Engine Performance Degradation Research Articles

A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency

In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models.

A hybrid design method of steady-state throttling control schedules for high-flow variable cycle engine

It is critical for variable cycle engine to design throttling control schedules that exploit its full steady-state performance potential. However, the increasing amount of variable geometry components makes it difficult to design the optimal control schedules, and the control schedules cannot adapt to the engine performance degradation. Therefore, a novel hybrid design method of steady-state throttling control schedules is proposed in this study by combining an offline optimization phase and an online compensation phase. In the offline optimization phase, a novel multi-objective differential evolution algorithm with neighborhood-based hierarchical mutation and cluster-based environment selection is first proposed to optimize the control schedules. The stratified selection method is then proposed to select the optimal and smooth control schedules. In the online compensation phase, the sensitivity calculation is first employed to identify the variable geometry component most sensitive to engine performance, and the compensator based on the extreme gradient boosting method is then designed to adjust the control schedules online. The hybrid design method is applied to the high-flow variable cycle engine. By comparing with the traditional design method, the results demonstrate that the proposed method can optimize the engine performance under the premise of ensuring good thrust linearity and that the control schedules can adapt to performance degradation during online use.

Predictive control method for mode transition process of multi-mode turbine engine based on onboard adaptive composite model

In order to achieve precise control of thrust and flow during the mode transition(MT) process of multi-mode turbine engine(MMTE) within the switching envelope and under the condition of engine performance degradation, research on multi-mode model predictive control method is conducted. A mode transition trajectory optimization method based on SQP is explored, and a small range of adjustable parameters that ensure the continuous thrust and flow is obtained. Based on this, a multi-mode onboard modeling approach based on the directional adjustment law scheduling of the variable geometry adjustment mechanism is proposed. Furthermore, an investigation into a predictive control method for MT process based on onboard adaptive composite model is conducted. The simulation results demonstrate that the proposed method exhibits a maximum thrust fluctuation of less than 0.66 % during MT process, under both normal and degraded engine component performance conditions, which is significantly superior to the conventional PID control of 2.8 %. Compared to the average single step calculation time of 7.24 ms using the SQP optimization method, the proposed approach only requires 0.204 ms. The methodology presented takes into consideration of control accuracy and computing efficiency, thereby offering promising prospects for engineering applications in MMTE MT control system design.

Optimisation of POME biodiesel with isobutanol additive to cater UN sustainable development goal on affordable and clean energy

Biodiesel is a suitable alternative to solve global pollution and declining non-renewable resources. This aligns with the sustainable climate goals and policies to cater to SDG 7. Sustainable technology and solutions have to be fostered. The higher blends of biodiesel will result in engine performance and emissions degradation. Therefore, this research aims to improve the engine performance and emissions of diesel engines operating with B20 POME biodiesel and isobutanol. The Central Composite Design (CCD) model was used to construct the RSM model to determine the optimised condition of the engine testing. Engine performance and emissions of diesel engines were tested on the Yanmar TF120M at 50 % load. Six fuel samples, diesel, B20 Palm Oil Methyl Ester (POME) biodiesel blended fuel, and another four B20 POME biodiesel blends added with 5–20 % in volume percentage of isobutanol, were tested. The optimised RSM obtained a desirability of 0.7. The optimal conditions for the engine testing were at 1868 rpm and a 10 % isobutanol additive percentage. The brake power, BSFC, and BTE of the B95IBU5 and B90IBU10 show an improvement compared to the B20. The exhaust emission shows the lowest CO emission for B90IBU10 at 50 % load. The lowest NOx emission was obtained by B95IBU5 and B90IBU10, with an improvement of 1.8 % and 2.4 %, respectively. Therefore, from this study, it can be concluded that a lower percentage of isobutanol additive of 5–10 % is a promising additive for biodiesel blended fuels.

