aero engine performance deterioration highly influences its reliability, availability and life cycle. Predictive maintenance is therefore a key figure within Industry 4.0, which guarantees high availability and reduced downtime thus reduced operational costs for both military and civil engines. This leads to maintenance on demand and needs an effective engine health monitoring system. This paper overviews the work carried out on aero engine health monitoring, diagnostic and prognostic techniques based on gas path performance parameters. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality data base using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Generating dependable information about the health condition of the engine is therefore a requisite for a successful implementation of condition-based maintenance. Based on this study, further research can be attempted to predict residual life of critical components using degradation pattern from aero engine performance data bank which will be an invaluable asset for engine designers as well as for operators.

Development of a combined Artificial Neural Network and Principal Component Analysis technique for Engine Health Monitoring

In aerospace sector, reliability is a crucial point. Modern technologies widely use Artificial Intelligence (AI) algorithms together with detections by sensors in order to design a health-based maintenance plan which stops an aircraft only when needed. In this work, an Engine Health Monitoring (EHM) system was developed by exploiting AI algorithms as Artificial Neural Networks (ANNs) trained to estimate a series of Performance Parameters (PPs) used as index of the health status of the main components constituting an engine. A neural network called Feed-Forward Neural Network (FFNN) in combination with a Principal Component Analysis (PCA) for feature reduction was used in this paper. The software Gas turbine Simulation Program (GSP) was used to generate a series of data containing information about engine performance under different flight conditions and compressor degradation levels. The datasets were subsequently used to train the neural networks to estimate the PPs of the degraded component. The final purpose of the present work is to develop an efficient diagnostic system useful to increase flight safety and decrease maintenance costs and fuel consumption.

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.

Modelling and supporting flight crew decision-making during aircraft engine malfunctions: developing design recommendations from cognitive work analysis

In this article, we analyse flight crew response to an in-flight powerplant system malfunction (PSM) using control task analysis. We demonstrate the application of the decision ladder template and the skills, rules, and knowledge (SRK) framework to this new area of inquiry. Despite the high reliability of turbofan engines, accidents and incidents involving PSM still occur. During these unusual events, flight crew have not always responded appropriately, leading to a reduction in safety margins or disruption of operations. This article proposes recommendations for technological and information system that can support flight crew in responding safely and appropriately to a PSM. These recommendations focus on new ways in which information from engine health monitoring system and other sources of data can be utilised and displayed. Firstly, we conducted knowledge elicitation using Critical Decision Method (CDM) interviews with airline pilots who have experienced real or simulated PSM events. We then developed generic decision ladders using the interview data, operations manual, training manual, and other guideline documents. The generic decision ladders characterise the different stages of responding to PSM identified as part of the research. These stages include: regaining and maintaining control of aircraft, identifying PSM and selecting appropriate checklists to secure the engine, and modifying the flight plan. Using the decision ladders and insights from the CDM interviews, we were able to identify cognitive processes and states that are more prone to errors and therefore more likely to generate an inappropriate response. Using the SRK framework, we propose design recommendations for technological and information systems to minimise the likelihood of such inappropriate response. We conclude that this combination of methods provides a structured and reliable approach to identifying system improvements in complex and dynamic work situations. Our specific contributions are the application of these techniques in the unrepresented area of flight operations, and the development of evidence-based design recommendations to improve flight crew response to in-flight powerplant system malfunctions.

A Self-Powered Engine Health Monitoring System Based on L-Shaped Wideband Piezoelectric Energy Harvester.

Engine health monitoring is very important to maintain the life of engines, and the power supply to sensor nodes is a key issue that needs to be solved. The piezoelectric vibration energy harvester has attracted much attention due to its obvious advantages in configuration, electromechanical conversion efficiency, and output power. However, the narrow bandwidth has restricted its practical application. A self-powered engine health monitoring system was proposed in this paper, and an L-shaped wideband piezoelectric energy harvester was designed and implemented. The L-shaped beam-mass structure and the piezoelectric bimorph cantilever beam structure was combined to form the wideband piezoelectric energy harvester configuration, which realized effective output at both resonance points. The theoretical model and finite element simulation analysis of the wideband piezoelectric energy harvester were proposed and the parameters were optimized based on that to meet the requirement of the vibration frequency of the engine. The experimental results show that the proposed energy harvester can be applied into the automobile engine health monitoring system. Engine signal analysis results also demonstrate that the proposed system can be used for engine health monitoring.

