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The purpose of this paper is to develop an automated engine health monitoring system (AEHMS) for commercial aircraft.

The AEHMS is developed by using fuzzy logic. The input of the fuzzy logic is engine performance parameters gathered from aircraft for every flight during cruise. The fuzzy rule inference system for different engine faults is based on expert knowledge and real life data in the Turkish Airlines fleet. The very smallest is used for defuzzification, since it provides a more meaningful result than others. The complete loop of engine health monitoring (EHM) is automatically performed by the programs and Fuzzy Logic Toolbox in MATLAB. The system produces output values between 0 – faulty and 1 – not faulty for every fault or deterioration on a time series. The program triggers an alert if any output exceeds a specified value. Finally, the method is utilized for monitoring the engines in the Turkish Airlines fleet.

Health monitoring has been a very popular subject to increase aircraft availability with the minimum maintenance cost. Fuzzy logic is a very useful method for automated health monitoring strategies.

It does not provide long‐term engine maintenance decisions such as scheduling overhaul times, predicting the remaining life of the engine components.

The paper provides a robust method for EHM with the application to real aircraft data. The AEHMS can greatly simplify the EHM system for airlines and minimizes its drawbacks, such as extra labor hours, human error and requirement for engineering expertise.

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.

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.

Remote monitoring technology (RMT) is widely acknowledged as an important enabler of servitisation however, there is a dearth of understanding about how RMT is used by manufacturing firms to support servitised strategies. The purpose of this paper is to contribute to this important yet somewhat ignored topic in servitisation research. It attempts to address the following questions: what has constrained, and what has enabled the exploitation of RMT in the context of servitised strategies?

The research adopts an exploratory multiple-case study design. Four in-depth descriptive case studies of companies operating in aerospace, industrial equipment, marine, and transport sectors were conducted. The collected data were analysed and synthesised, drawing out conclusions.

The study describes how four manufacturers are using RMT and identifies ten factors that have enabled and constrained the realisation of expected outcomes. The enabling factors identified include: skills, experience, and knowledge; support from customers and other complementary data sources, processes, and structures; operations centres; historical data; and presence of in-house knowledge and capabilities. While the constraining factors include: defining benefits of RMT; limitations of RMT; limited understanding about true capabilities of RMT; knowledge management; and lack of alignment between services and manufacturing strategies.

While considerable attention and effort have been invested in designing and conducting the research and analysing the data from the case studies, more empirical work is required to validate and enrich findings and conclusions. For this purpose several research questions to guide further theory development in this area are formulated.

This paper is an in-depth study examining the role of RMT in supporting servitised strategies. In particular, it explores how this technology is used in practice to support service-oriented value propositions of manufacturers and identifies the factors that are key to successfully executing this strategy. As such it qualifies as one of the first studies of this kind.

AEM will be exhibiting in Hall 4, Stand G1. The exhibit will illustrate AEM's comprehensive range of accessory repair and overhaul services for electrical, hydraulic, avionic and safety equipment. Farnborough will also be used as the official launch of AEM's Boeing 737 Landing Gear Total Support Pro‐gramme, which encompasses a complete exchange and overhaul service. Copies of Aviation Accessory News will be available on the stand.

This study aims to focus on verifying the possibility of monitoring the condition of a turboprop engine using data recorded by on-board avionics Garmin G1000. This approach has potential benefits for operators without the need to invest in specialised equipment. The main focus was on the inter-turbine temperature (ITT). An unexpected increase in temperature above the usual value may indicate an issue with the engine. The problem lies in the detection of small deviations when the absolute value of the ITT is affected by several external variables.

The ITT is monitored by engine sensors and stored by avionics 1× per second onto an SD card. This process generates large amount of data that needs to be processed. Therefore, an algorithm was created to detect the steady states of the engine parameters. The ITT value also depends on the flight parameters and surrounding environment. As a solution to these effects, the division of data into clusters that represent the usual flight profiles was tested. This ensures a comparison at comparable ambient pressures. The dominant environmental influence then remain at the ambient air temperature (OAT). Three OAT compensation methods were tested in this study. Compensation for the standard atmosphere, compensation for the standard temperature of the given flight level and compensation for the speed of the generator, where the regression analysis proved the dependence between the ambient temperature and the speed of the generator.