An adversarial transfer learning method based on domain distribution prediction for aero-engine fault diagnosis

The utilization of transfer learning enables effective realization of the transferability of aero-engine fault diagnosis models across disparate states. However, the gradual degradation of engine performance and the complex and variable flight states lead to continuous changes in data distribution. The common transfer learning methods employ discrete divisions of data domain distributions, which are insufficient to cope with the continuous changes of the domain. To accomplish transfer learning towards a continuous and multi-dimensional target domain, we propose a continuous domain distribution adversarial network (CDDAN) based on deep domain adversarial network. The method defines the number of effective cycles directly associated with engine degradation as a continuous domain index. When the domain index is extended to multiple dimensions, a hybrid gaussian model is introduced to represent distribution prediction within multi-dimensional continuous domains. Subsequently, we validate the feasibility and superiority of our method using datasets generated based on twin-spool turbofan engines. In comparison to other transfer learning methods, the proposed method achieves superior effect in continuous domain adaptation. The experimental results demonstrate that this method exhibits adaptability to failures in the diagnosis method caused by performance degradation and changes in flight state of the aero engine, while remaining independent of target domain data annotation. This advantage becomes particularly apparent in scenarios with limited target domain data and multi-state fault diagnosis.

Data-driven method embedded physical knowledge for entire lifecycle degradation monitoring in aircraft engines

The degradation of aircraft engine performance necessitates real-time monitoring. We introduce a real-time performance-degradation monitoring method comprising a baseline state (BLS) model for pristine engine performance prediction and a real-time model for ongoing engine performance prediction. Both models are LSTM-based and integrated with the engine physical topology; however, they vary in their input parameters. To counteract the data distribution disparities across various engine states during model training, a data augmentation method is proposed. In a case study, data from 600 h of engine running were employed, with thrust as the target monitoring parameter. The initial 50 h dataset covers the entire range of working conditions for the subsequent 550 h, serving as the training dataset for both models. The results demonstrated the accuracy of these models, with the BLS model achieving a mean absolute relative error (MARE) below 0.5% and a maximum absolute relative error (Emax) below 8% in the initial 50 h. Simultaneously, the real-time model yielded a MARE below 0.7% and Emax below 10% for the subsequent 550 h. A comparison of the real-time thrust degradation between this study and traditional methods highlights the feasibility and advantages of this approach.

Design and implementation for the state time-delay and input saturation compensator of gas turbine aero-engine control system

In order to obtain desired steady-state and transient performance of a gas turbine aero-engine during its operation, a usual way is to control the engine to operate in a manner close to its physical limits. The thermodynamic performance of a gas turbine aero-engine varies greatly within a wide working envelope, affected by factors such as engine performance degradation, actuator response lag and combustion time-delay. When the engine works in extreme conditions, for example rapid acceleration, some phenomena such as over-speed, over-temperature and over-pressure may occur. For this reason, various restrictions are often set in the engine control system to ensure safety during its operation, which are often realized by the form of saturation. In this paper, a compensation structure for gas turbine aero-engine state time-delay and input saturation is proposed. At first, the problems frequently occurred in the gas turbine aero-engine control system are investigated. And the gas turbine aero-engine control model is derived based on a thermodynamic non-linear model and its linearization model is established, together with its designed compensation structure. Differing from the existing technologies, a combination of dynamic system input saturation feedback and static state time-delay feedback is adopted in the proposed model. Then, the saturated nonlinearity and time-delay characteristic are introduced as convex combinations in the stability analysis. The local stability conditions of the closed-loop system are obtained by using Lyapunov–Krasovskii functionals and linear matrix inequalities (LMIs). Finally, hardware-in-loop (HIL) experiments are conducted to verify the feasibility of the proposed compensation method.