Multi-task Bayesian compressive sensing for vibration signals in diesel engine health monitoring

In the diesel engine health monitoring system, massive vibration signals are acquired and transmitted to the system center for failure detection and identification. To improve the efficiency of data transmission, an improved compressive sensing (CS) algorithm is developed for vibration signal processing by taking advantages of the multi-task Bayesian compressive sensing (MT-BCS) and classification theory. In this paper, the vibration signals are divided into different ‘tasks’ by rectangular window function to reduce the complexity of CS. with the effects of noises considered, the reconstruction model for the vibration signals is established based on the learned dictionary. Whereafter, Fuzzy C-means (FCM) is used to classify all the tasks into several clusters, which guarantees the existence of information sharing in each cluster. Thus multi-task Bayesian regression algorithm can be used for the tasks belonging to the same cluster to improve the reconstruction effect for diesel engine vibration signals. Finally, the effectiveness of the proposed multi-task Bayesian compressive sensing is validated by the experiments.

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Modern condition monitoring-based methods are used to reduce maintenance costs, increase aircraft safety, and reduce fuel consumption. In the literature, parameters such as engine fan speeds, vibration, oil pressure, oil temperature, exhaust gas temperature (EGT), and fuel flow are used to determine performance deterioration in gas turbine engines. In this study, a new model was developed to get information about the gas turbine engine’s condition. For this model, multiple regression analysis was carried out to determine the effect of the flight parameters on the EGT parameter and the artificial neural network (ANN) method was used in the identification of EGT parameter. At the end of the study, a network that predicts the EGT parameter with the smallest margin of error has been developed. An interface for instant monitoring of the status of the aircraft engine has been designed in MATLAB Simulink. Any performance degradation that may occur in the aircraft’s gas turbine engine can be easily detected graphically or by the engine performance deterioration value. Also, it has been indicated that it could be a new indicator that informs the pilots in the event of a fault in the sensor of the EGT parameter that they monitor while flying.

Design Considerations for Tip Clearance Control and Measurement on a Turbine Rainbow Rotor With Multiple Blade Tip Geometries

The accurate design, control, and monitoring of the running gaps between static and moving components are vital to preserve the mechanical integrity and ensure the correct functioning of any compact rotating machinery. Throughout engine service, the rotor tip clearance undergoes large variations due to installation tolerances or as the result of different thermal expansion rates of the blades, rotor disk, and casing during speed transients. Hence, active tip clearance control concepts and engine health-monitoring systems rely on precise real-time gap measurements. Moreover, this tip gap information is crucial for engine development programs to verify the mechanical and aerothermal designs and validate numerical predictions. This paper presents an overview of the critical design requirements for testing engine-representative blade tip flows in a rotating turbine facility. This paper specifically focuses on the challenges related with the design, verification, and monitoring of the running tip clearance during a turbine experiment. In the large-scale turbine facility of the von Karman Institute, a rainbow rotor was mounted for simultaneous aerothermal testing of multiple blade tip geometries. The tip shapes are a selection of high-performance squealer-like and contoured blade tip designs. On the rotor disk, the blades are arranged in seven sectors operating at different clearance levels from 0.5 up to 1.5% of the blade span. Prior to manufacturing, the blade geometry was modified to compensate for the radial deformation of the rotating assembly under centrifugal loads. A numerical procedure was implemented to minimize the residual unbalance of the rotor in rainbow configuration and to optimize the placement of every single airfoil within each sector. Subsequently, the rotor was balanced in situ to reduce the vibrations and satisfy the international standards for high balance quality. Three fast-response capacitive probes located at distinct circumferential locations around the rotor annulus measured the single-blade tip clearance in rotation. Additionally, the minimum running blade clearance is captured with wear gauges located at five axial positions along the blades chord. The capacitance probes are self-calibrated using a multitest strategy at several rotational speeds. The in situ calibration methodology and dedicated data reduction techniques allow the accurate measurement of the distance between the turbine casing and the local blade tip features (rims and cavities) for each rotating airfoil separately. General guidelines are given for the design and calibration of a tip clearance measurement system that meets the required measurement accuracy and resolution in function of the sensor uncertainty, nominal tip clearance levels, and tip seal geometry.