The influence of ambient temperature on the corrected ITT values is noticeable. The best method for correcting the OAT appears to be the use of compensation through the revolutions of the compressor turbine NG. The speed of the generator depends on several parameters, and can refine the corrected ITT value. During the long-term follow-up, the ITT differences (delta values) were within the expected range. The tested data did not include the behaviour of the engine with a malfunction or other damage that would clearly verify this approach. Therefore, the engine monitoring will continue.

This study presents a possible approach to turbine engine condition monitoring using limited on board avionic data. These findings can support the development of an engine condition monitoring system with automatic abnormality detection and low operating costs.

This article represent a practical description of problems in monitoring the condition of a turboprop engine in an aircraft with variable flight profiles. The authors are not aware of a similar method that uses monitoring of engine parameters at defined flight levels. Described findings should limit the influence of ambient air pressure on engine parameters.

The purpose of this paper is to propose and develop artificially intelligent methodologies to discover degradation trends through the detection of engine’s status. The objective is to predict these trends by studying their effects on the engine measurable parameters.

The method is based on the implementation of an artificial neural network (ANN) trained with well-known cases referred to real conditions, able to recognize degradation because of two main gas turbine engine deterioration effects: erosion and fouling. Three different scenarios are considered: compressor fouling, turbine erosion and presence of both degraded conditions. The work consists of three parts: the first one contains the mathematical model of real jet engine in healthy and degraded conditions, the second step is the optimization of ANN for engine performance prediction and the last part deals with the application of ANN for prediction of engine fault.

This study shows that the proposed diagnostic approach has good potential to provide valuable estimation of engine status.

Knowledge of the true state of the engine is important to assess its performance capability to meet the operational and maintenance requirements and costs.

The main advantage is that the engine performance data for model validation were obtained from real flight conditions of the engine VIPER 632-43.

Servitization centres on the transfer of risks from the customer to manufacturer. By providing real-time information about current and predicted health of a product in the field, remote monitoring technology can mitigate some of those risks. Although recognised as one of the key enablers of servitization, the mainstream servitization research community has shown very little interest in this topic. The aim is to identify and critically analyse relevant research addressing the topic of remote monitoring technology and servitization and, based on this analysis, propose an agenda to guide future research in this area.

The methodology adopted is literature review consisting of three steps: define purpose and research questions, select keywords and databases, and identify and analyse relevant papers.

Ten findings have been made, which characterise current state of research under categories of examples, benefits, and challenges of using remote monitoring technology to support servitized strategies. Several areas that call for further research are suggested, but general impression is that the understanding about the role and contribution of remote monitoring technology in service delivery and strategy is still in its infancy and much greater effort will have to be invested to change this. It is also suggested that this technology holds a great potential for service and business model innovation; hence, more research is needed to further the knowledge about these topics.

This paper qualifies as the first attempt to consolidate and analyse relevant research at the intersection of servitization and remote monitoring technology.

An aircraft engine control system consists of a large scale of control parameters and variables because of the complex structure of aero-engine. Monitoring and adjusting control variables and parameters such as detecting, isolating and reconfiguring the system faults/failures depend on the controller design. Developing a robust controller is based on an accurate mathematical model.

In this study, a small-scale turboprop engine is modeled. Simulation is carried out on MATLAB/Simulink for design and off-design operating conditions. Both steady-state and transient conditions (from idle to maximum thrust levels) are tested. The performance parameters of compressor and turbine components are predicted via trained Neuro-Fuzzy model (ANFIS) based on component maps. Temperature, rotational speed, mass flow, pressure and other parameters are generated by using thermodynamic formulas and conservation laws. Considering these calculated values, error calculations are made and compared with the cycle data of the engine at the related simulation conditions.

Simulation results show that the designed engine model’s simulation values have acceptable accuracy for both design and off-design conditions from idle to maximum power operating envelope considering cycle data. The designed engine model can be adapted to other types of gas turbine engines.

Different from other literature studies, in this work, a small-scale turboprop engine is modeled. Furthermore, for performance prediction of compressor and turbine components, ANFIS structure is applied.