The Application of Bayesian Change Point Detection in UAV Fuel Systems

Niculita, Octavian; Skaf, Zakwan; Jennions, Ian K.

doi:10.1016/j.procir.2014.07.119

Cited by 23 publications

(13 citation statements)

References 16 publications

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“…The graphical representation below shows the clogged filter degradation scenario. Case two: the multiple condition test for faulty gear pump represented by the DPV2 was emulated by opening the valve from 100%, 80%, 60%, 40%, 30%, while the DPV1 was at 100%, DPV3 at 100%, DPV4 at 0%, and DPV5 at 100%, then pressure (1,2,3,4,5) were investigated as shown in the graphical representation of figure 7 below. …”

Section: Results Simulationmentioning

confidence: 99%

“…[1] Asserts that test bed is developed to allowed user to safely control the UAV effectively. The development of test bed is used as a platform to investigate the component fault such as clogged filter, faulty gear pump, stuck valve, leaking pipe, and clogged nozzle [2]. The figure 2 below shows the interconnection between the test bed hardware and software via the NI DAQ module.…”

Section: Test Bed Developmentmentioning

confidence: 99%

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The Development of a test bed to support the Design of Diagnostics and Prognostics Applications of a complex system

Emmanuel¹,

Agbep²,

Komi³

2017

IOSR JEEE

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Section: Results Simulationmentioning

confidence: 99%

Section: Test Bed Developmentmentioning

confidence: 99%

The Development of a test bed to support the Design of Diagnostics and Prognostics Applications of a complex system

Emmanuel¹,

Agbep²,

Komi³

2017

IOSR JEEE

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“…The experimental fuel rig test-bed (shown in Figure 2) has been developed in the IVHM Centre at Cranfield University (Niculita, Skaf, & Jennions, 2014). It is specifically designed in order to replicate a number of component degradation faults with high accuracy and repeatability so that it can produce benchmark datasets to evaluate and assess the developed algorithms.…”

Section: Experimental Fuel Rig Descriptionmentioning

confidence: 99%

A Bayesian Approach to Fault Identification in the Presence of Multi-component Degradation

Lin

Skaf

Jennions

2020

IJPHM

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Fault diagnosis typically consists of fault detection, isolation and identification. Fault detection and isolation determine the presence of a fault in a system and the location of the fault. Fault identification then aims at determining the severity level of the fault. In a practical sense, a fault is a conditional interruption of the system ability to achieve a required function under specified operating condition; degradation is the deviation of one or more characteristic parameters of the component from acceptable conditions and is often a main cause for fault generation. A fault occurs when the degradation exceeds an allowable threshold. From the point a new aircraft takes off for the first time all of its components start to degrade, and yet in almost all studies it is presumed that we can identify a single fault in isolation, i.e. without considering multi-component degradation in the system. This paper proposes a probabilistic framework to identify a single fault in an aircraft fuel system with consideration of multi-component degradation. Based on the conditional probabilities of sensor readings for a specific fault, a Bayesian method is presented to integrate distributed sensory information and calculate the likelihood of all possible fault severity levels. The proposed framework is implemented on an experimental aircraft fuel rig which illustrates the applicability of the proposed method.

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“…This approach provides accurate information only at discrete degradation levels. In order to map the full degradation spectrum, the component under test must be subjected to various machining steps to accurately capture the degradation phenomenon (Niculita et al, 2014). This approach has found wide application to both high and low-speed rotating machinery, power drives and power trains, as well as to component structural integrity investigation.…”

Section: Introductionmentioning

confidence: 99%

“…This technique enables the emulation of the degradation effects of some components and their impact at the system level; in this particular application, an example of this technique is the utilization of a direct-acting control valve to emulate the degradation of a filter in a fluid system by gradually closing the valve (to replicate the clogging phenomenon). This method has found application to process-oriented systems, like fluid systems (Niculita et al 2014;Skaf et al, 2015;Lin et al, 2017). For the emulated faults, the main limitation is the utilization of an actuator that is capable to emulate the effects of the desired component under test.…”

Section: Introductionmentioning

confidence: 99%

Requirements and Data Integrity Considerations for Diagnostics Testbeds

Bardakis¹,

Niculita

Wallace

2021

IJPHM

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The process of generating high quality data for the test and evaluation of diagnostic and prognostic algorithms is still of high importance to the Prognostics and Health Management (PHM) research community. To support these efforts a testbed has been designed, manufactured and commissioned. It has specifically been designed in order to replicate several component degradation faults with high accuracy and high repeatability. This paper documents the design, requirements and the data integrity elements of this benchmark hydraulic system. This document consolidates the process of designing diagnostics testbeds as at present there is a lack of literature on how diagnostics testbeds should be built and is intended to serve as a starting point and quick reference guide for engineers and researchers intending to design and develop a testbed to test and validate PHM applications. The first part of this paper highlights design requirements for all the design aspects for such testbeds with great consideration for industry standards and best practices covering the achievement of electromagnetic compatibility (EMC) and noise mitigation, as well as operators’ safety and equipment protection. The second part of the paper put great emphasis on data integrity elements of the data generated by this testbed (describing the system under healthy and faulty conditions) before it is actually used for system characterization or by diagnostics and prognostics algorithms.

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The Application of Bayesian Change Point Detection in UAV Fuel Systems

Cited by 23 publications

References 16 publications

The Development of a test bed to support the Design of Diagnostics and Prognostics Applications of a complex system

The Development of a test bed to support the Design of Diagnostics and Prognostics Applications of a complex system

A Bayesian Approach to Fault Identification in the Presence of Multi-component Degradation

Requirements and Data Integrity Considerations for Diagnostics Testbeds

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