Browsing by Author "Skliros, Christos"

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    Aircraft system-level diagnosis with emphasis on maintenance decisions
    (SAGE, 2021-10-26) Skliros, Christos; Ali, Fakhre; King, Steve; Jennions, Ian
    This paper proposes a diagnostic technique that can predict component degradation for a number of complex systems. It improves and clarifies the capabilities of a previously proposed diagnostic approach, by identifying the degradation severity of the examined components, and uses a 3D Principal Component Analysis approach to provide an explanation for the observed diagnostic accuracy. The diagnostic results are then used, in a systematic way, to influence maintenance decisions. Having been developed for the Auxiliary Power Unit (APU), the flexibility and power of the diagnostic methodology is shown by applying it to a completely new system, the Environmental Control System (ECS). A major conclusion of this work is that the proposed diagnostic approach is able to correctly predict the health state of two aircraft systems, and potentially many more, even in cases where different fault combinations result in similar fault patterns. Based on the engineering simulation approach verified here, a diagnostic methodology suitable from aircraft conception to retirement is proposed.
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    A case study of vibration fault diagnosis applied at Rolls-Royce T-56 turboprop engine
    (Taylor & Francis, 2020-01-22) Skliros, Christos
    Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.
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    Fault simulations and diagnostics for a Boeing 747 Auxiliary Power Unit
    (Elsevier, 2021-07-01) Skliros, Christos; Ali, Fakhre; Jennions, Ian
    Health monitoring of aircraft systems is of great interest to aircraft manufacturers and operators because it minimises the aircraft downtime (due to avoiding unscheduled maintenance), which in turn reduces the operating costs. The work that is presented in this paper explores, for a Boeing 747 APU, fault simulation and diagnostics for single and multiple component faults. Data that corresponds to healthy and faulty conditions is generated by a calibrated simulation model, and a set of performance parameters (symptom vector) are selected to characterise the components health state. For each component under examination, a classification algorithm is used to identify its health state (healthy or faulty) and the training strategy that is used considers the existence of multiple faults in the system. The proposed diagnostic technique is tested against single and multiple fault cases and shows good results for the compressor, turbine, Load Control Valve (LCV) and Fuel Metering Valve (FMV), even though these faults present similar fault patterns. On the contrary, the classifiers for the Speed Sensor (SS) and the generator do not provide reliable predictions. As regards the SS, the sensitivity assessment for this component showed that the existence of faults in the other components can sometimes mask the SS fault. The reason that the generator diagnosis fails under the proposed diagnostic technique is attributed to the fact that it has only a very slight influence on the other symptom vector parameters. In both cases, additional diagnostic strategies are suggested.
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    A review of model based and data driven methods targeting hardware systems diagnostics
    (PTDT, 2018-11-22) Skliros, Christos; Miguez, Manuel Esperon; Fakhre, Ali; Jennions, Ian
    System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls.
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    System diagnosis for an auxiliary power unit
    (Cranfield University, 2020-08) Skliros, Christos; Jennions, Ian K.; Ali, Fakhre
    Even though the Auxiliary Power Unit (APU) is a widely used system in modern aviation, the existing experimental, simulation and diagnostic studies for this system are very limited. The topic of this project is the System Diagnosis of an APU, and the case study that is used in this research is a Boeing 747 APU. This APU was used to develop an experimental rig in order to collect performance data under a wide range of loading and environmental conditions. The development of the experimental rig consumed considerable time and required the design and installation of structures and parts related with the control of the APU, the adjustment of the electric and pneumatic load and the data acquisition. The validation of the rig was achieved by a repeatability test, which ensures that the collected measurements are repeatable under the same boundary conditions, and by a consistency test, which ensures that the performance parameters are consistent with the imposed ambient conditions. The experimental data that are extracted from the rig were used to calibrate a physics-based (0-D) model for steady-state conditions. Data that correspond to faulty conditions were generated by injecting faults in the simulation model. Based on the most prominent APU faults, as reported by The Boeing Company, six components that belong to different sub-systems were considered in the diagnostic analysis, and for each one of them, a single fault mode was simulated. By using healthy and faulty simulation data, for each component under examination, a classification algorithm that can recognise the healthy and faulty state of the component is trained. A critical part of the diagnostic analysis is that each classifier was trained to recognise the healthy and the faulty state of the corresponding component, while other components can be either healthy or faulty. The test results showed that the proposed technique is able to diagnose both single and multiple faults, even though in many cases different component faults resulted in similar fault patterns.