Search results

This paper aims to discuss engine health monitoring for unmanned aerial vehicles. It is intended to make consistent predictions about the future status of the engine performance parameters by using their current states.

The aim is to minimize risks before they turn into problems. In accordance with these objectives, temporal and financial savings are planned to be achieved by contributing processes such as extending the engine life, preventing early disassembly-reassembly and mechanical wears and reducing the maintenance costs. Based on this point of view, a data-based software is developed in MATLAB (Matrix Laboratory) program for the so-called process.

The software is operated for the performance parameters of the turbojet engine that is used in a small unmanned aerial vehicle of Tusas Engine Industry. The obtained results are compared with the real data of the engine. As a result of this comparison, a fault that may occur in the engine can be detected before being determined.

It is clearly demonstrated that the engine operation in adverse conditions can be prevented. This situation means that the software developed operates successfully.

Electrostatic 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.

Combined with the time and frequency domain methods of signal processing, the authors analyze the electrostatic signal from the short timescale and the long timescale.

The 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.

The findings in this article prove the effectiveness of electrostatic monitoring and contribute to the application of this technology to aero-engine.

The research in this paper would be the foundation to achieve the application of the technology in aero-engine.

The aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.

In this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.

The proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.

The proposed method is one of the key technologies of AEMT.

The purpose of this paper is to provide a relatively straightforward approach of implementing standard statistical process control (SPC) concepts while instituting problem‐solving intonations in aero‐engine maintenance processes.

The inspection workflow approach is presented in order to aid in collecting and monitoring critical aero‐engine data. Observed defects are categorized according to a Pareto analysis assisted by a cause‐and‐effect diagram. A binomial process capability analysis is performed on nonconforming aero‐engines based on operating curves produced specifically for this case study. The time frame for experimental analysis is reflected in a span of six months.

It is found that a significant number of aero‐engines may be benefited by entering a more progressive maintenance program relying on predictive maintenance on the way to establishing a more effective Total Productive Maintenance scheme.

The case study showcases an approach to aero‐engine rejection statistical rates by accepting the fact that maintenance process may not be viewed as a process that may be limited to constant sampling.

For a long time, total quality management (TQM) tools have been deeply rooted in design, manufacturing and assembling of airliners and jet fighters alike. However, a comprehensive study focusing on the maintenance function of such complex machines may prove worthwhile now that an unstable global economy may prohibit extensive replacement of aging flying fleets.

With the lack of a prior practical unfolding in the field of genuine aero‐engine maintenance, this presentation aims to fill in the gap for engine rejection treatment. The variant operating curve notion introduced in the text is also a unique idea espoused for variable sampling situations when a binomial distribution is adopted.

This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.

The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.

The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.

To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.

Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.

MODERN aircraft engines incorporate numerous sensors for measuring their operating parameters, both in the immediate sense for electronic control of performance and, in the longer term, to monitor engine health. The same or separate sensors may serve these two functions. Many of the control sensors additionally supply signals to cockpit indicators which inform the pilot of the engine condition when adjusting the controls to call for more or less engine power. The sensors are mounted on or inside the engine and consequently experience very severe environmental conditions. The apparent simplicity of some of these sensors belies the considerable expertise that is required to design, manufacture and test them to meet such demanding operating conditions in accordance with the high specification standards set by the aero engine and aviation industries.

EC-135P2+ helicopters operated by Polish Medical Air Rescue are highly exposed to environmental particles entering engines when performing helicopter emergency medical services. This paper aims to assess the effectiveness of inlet barrier filters installed to protect the engines, including their impact on maintenance.

The organisation adopted a comprehensive set of measures to predict and limit the impact of dust ingestion including visual inspections, health management and engine trend monitoring based on ground power checks’ (GPC) results. Three alternative particle separation solutions were considered. Finally, helicopter inlets were modified to allow the selected filter system to be installed, which reduced the number of particles ingested by the engine and prevented from premature overhauls.

The analyses carried out enabled not only the selection of the optimal filtration solution and its seamless implementation into the fleet but also confirmed its efficiency. After installing the filters, engines’ lifetime is extended from 500 to 4,500 flight hours while operating costs and the number of maintenance tasks was reduced significantly.

Lessons learned from operational experience show that a well-matched particle separation system can mitigate accelerated engine deterioration even if the platform is continuously exposed to environmental particles. The remaining useful life of engines can be predicted using performance models and data from GPC.

Looks at an aircraft engine manufacturer’s use of an electronic system to enhance an existing product. During the early 1990s Rolls‐Royce studied the changing military market and compared its range of engines with potential business opportunities. The trainer/light combat powerplant was one area where there was potential to develop its position. As a result of the continuing success of the BAe Hawk and Boeing Goshawk aircraft and the expectation of new sales well into the next century, the decision was taken to launch a new version of the Adour engine. Thus in 1996 Rolls‐Royce launched the Adour 900, based on the current, well‐proven Mark 871, but offering thrust growth, improved life cycle costs and enhanced functionality. Central to the concept of the Adour 900 is a new Full Authority Digital Engine Controller with integrated Engine Health Monitoring. Examines the features of the FADEC and EHM System, showing how they have been tailored to meet the needs of a modern trainer aircraft without sacrificing retrofit capability. Attention is also given to the development process which had to ensure the demanding design targets were met within the cost and timescale constraints.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

The purpose of this paper is to propose a comprehensive framework for integrating big data analytics (BDA) into cyber-physical system (CPS) solutions. This framework provides a wide range of functions, including data collection, smart data preprocessing, smart data mining and smart data visualization.

The architecture of CPS was designed with cyber layer, physical layer and communication layer from the perspective of big data processing. The BDA model was integrated into a CPS that enables managers to make sound decisions.

The effectiveness of the proposed BDA model has been demonstrated by two practical cases − the prediction of energy output of the power grid and the estimate of the remaining useful life of the aero-engine. The method can be used to control the power supply system and help engineers to maintain or replace the aero-engine to maintain the safety of the aircraft.

The communication layer, which connects the cyber layer and physical layer, was designed in CPS. From the communication layer, the redundant raw data can be converted into smart data. All the necessary functions of data collection, data preprocessing, data storage, data mining and data visualization can be effectively integrated into the BDA model for CPS applications. These findings show that the proposed BDA model in CPS can be used in different environments and applications.