Engine Health Management Research Articles

Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

Design principles and digital control of advanced distributed propulsion systems

The integration of advanced distributed propulsion (DP) systems within various aircraft configurations holds the potential to greatly increase aircraft performance, particularly in terms of fuel efficiency, reduction of harmful emissions and reduction of take-off field length requirements. This has been enabled by modern analysis tools, materials technology and control systems which take advantage of the positive interactions between the propulsive configuration and the aerodynamics of the aircraft. In particular, synergies are maximized when the propulsion system uses distributed nozzles, crossflow fans and multiple distributed fans. Recent advances in electric propulsion have encouraged the hybridization of propulsive systems, with airliners having multiple electric fans powered by one or two gas turbine engines. Furthermore, due to recent advances in airframe integration solutions, the propulsive element can become an integral part of the control and stability augmentation capabilities of the aircraft. Thereby, the digital control of advanced DP systems is crucial for the purpose of thrust modulation and intelligent management of engine resources and health. This not only aids in mission optimization but also supports the case for airworthiness certification of novel aircraft configurations integrating advanced DP systems. This paper presents a critical review of the existing literature on the subject of DP, identifying the benefits and drawbacks of this technology as well as its proposed practical applications. The review also discusses contemporary advances in hybrid electric technology, aeroelasticity research and the fundamental design steps to integrate advanced DP systems in fixed-wing aircraft. Additionally, an evolutionary outlook is presented on digital control of DP systems with a focus on advancing the techniques for mission optimization and engine health management for enhanced safety and sustainability. Based on the proposed design principles and digital control methodology, conclusions are drawn about the suitability of advanced DP for various applications and recommendations are made for future research and development.

Aero-Engine Real-Time Models and Their Applications

Aero-engine real-time models are widely used in control system design, integration, and testing. They can be used as the basis for model-based engine intelligent controls and health management, which is critical to improve engine safety, reliability, economy, and other performance indicators. This article provides an up-to-date review on aero-engine real-time modeling methods, model adaptation techniques, and applications for the last several decades. Besides, future research directions are also discussed, mainly focusing on the following four areas:(1) verification of the aero-engine real-time model over the full flight envelope; (2) better balance between real-time performance and accuracy in simplified methods for the aero-thermodynamic component level models; (3) further improvement in the real-time performance for the identified nonlinear models over the full flight envelope; (4) improvement of hybrid on-board adaptive real-time models combining the advantages of both model-based and data-based on-board adaptive real-time modeling methods.

A data-driven approach using machine learning for early detection of the lean blowout

A data-driven approach using machine learning is presented for the identification of the critical flame location for the early detection of an incipient lean blowout (LBO) in a realistic gas turbine engine combustor under engine-relevant conditions. This method is demonstrated by utilizing the temperature (T) and the hydroxyl radical mass fraction (YOH) data from high fidelity large eddy simulations (LES) of Jet-A combustion. The fuel flow rate is progressively reduced in numerical simulations with a fixed airflow rate to mimic experimental studies of LBO in the gas turbine combustor. These simulations are the first of their kind for a fully resolved realistic combustor geometry with adaptive mesh refinement and have accurately captured the dynamics of the LBO process and global lean blowout equivalence ratio. Time-series of T and YOH are extracted in the primary zone of the combustor, from stable flame condition to LBO condition, to train the machine learning model. A Support Vector Machine (SVM) model with radial basis function is successfully developed to identify the critical flame location for early detection of incipient LBO condition in a practical combustor for the first time. The performance of the SVM model is quantified using the F-score, and the critical flame location corresponds to the maximum value of the F-score. The critical flame location is found to be in the flame root region and is effective in the early detection of incipient LBO. The conventional statistical measures are compared with the results of the trained machine learning model to assess the feasibility of the latter for online flame health monitoring. The machine learning model successfully prognosticated the LBO approximately 20 ms before the event, and this study has shown significant promise for the use of the SVM model in engine prognostics and health management.

Online monitoring experiments of turbo-shaft engine based on electrostatic sensor

PurposeElectrostatic monitoring technology is a useful tool for monitoring and detecting component faults and degradation, which is necessary for engine health management. This paper aims to carry out online monitoring experiments of turbo-shaft engine to contribute to the practical application of electrostatic sensor in aero-engine.Design/methodology/approachCombined with the time and frequency domain methods of signal processing, the authors analyze the electrostatic signal from the short timescale and the long timescale.FindingsThe short timescale analysis verifies that electrostatic sensor is sensitive to the additional increased charged particles caused by abnormal conditions, which makes this technology to monitor typical failures in aero-engine gas path. The long scale analysis verifies the electrostatic sensor has the ability to monitor the degradation of the engine gas path performance, and water washing has a great impact on the electrostatic signal. The spectrum of the electrostatic signal contains not only the motion information of the charged particles but also the rotating speed information of the free turbine.Practical implicationsThe findings in this article prove the effectiveness of electrostatic monitoring and contribute to the application of this technology to aero-engine.Originality/valueThe research in this paper would be the foundation to achieve the application of the technology in aero-engine.