Conceptual Study on Integration of Engine Health Monitoring (EHM) System with Integrated Vehicle Health Monitoring (IVHM) System

AbstractAn approach to upgrade existing control system and lean architecture principle is presented to optimize the system along with reduction of weight by understanding the physical system design, Engine Health Monitoring (EHM) system and data study from Integrated Vehicle Health Monitoring (IVHM) system (Aaseng n.d.). This paper brings out the integration of EHM sufficiency with IVHM system. Research effort to describe the hardware, software, algorithms and engine parameters required to monitor various events capturing, health and life management issues with IVHM. Here basic functionality will be performance monitoring, vibration monitoring, event monitoring (including stall, surge, over speed, flameout, over temperature, oil parameter and life monitoring) anti ice system with IVHM requirement of mission completion, safety and data protection.A systematic SE approach and methodology with need and requirement is generated using DOORS software (INCOSE 2011). Full requirement capture with engine designer and aircraft designer (Harinarayana, Developing light combat aircraft: foresight as the guiding principle 2004) for integrating with IVHM (Philip A. Scandura October 30, 2005)has been studied and brought out to model the concept using Rhapsody software. Here various operational scenarios has been attempted to make the functional model. Functional model has been tested for various operational issues and a final configuration has been proposed to the designer (Jaw, Van and Mink 7 – 10 May 2007). This study has brought out a perspective work model for a designer and as a result of this the final product will be tested for integrating the EHM with IVHM

A High Temperature Frequency Signal Conditioning Unit for Aeronautical Rotating Systems

This paper covers the development and testing of a high temperature signal conditioning unit capable of sensing wide dynamic range signals from rotating sensors. The unit includes a current peak detector block, a digital pulse generator, ESD (electrostatic discharge) protection and generates an output current proportional to the peak value of the applied input signal. The digital data from the unit can be directly utilized by the FADEC (Full Authority Digital Electronic Control) system or the EHMS (Engine Health Monitoring System) on an aeroengine. The mixed signal unit presented in this paper was designed and fabricated on 1 $\mu\text{m}$ silicon-on-insulator (SOI) CMOS (complementary metal oxide semiconductor) technology and has been shown to function up to 235 $^{\circ}$ Celsius with very high linearity of the output signal. This is the highest operating temperature of an integrated signal conditioning unit developed for a civil aircraft. The mixed signal unit was mounted on a high temperature hybrid module and helps reducing the weight of the aeroengine by several kilograms.

Vibration-based detection of axial-flow compressor unstable operation

Computer-aided and experimental research of a axial stage stability loss is presented. Informative criteria of the pre-surge compressor state is formulated. It is proved that the harmonic of a rotating stall is unstable, standing out sharply against noise; thus it can serve as a rotating stall indicator and a surge precursor. It was proved that vibration from the mounted on casing sensor can be applicable for rotating stall detection. An algorithm is proposed that detects the compressor pre-surge state by vibrations of the compressor casing. The algorithm is easy to be implemented in engine control and health monitoring systems

Inductive sensors for blade tip-timing in gas turbines

Abstract The paper reviews features and applications of the upgraded inductive sensor for BTT, which is able to operate in contact with exhaust gases of temperature even as high as 1200 K. The new design includes metal-ceramic housing ensuring proper heat transfer, magnetic circuit containing set of permanent magnets with various magnetic field values and Curie temperatures, completely redesigned windings and current/voltage converter used instead of an electromotive force amplifier. Its principle of operation is based on electro-dynamical interaction and therefore it may be referred as a passive eddy-current sensor. The sensor technique has been demonstrated on four stages of a surplus military turbofan including the high pressure turbine as part of the engine health monitoring system. We present signal samples and review methods used for online processing of time-of-arrival signals when only a limited number of sensors is available.