Surge Process of a High-Speed Axial–Centrifugal Compressor

The surge is a typical aerodynamic instability phenomenon in the compression system, which can lead to serious consequences such as engine performance degradation and structural damage. A deep understanding of the surge process can support the development of a compressor with a wider operating range. In this paper, an experimental study was carried out and high-responding pressure sensors were used to obtain the aerodynamic instability process and the post-surge characteristics of an axial–centrifugal compressor at design and off-design speeds. The evolution of the flow field and instability behavior before and after the surge were analyzed. The results showed that the inlet temperature change can reflect the aerodynamic instability to some extent, and as the operating condition moves from the choke to surge boundary, the inlet temperature undergoes a sudden increase at a certain condition and further increases with the decrease in mass flow rate. At the design speed, the instability of the combined compressor featured a deep surge with an obvious rotating stall behavior before its inception, and the amplitude of the stall cell was gradually enhanced, finally leading to the surge. At the off-design speed, affected by the stage mismatching, the axial stage mainly worked near the unstable operating condition. Therefore, the compressor successively experiences two modes of mild surge and deep surge, and the rotating stall can also be observed during the surge cycle.

Performance evaluation and optimization for a novel supersonic precooled engine based on hydrogen production technology from ammonia cracking

Precooling technology is a widely used method for increasing the flight speed of turbojet engines. To solve the problems of oversized fuel tank and engine performance degradation caused by the low density of hydrogen fuel, a new precooled engine cycle based on hydrogen production technology from ammonia cracking is proposed. To evaluate the engine performances, a thermodynamic model and a test platform are established. The study shows that the test heat sink of ammonia is 4.12 MJ at 620.5 °C, and the upper boundaries of heat sink and hydrogen production rate of ammonia are higher than other chemical fuels. Besides, the optimized fuel ratio is set to 2.0, and the best compressor pressure ratio could be obtained at the point with the highest engine performances Further, the maximum flight Mach number of precooled engine is Ma4.69, and compared with GE90 and J-58 engines, the carbon emission of precooled engine could be reduced by 94.15% and 72.95% respectively. Moreover, by increasing the cold storage degree, the engine performance can be further improved especially at high pressure ratio. Overall, the research of this paper is hopeful to provide a new idea and necessary theoretical support for the development of high-performance supersonic aero-engine.

Novel approach for efficient operation and reduced harmful emissions on a dual-fuel research engine propelled with hydrogen-enriched natural gas and diesel

ABSTRACT The stricter emission norms have created a need to study recent combustion technology in internal combustion engines. Dual-fuel (DF) combustion technology is one of the key techniques to achieve future emission goals. Natural gas is one of the easily available low-reactive fuels worldwide that can be used in the DF combustion strategy. The main problem with using NG in the DF strategy is its lean-burning ability to produce emissions like HC and CO. The combustion losses associated due to crevice losses in the piston geometry are also the reasons for getting degraded engine performance. In the present study, the lean flammability of NG was improved by enriching it with hydrogen. The combustion losses were reduced using modified disc piston geometry with lower crevice volume. The results showed that the thermal efficiency obtained using HENG/diesel DF strategy with disc piston (DF disc) was only less than 0.3%, 0.4%, and 1% for low, mid, and high load conditions compared to conventional compression ignition mode using bowl piston (CI bowl). The change in piston geometry from bowl to disc type using the DF strategy improved the thermal efficiency by about 3.8%, 3.3%, and 2.7% for three load conditions. The NOx and smoke opacity reduction by about 6% to 19% and 40% to 60% in DF disc mode was achieved compared to CI bowl mode.

Engine remaining useful life prediction model based on R-Vine copula with multi-sensor data

Aeroengine is a highly complex and precise mechanical system. As the heart of an aircraft, it has a crucial impact on the overall life of the aircraft. Engine degradation process is caused by multiple factors, so multi-sensor signals are used for condition monitoring and prognostics of engine performance degradation. Compared with the single sensor signal, the multi-sensor signals can more comprehensively contain the degradation information of the engine and achieve higher prediction accuracy of the remaining useful life (RUL). Therefore, a new method for predicting the RUL of an engine based on R-Vine Copula under multi-sensor data is proposed. Firstly, aiming at the phenomenon that the engine performance parameters change over time, and the performance degradation presents nonlinear characteristics, the nonlinear Wiener process is used to model the degradation process of a single degradation signal. Secondly, the model parameters are estimated in the offline stage to integrate the historical data to obtain the offline parameters of the model. In the online stage, when the real-time data is obtained, the Bayesian method is used to update the model parameters. Then, the R-Vine Copula is used to model the correlation between multi-sensor degradation signals to realize online prediction of the remaining useful life of the engine. Finally, the C-MAPSS dataset is selected to verify the effectiveness of the proposed method. The experimental results show that the proposed method can effectively improve prediction accuracy.

Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications

The engine fuel control system plays a crucial role in engine performance and fuel economy. Fuel control, in traditional engine control systems, is carried out by means of sensor-based control methods, which correct the fuel flow rate through correlations or scheduled parameters in order to reduce the error between a measured parameter and its desired value. In the presence of component degradation, however, the relationship between the engine measurable parameters and performance may lead to an increase in the control error. In this research, linear models for advanced control systems and for direct fuel control in the presence of components degradation are proposed, with the main objective being to directly predict and correct fuel consumption in the presence of degradation instead of adopting measurable parameters. Two techniques were adopted for model linearization: Small Perturbation and System Identification. Results showed that both models are characterized by high accuracy in predicting the output engine variables, with the mean errors between model prediction and data below 1%. The maximum errors, recorded for shaft power, were about 6% for Small Perturbation and lower than 3% for System Identification. A simple correlation between engine performance and components degradation was also demonstrated; in particular, the achieved results allow one to conclude that the Small Perturbation approach is the best candidate for controller development when a prediction of components degradation is included.

Analysis of satellite-derived data for the study of fouling in aircraft engines

Atmospheric particulate is one of the main causes of performance degradation in gas turbine engines, especially in the aeronautical field where filter barriers are absent. The ingested particles can stick to the blade surfaces of the engine, varying their shape and roughness. As a consequence, engine performance degradation takes place. The type and the amount of the particles ingested depend on the flight zones and altitude. During their missions, aircrafts follow a prescribed path defined in terms of altitude, longitude, and latitude. During its route, the aircraft engine encounters different environments characterized by different temperature, pressure, and air composition. Regarding the latter issue, the knowledge of this characteristic can be key information when these statistics are needed for obtaining data useful for engine degradation assessment or prediction. Many satellites, such as the environmental satellite CALIPSO, are employed to study the terrestrial aerosol and clouds profile by using a LIDAR (Laser Detection and Ranging). This technology is commonly used to determine the distance between a light emitter and an object and it is based on the light refraction phenomenon. Backscatter coefficients profiles data, which characterize the distribution of particles and aerosols in the atmosphere, are available in the open literature from the findings of CALIPSO. In this work, a new methodology to estimate the aerosol type and concentration encountered by an aircraft during a mission is proposed. To test the feasibility of this method, two aircraft missions for different length scales (medium and long haul) are analyzed and an estimate of the particulate encountered by the engines is provided. The mission analysis has been conducted by discretizing the altitude profile, longitude, and latitude coordinates of each flight and then cross-referencing them with the particulate concentration obtained from CALIPSO data.

Acoustic Thrust Estimation on Turbofan Engines

An acoustic time of flight sensor is operated across a turbofan exhaust plume and is calibrated to provide nozzle thrust in a ground-test cell. Such a sensor could potentially be used to monitor any degradation of engine performance in an on-wing application. Validation of the technique was performed on an AE3007-A1E turbofan engine. Using a pneumatic horn and a set of microphones, the acoustic time of flight across the exhaust plume was estimated. A correlation-based approach was then used to relate the acoustic time of flight to the thrust produced by the engine. The thrust estimates resulting from this single-receiver approach matched load cell data within a root-mean-square (RMS) error of 1.1% full scale. The same approach was also used to estimate the thrust of a TFE731-2B engine. In that case, the estimated thrust matched load cell data to within an RMS error of 1.5% full scale. The technique is shown to significantly improve on a previous technique that produced errors of 5% RMS full scale or greater. Possible explanations for the comparative inaccuracy of the previous technique are analyzed.