Design Technology Research of Aircraft Engine Health Management (EHM) Technologies

Aircraft engine is an important guarantee for aircraft safety, and its failure mode and health management have become the top priority. However, there are very few researches on aircraft engine health management. This article mainly summarizes the current research status of aircraft engine health management (EHM) from the aspect of aircraft electronic system, focuses on the overall structure, functional areas and key technologies of EHM system design, points out the design requirements of EHM system, and finally proposes EHM system. The design must improve the monitoring accuracy of the sensor to meet the monitoring requirements of more than 0.1%. High-precision monitoring data is more conducive to engine fault detection and processing, and EHM will therefore develop in the direction of real-time, intelligent, integrated and networked.

A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management

Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems.

An Efficient Approach for Identification of the Inlet Distortion of Engine Based on Acoustic Emission Technique

Effective and accurate diagnosis of engine health is key to ensuring the safe operation of engines. Inlet distortion is due to the flow or the pressure variations. In the paper, an acoustic emission (AE) online monitoring technique, which has a faster response time compared with the ordinary vibration monitoring technique, is used to study the inlet distortion of an engine. The results show that with the deterioration of the inlet distortion, the characteristic parameters of AE signals clearly evolve in three stages. Stage I: when the inlet distortion J ≤ 30%, the characteristic parameters of the AE signal increase as J increases and the amplitude saturates at J = 23%, faster than the other three parameters (the strength, the root mean square (RMS), and the average signal level (ASL)). Stage II: when the inlet distortion 30% < J ≤ 43.64%, all the parameters saturate with only slight fluctuations as J increases and the engine works in an unstable statue. Stage III: when the inlet distortion J > 43.64%, the engine is prone to surge. Furthermore, an intelligent recognition method of the engine inlet distortion based on a unit parameter entropy and the back propagation (BP) neural network is constructed. The recognition accuracy is as high as 97.5%, and this method provides a new approach for engine health management.

Remaining Useful Life Prediction of Airplane Engine Based on PCA–BLSTM

The accurate prediction of airplane engine failure can provide a reasonable decision basis for airplane engine maintenance, effectively reducing maintenance costs and reducing the incidence of failure. According to the characteristics of the monitoring data of airplane engine sensors, this work proposed a remaining useful life (RUL) prediction model based on principal component analysis and bidirectional long short-term memory. Principal component analysis is used for feature extraction to remove useless information and noise. After this, bidirectional long short-term memory is used to learn the relationship between the state monitoring data and remaining useful life. This work includes data preprocessing, the construction of a hybrid model, the use of the NASA’s Commercial Aerodynamic System Simulation (C-MAPSS) data set for training and testing, and the comparison of results with those of support vector regression, long short-term memory and bidirectional long short-term memory models. The hybrid model shows better prediction accuracy and performance, which can provide a basis for formulating a reasonable airplane engine health management plan.

Topical Issues in Aircraft Health Management with Applications to Jet Engines

Aircraft Health Management Technology for jet engines represents a very important problem, since it develops a large impact on reducing the engine life cycle costs, improving the fuel efficiency, increasing the engines durability and life cycle. This technology is high-end and, in order to enable an improved level of performance that far exceeds the current one, propulsion systems must comply with terms of reducing harmful emissions, maximizing fuel efficiency and minimizing noise, while improving system’s affordability and safety. Aircraft Health Management Technology includes multiple goals of aircraft propulsion control, diagnostics problems, prognostics realized, and their proper integration in control systems. Modern control for Aircraft Health Management Technology is based on improved control techniques and therefore provides improved aircraft propulsion system performances. The study presented in this paper approaches a new concept, of attractive interest currently, that is the intelligent control; in this context, the Health Management of jet engines is crucial, being focused on engine controllers which are designed to match certain operability and performance constraints. Automated Engine Health Management has the capacity to significantly reduce the maintenance effort and propulsion systems’ logistical footprint. In order to prioritize and resolve problems in the field of support engineering there are required more detailed data on equipment reliability and failures detection and management; the equipment design, operations and maintenance procedures and tooling are also very important.

Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future

On-board real time modelling for gas turbine aero-engines has been extensively used for engine performance improvement and reliability. This has been achieved by the utilization of on-board model for the engine's control and health management. This paper offers a historical review of on-board modelling applied on gas turbine engines and it also establishes its limitations, and consequently the challenges, which should be addressed to apply the on-board real time model to new and the next generation gas turbine aero-engines. For both applications, i.e. engine control and health management, claims and limitations are analysed via numerical simulation and publicly available data. Regarding the former, the methods for modelling clean and degraded engines are comprehensively covered. For the latter, the techniques for the component performance tracking and sensor/actuator diagnosis are critically reviewed. As an outcome of this systematic examination, two remaining research challenges have been identified: firstly, the requirement of a high-fidelity on-board modelling over the engine life cycle, especially for safety-critical control parameters during rapid transients; secondly, the dependability and reliability of on-board model, which is critical for the engine protection in case of on-board model failure. Multiple model-based on-board modelling and runtime assurance are proposed as potential solutions for the identified challenges and their potential and effectiveness are discussed in detail.

Review of advanced low-emission technologies for sustainable aviation

Turbofan engines are the most widely used propulsion technology in commercial transport aircraft and are directly involved in many of the environmental impacts of aviation. Advancements in turbofan technology have thus a very significant potential in reducing aviation impacts on the environment. This article reviews the main technological advances currently being pursued in low-emission aircraft propulsion including: combustion and thermofluidic enhancements, gearbox technology, lightweight materials and intelligent engine health management systems. The paper discusses some key opportunities and challenges for these new technologies, with a particular outlook on the historical emission trends. Particularly, historical records from the ICAO aircraft engine emissions databank are used to extrapolate current trends and the progress against the ambitious targets set by international bodies. The analysis highlights that the sustained investments made by the aviation industry have yielded progressively diminishing returns and that the emission objectives will not be achieved at the current pace. Disruptive technological advances will therefore be required to significantly improve the fuel efficiency and mitigate environmental impact of commercial transport aircraft in the future.

Identification-based aeroengine nonlinear modeling: a comparative study

Aeroengine modeling plays an important role in the development of advanced controls and other intelligent functions such as engine health management etc. This is particularly true for those on-board applications, where identification-based modeling approaches are often utilized. While linear modeling methodologies have been extensively introduced in the literature, identification-based nonlinear modeling should be investigated, due to the inherent nonlinear nature of an aeroengine’s dynamical properties. Three approaches, i.e., back propagation (BP) neural networks (BPNN), improved BP neural networks (IBPNN), and a piecewise nonlinear autoregression with extra inputs (PNARX) model, are presented. A comparative study is given for systematic development and illustration of nonlinear modeling techniques. Some practical considerations are also given for guidance of implementing the identification-based nonlinear modeling methodologies.

F-16 turbofan engine monitoring system

The multirole F-16 is the most advanced aircraft in the Polish Air Forces. It has been equipped with the very modern, sophisticated and advanced turbofan engine F100-PW-229. Due to the fact, that there is only one engine, its reliability, durability, efficiency and performance are the crucial factors for the safety reasons. In the article author researched maintenance system of the F100 turbofan engines, to describe Engine Monitoring System features. Engine Monitoring System (EMS) is the key element in the engine prognostic and health monitoring. The EMS provides engine fault indicators to the pilots and technicians and with the engine performance trending affects the F-16 flight safety risk and enhanced engine maintenance management concept. The main goal of this article was to provide information on the F-16 Engine Monitoring System and its impact on the aircraft airworthiness and F-16 fleet readiness resulting from the engine reliability. It is also an introduction to the F-16 Engine Health Management concept.

Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine

Aero-engine gas path health monitoring plays a critical role in Engine Health Management (EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine (HPT) exit pressure, which is vital to distinguishing failure modes between different turbines. For the case of an abrupt failure occurring in a single turbine component, a model-based sensor measurement reconstruction method is proposed in this paper. First, to estimate the missing measurements, the forward algorithm and the backward algorithm are developed based on corresponding component models according to the failure hypotheses. Then, a new fault diagnosis logic is designed and the traditional nonlinear filter is improved by adding the measurement estimation module and the health parameter correction module, which uses the reconstructed measurement to complete the health parameters estimation. Simulation results show that the proposed method can well restore the desired measurement and the estimated measurement can be used in the turbofan engine gas path diagnosis. Compared with the diagnosis under the condition of missing sensors, this method can distinguish between different failure modes, quantify the variations of health parameters, and achieve good performance at multiple operating points in the flight envelope.