Smart, In Situ, Wide Range Pressure Sensor for Advanced Engine Controls

Air Force Research Laboratory (AFRL) is pursuing development of advanced, distributed, intelligent, adaptive engine controls and engine health monitoring systems. The goals this pursuit are enhancing engine performance, safety, affordability, operability, and reliability while reducing obsolescence risk. The development of smart, high-bandwidth, high-temperature-operable, wide-range, pressure/temperature multi-sensors, which addresses these goals, is discussed. The resulting sensors and packaging can be manufactured at low cost and operate in corrosive environments, while measuring temperatures up to 2,552 °F (1,400 °C) with simultaneous pressure measurements up to 1,000 psi (68 atm). Such a sensor suite provides unprecedented monitoring of propulsion, energy generation, and industrial systems. The multi-sensor approach reduces control system weight and wiring complexity, design time, and cost, while increasing accuracy and fault tolerance. In situ pressure sensors reduce size and weight while eliminating failures associated with hypo-tube fouling. High-bandwidth sensors support advanced compressor and combustor control schemes that facilitate higher thrust, reduced fuel consumption and reduced emissions. Smart sensors are a key enabling technology for distributed engine control which is an important architectural paradigm shift to achieve increased flexibility, scalability, fault tolerance and performance while reducing weight, lifecycle costs and obsolescence risk. Current efforts include combining the sensor technology with other high-temperature electronics to produce smart sensors for distributed engine controls, capacitive transducer elements with increased dynamic bandwidth, and advanced high-temperature sensor packaging technologies. Test results demonstrating technology readiness level (TRL) 6 are reported herein.

Design and Assembly of High Temperature Distributed Aero-engine Control System Demonstrator

This paper covers the development of a distributed high temperature electronics demonstrator for integration with sensor elements to provide digital outputs that can be used by the FADEC (Full Authority Digital Electronic Control) system or the EHMS (Engine Health Monitoring System) on an aircraft engine. This distributed electronics demonstrator eliminates the need for the FADEC or EHMS to process the sensor signal, which will assist in making the overall system more accurate and efficient in processing only digital signals. This will offer weight savings in cables, harnesses and connector pin reduction. The design concept was to take the output from several on-engine sensors, carry out the signal conditioning, multiplexing, analogue to digital conversion and data transmission through a serial data bus. The unit has to meet the environmental requirements of DO-160 with the need to operate at 200°C, with short term operation at temperatures up to 250°C. The work undertaken has been to design an ASIC based on 1.0μm Silicon on Insulator (SOI) device technology incorporating sensor signal conditioning electronics for sensors including resistance temperature probes, strain gauges, thermocouples, torque and frequency inputs. The ASIC contains analogue multiplexers, temperature stable voltage band-gap reference and bias circuits, ADC, BIST, core logic, DIN inputs and two parallel ARINC 429 serial databuses. The ASIC was tested and showed to be functional up to a maximum temperature of 275°C. The ASIC has been integrated with other high temperature components including voltage regulators, a crystal oscillator, precision resistors, silicon capacitors within a hermetic hybrid package. The hybrid circuit has been assembled within a stainless steel enclosure with high temperature connectors. The high temperature electronics demonstrator has been shown to operate from −40°C to +250°C. This work has been carried out under the EU Clean Sky HIGHTECS project with the Project being led by Turbomeca (Fr) and carried out by GE Aviation Systems (UK), GE Research – Munich (D) and Oxford University (UK).