The effect of high altitude environment on diesel engine performance: Comparison of engine operations in Hangzhou, Kunming and Lhasa cities

The all-area operating performance of the vehicles requires the development of diesel engines that can operate at high altitudes without significant performance deterioration. Prior to optimizing the efficiency and emissions of highland engines, there is a necessity to investigate the underlying causes of engine performance degradation. The purpose of this paper was to study the in-cylinder activities occurring in the combustion chamber of diesel engines at high altitudes, which can help explain the effect of altitude on engine efficiency and emissions of concern to the customer. Specifically, a turbocharged direct injection compression ignition engine was operated at a constant engine speed and load, but at different altitudes. The theoretical analyses based on experimental data suggested that the mismatch between air and diesel quantities caused by the high altitude atmosphere led to the engine combustion deterioration. Specifically, the lower gas density at high altitudes during fuel injection resulted in a reduction of the injection angle and an enhancement of the penetration capability. In addition, the rise in altitude extended the ignition delay, which increased the fuel fraction mixed with air in the premixed combustion stage and raised the pressure rise rate. Moreover, at high altitudes, the reduction in excess air ratio and increased possibility of wall impingement of the fuel droplets resulted in uneven mixture concentration distribution and reduced air utility. Accordingly, combustion deterioration occurred in the combustion chamber of the plateau engines, which reduced energy release during main mixing-controlled combustion, lowered combustion efficiency, increased exhaust energy, and raised engine-out incomplete combustion emissions. All these effects resulted in a decrease in engine thermal efficiency of ∼6.5% and an increase in soot emissions of ∼4.2 times from Hangzhou to Lhasa city for the engine and operating conditions investigated in this study. Consequently, engines operating in highland areas need to be optimized in terms of efficiency and emissions.

Thermodynamic analysis for a novel chemical precooling turbojet engine based on a multi-stage precooling-compression cycle

Precooling technology is an effective way to improve the flight Mach number for turbojet engine. To solve the problem of engine performance degradation caused by excessive fuel consumption, a multi-stage precooling-compression cycle is newly proposed, in which the secondary cooling capacity of fuel is reused by transfer the heat energy from fuel to the mechanical work. To evaluate the engine performance, several new performance parameters are defined and a thermodynamic model is established, in which the optimum distribution of pressure ratio for each stage of turbine/compressor and the performance limit of engine are derived in theory. By calculation, it is found that the heat absorption capacity of fuel and the engine performance could be improved with the increase of stage number. Further, the lower and upper performance boundaries of engine could be obtained when the stage number is set to one and infinity respectively. Moreover, considering the engineering application, the two-stage precooling-compression cycle will be regarded as the best design, and when the engine performance is improved by about 8.15%, the heat transfer area of pre-cooler will be increased by 88.19%, as well as the mass of compressor and turbine will be increased by 30.33% and 11.18% respectively.

Monitoring the Performance of a Ship’s Main Engine Based on Big Data Technology

Abstract Under the recent background of ‘Green Shipping’ and rising fuel prices, it is very important to reduce the fuel consumption rate of ships, which is directly affected by the performance of the main engine. A reasonable maintenance schedule can optimise the performance of the main engine. However, a traditional maintenance schedule is based on the navigation distance and time, ignoring many other factors, such as a harsh working environments and frequently changing operating conditions, which will lead to faster performance degradation. In this study, a real-time evaluation method combing big data of ship energy efficiency with physics-based analysis is proposed to judge the degradation of main engine performance and assist in determining the maintenance schedule. Firstly, based on the developed ship energy efficiency big data platform, the distribution statistics and comparison of different operating states are carried out. Gaussian mixture model (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to cluster the data and the high-density data areas are obtained as the analysis points. Then, the data of the analysis points are polynomial fitted, by the least square method, to obtain the propulsion characteristics curves, load characteristic curves, and speed characteristic curves, which can be used to observe the performance degradation of the main engine. The results show that this method can effectively monitor the degradation degree of the main engine performance, and is of great significance to fuel efficiency improvements and greenhouse gas (GHG) emissions reduction.