Research Progress of Engine Health Management

Research Progress of Engine Health Management

Intelligent Reasoning for Gas Turbine Fault Isolation and Ambiguity Resolution

An engine health management (EHM) system typically consists of automated logic for data acquisition, parameter calculation, anomaly detection and eventually, fault identification (or isolation). Accurate fault isolation is pivotal to timely and cost-effective maintenance but is often challenging due to limited fault symptom observability and the intricacy of reasoning with heterogeneous parameters. Traditional fault isolation methods often utilize a single fault isolator (SFI) that primarily relies on gas path performance parameters. While effective for many performance-related faults, such approaches often suffer from ambiguity when two or more faults have signatures that are very similar when monitored by a rather limited number of gas path sensors. In these cases, the ambiguity often has to be resolved by experienced analysts using additional information that takes many different forms, such as various nongas path symptoms, full authority digital engine control fault codes, comparisons with the companion engine, maintenance records, and quite often, the analyst's gas turbine domain knowledge. This paper introduces an intelligent reasoner that combines the strength of an optimal, physics-based SFI and a fuzzy expert system that mimics the analytical process of human experts for ambiguity resolution. A prototype diagnostic reasoner software has been developed and evaluated using existing flight data. Significant performance improvements were observed as compared with traditional SFI results. As a generic reasoning framework, this approach can be applied not only to traditional snapshot data, but to full flight data analytics as well.

Remaining Useful Life Prediction of Turbofan Engine Using Hybrid Model Based on Autoencoder and Bidirectional Long Short-Term Memory

Turbofan engine is a critical aircraft component with complex structure and high-reliability requirements. Effectively predicting the remaining useful life (RUL) of turbofan engines has essential significance for developing maintenance strategies and reducing maintenance costs. Considering the characteristics of large sample size and high dimension of monitoring data, a hybrid health condition prediction model integrating the advantages of autoencoder and bidirectional long short-term memory (BLSTM) is proposed to improve the prediction accuracy of RUL. Autoencoder is used as a feature extractor to compress condition monitoring data. BLSTM is designed to capture the bidirectional long-range dependencies of features. A hybrid deep learning prediction model of RUL is constructed. This model has been tested on a benchmark dataset. The results demonstrate that this autoencoder-BLSTM hybrid model has a better prediction accuracy than the existing methods, such as multi-layer perceptron (MLP), support vector regression (SVR), convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model can provide strong support for the health management and maintenance strategy development of turbofan engines.

Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning

Degradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostic approach to modeling an exponential degradation process due to wear as well as predicting the RUL of aircraft engines. The ensemble learning algorithm combines multiple base learners, including random forests (RFs), classification and regression tree (CART), recurrent neural networks (RNN), autoregressive (AR) model, adaptive network-based fuzzy inference system (ANFIS), relevance vector machine (RVM), and elastic net (EN), to achieve better predictive performance. The particle swarm optimization (PSO) and sequential quadratic optimization (SQP) methods are used to determine optimum weights that are assigned to the base learners. The predictive model trained by the ensemble learning algorithm is demonstrated on the data generated by the commercial modular aero-propulsion system simulation (C-MAPSS) tool. Experimental results have shown that the ensemble learning algorithm predicts the RUL of the aircraft engines with considerable robustness as well as outperforms other prognostic methods reported in the literature.

Wireless and autonomous sensor for Integrated Engine Health Management

Providing the best availability of aircrafts is a main cause for concern in aeronautics industry. Integrated Engine Health Management (IEHM) is the up to date solution that is used by Aircraft Engine Manufacturers in order to maintain a fleet of engines operative. IEHM detect signs of failure before they happen is the key tool for improving the level of aircraft operability. There is a strong focus on the sensor part and particularly on a wireless autonomous load sensor. Measuring load can have a high value to estimate the remaining useful life of equipment’s like suspension rods or gears after a hard landing. Indeed, after such an event, aircraft companies need to know if suspension rods/gears are safe or could have been damaged. This paper presents the current CORALIE sensor development (autonomous wireless load recorder) that will enable maintenance operators to determine the load level seen by an engine link after a hard landing by simply setting a portable device near the mounting system. Interesting features are Energy Harvesting and RFID communication modules that have been developed for this sensor. Such autonomous SHM systems typically include embedded sensors, and elements for data acquisition, wireless communication, and energy harvesting. Among all of these components, this paper focuses on RFID technologies. Actually, low power sensors and wireless communication components are used in newer SHM systems, and a number of researchers have recently investigated such techniques to extract wirelessly communicate data stored in these stand-alone systems. The first part of the paper is dedicated to benchmark the different wireless communication protocols against RFID available for the project application (engine pods). The second part gives a presentation of the RFID communication system (Antenna, reader and RFID front end circuit) developed for transmitting local data stored in the memory of the system for analysis in a SHM maintenance strategy. The last part presents real test of RFID wireless communication and provide a maturity assessment on the technology.