Trends in Automotive Packaging

The long-term trend in automobiles has been increasing electronics content over time. This trend is expected to continue and drives diverse functional, form factor, and reliability requirements. These requirements, in turn, are leading to changes in the package types selected and the performance specifications of the packages used for automotive electronics. Several examples will be given. This abstract covers the development of a distributed high temperature electronics demonstrator for integration with sensor elements to provide digital outputs that can be used by the FADEC (Full Authority Digital Electronic Control) system or the EHMS (Engine Health Monitoring System) on an aircraft engine. This distributed electronics demonstrator eliminates the need for the FADEC or EHMS to process the sensor signal, which will assist in making the overall system more accurate and efficient in processing only digital signals. This will offer weight savings in cables, harnesses and connector pin reduction. The design concept was to take the output from several on-engine sensors, carry out the signal conditioning, multiplexing, analogue to digital conversion and data transmission through a serial data bus. The unit has to meet the environmental requirements of DO-160 with the need to operate at 200°C, with short term operation at temperatures up to 250°C. The work undertaken has been to design an ASIC based on 1.0 μm Silicon on Insulator (SOI) device technology incorporating sensor signal conditioning electronics for sensors including resistance temperature probes, strain gauges, thermocouples, torque and frequency inputs. The ASIC contains analogue multiplexers, temperature stable voltage band-gap reference and bias circuits, ADC, BIST, core logic, DIN inputs and two parallel ARINC 429 serial databuses. The ASIC was tested and showed to be functional up to a maximum temperature of 275°C. The ASIC has been integrated with other high temperature components including voltage regulators, a crystal oscillator, precision resistors, silicon capacitors within a hermetic hybrid package. The hybrid circuit has been assembled within a stainless steel enclosure with high temperature connectors. The high temperature electronics demonstrator has been demonstrated operating from −40°C to +250°C. This work has been carried out under the EU Clean Sky HIGHTECS project with the Project being led by Turbomeca (Fr) and carried out by GE Aviation Systems (UK), GE Research – Munich (D) and Oxford University (UK).

A Cost-Benefit Approach to Evaluating Engine Health Monitoring Systems


 
 
 Condition-based maintenance (CBM) enables fleet-level decisions that increase readiness, increase time between overhaul (TBO) and reduce inspections. Since engines account for a significant portion in overall maintenance cost drivers, detection of incipient faults is an important element of the overall CBM equation. The last few years have seen significant progress in design, development and deployment of engine health monitoring. In order for such potential health monitoring solutions to be operationally viable, they must integrate with existing engine designs and maintenance processes. That is, technical factors must be balanced against economic and operational benefits.
 A Cost-Benefit Analysis (CBA) is used to provide a comparison of alternative solutions that decision-makers can use to identify the most cost-effective approach to CBM. In this paper we describe our approach to developing the underlying value capture expressions for monetizing cost and benefits. We illustrate the approach to evaluate two options for mechanical components health monitoring techniques for a gas turbine engine. We conclude the paper with how CBA summary results can be presented to a decision maker.
 
 

Application of wireless sensor networks to aircraft control and health management systems

Use of fly-by-wire technology for aircraft flight controls have resulted in an improved performance and reliability along with achieving reduction in control system weight. Implementation of full authority digital engine control has also resulted in more intelligent, reliable, light-weight aircraft engine control systems. Greater reduction in weight can be achieved by replacing the wire harness with a wireless communication network. The first step towards fly-by-wireless control systems is likely to be the introduction of wireless sensor networks (WSNs). WSNs are already finding a variety of applications for both safety-critical and nonsafety critical distributed systems. Some of the many potential benefits of using WSN for aircraft systems include weight reduction, ease of maintenance and an increased monitoring capability. This paper discusses the application of WSN for several aircraft systems such as distributed aircraft engine control, aircraft flight control, aircraft engine and structural health monitoring systems. A brief description of each system is presented along with a discussion on the technological challenges. Future research directions for application of WSN in aircraft systems are also discussed.