A phenomenological study and comparison of the characteristics of droplet impact liquid film dynamics on randomly rough surfaces

The fouling of aero-engine blades is the main cause of degradation of engine performance and online washing is one of the most effective methods for restoring engine performance. The flow characteristics of the washing fluid after it impinges on the blade surface are critical to the process. The liquid film flow becomes complicated after being impacted by a droplet, because the fouling blade is a random rough surface. The purpose of this study is to evaluate the dynamical characteristics of droplets after they impact the liquid film, focusing on the diameter, the height of the coronal water bloom, and the near-wall flow. We establish a random rough surface to simulate the droplet impacting the liquid film on the fouling surface and analyze the morphological evolution of the corona during the droplet impact process. The results show that an increase in the particle size has a greater impact on the coronal diameter than the coronal height. In addition, a higher droplet impact velocity and thicker liquid film are conducive to the secondary atomization of droplets and improve the transport rate of the cleaning solution. However, the flowability of the liquid film at the impact point is best when the droplet impacts the thin liquid film. Increasing the thickness of the liquid film gradually helps to improve its overall fluidity and results in a better cleaning effect.

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

A low bypass ratio turbofan engine operates in a hostile environment, resulting in performance degradation. This seriously affects the security and reliability of the engine. Therefore, a performance degradation evaluation method for engines based on flight data is proposed. The method expands the equation system to solve the underdetermined problem caused by the lack of engine sensors based on multiple operating point analysis. The improved evolution algorithm is employed to solve the equation system, which relieves the problem of insufficient precision. The engine performance degradation dataset is established based on the engine performance calculation model to verify the reliability of the degradation evaluation method. The results show that the method is applicable to the dataset. Finally, the method is applied to the actual flight data to study the law of the performance degradation of the researched engine, which indicates that the engine’s fan efficiency and high-pressure compressor flow capacity have an apparent downward trend over time.

Numerical study of effects of solid particle erosion on compressor and engine performance

Solid particle erosion (SPE) is a major source of damage to, and cause of failure of, turbomachinery, including centrifugal and axial compressors and gas turbines. There is considerable interest within the turbomachinery industry to develop the means to enhance durability and improve prediction of likely breakdowns of machinery operating in locations and conditions where ingestion of, for example, dust and sand grains is unavoidable during operation. The present study examines the implications of degradation of the compressor on gas turbine cycle performance, caused by operating in eroding environments, linking compressor aerodynamics with the thermodynamic cycle. The research engine employed in the study comprised a fan, bypass, and two stages of the low-pressure compressor (booster). A numerical simulation using Computational Fluid Dynamics (CFD) software was performed to predict the likely erosion patterns due to solid particles (SPs) impacting on the blades of the first two stages of an axial compressor. This study introduces a method that provides the freedom to apply roughness to a particular region of the blade following the SPE patterns identified as part of the CFD simulations so as to represent the effects of the erosion spots on the overall flow field. The localised roughness method employs a perturbation of the geometry of the blade in the regions corresponding to the location predicted by the SPE model. The intensity of the perturbation, which is governed by a “fraction factor” coded in a Matlab script, was calibrated by reference to a roughness case, applied over the entire blade surface, with equivalent sand-grain roughness (ks) of 60 μm. Turbomatch, the Cranfield in-house gas turbine performance simulation software, was employed to model the degradation of engine performance. The research confirmed that SPE is linked with significant decreases in engine performance parameters such as isentropic efficiency (ηis) and pressure ratio (PR). These quantities showed, for the SPE cases considered, a drop of 8.8% and 5.2%, respectively. These findings are useful to link local SPE events to global GTE cycle performance.