System-Level Fault Diagnosis with Application to the Environmental Control System of an Aircraft

Hare, James Z.; Gupta, Savita; Najjar, Nayeff; D'Orlando, Paul; Walthall, Rhonda

doi:10.4271/2015-01-2583

Cited by 10 publications

(14 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done by a Bayesian network, which is constructed based on first principles, expert knowledge, and maintenance data and is designed to detect faults in the following components: heat exchanger, air cycle machine, temperature control valve and ram air actuator. Finally, Hare et al 22 trained multiple neural networks in order to differentiate between heat exchanger, air cycle machine, and sensor faults. An interesting aspect of their approach is that a dedicated neural network was trained to recognize the health state of each examined component.…”

Section: Background Of Ecs Diagnosticsmentioning

confidence: 99%

Aircraft system-level diagnosis with emphasis on maintenance decisions

Skliros

Ali

King

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

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.

show abstract

Section: Background Of Ecs Diagnosticsmentioning

confidence: 99%

Aircraft system-level diagnosis with emphasis on maintenance decisions

Skliros

Ali

King

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…The authors in [112] applied a system level diagnostic methodology at an aircraft's Environmental Control System (ECS). The proposed method breaks the ECS down into two major subsystems and each subsystem into two major components and one sensor module.…”

Section: System Level Diagnosticsmentioning

confidence: 99%

“…Data driven approaches such as neural networks [112] and Bayesian networks [113] have also been used for system level diagnostics. The key element that allows data driven algorithms to capture components interconnections is the training strategy used.…”

Section: Fuel Systemmentioning

confidence: 99%

“…The key element that allows data driven algorithms to capture components interconnections is the training strategy used. As an example, is presented the methodology used by [112], to train a neural network (The healthy case considered the subsystem or the component under examination to be healthy while one of the remaining components, in another subsystem, is faulty. The faulty case considered the component under examination to be faulty while all other components are healthy.…”

Section: Fuel Systemmentioning

confidence: 99%

See 1 more Smart Citation

A review of model based and data driven methods targeting hardware systems diagnostics

Skliros¹,

Miguez²,

Ali³

et al. 2018

Diagnostyka

View full text Add to dashboard Cite

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.

show abstract

“…1 Najjar et al 2 analyzed the temperature data of the heat exchanger using principal component analysis (PCA), and classified the different fault conditions using the support vector machine (SVM) and the k -nearest neighbor (k-NN). Hare et al 3 proposed a hierarchical fault detection and isolation method that improved the accuracy of the complex network fault detection and system-level fault diagnosis and isolation, while reducing computational complexity and false alarm rate. Andre et al studied the failure mode, mechanism and influence results of the fundamental components of the aircraft ECS.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network method for fault diagnosis of civil aircraft environment control system

Liu

Sun

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

This paper addresses the issues of fault diagnosis of the environmental control system of a certain commercial aircraft model of which the environmental control system has a high failure rate in field and causes many unplanned maintenance events. Because of the complexity and reciprocal compensation mechanism of the environmental control system, it is difficult to carry out fault isolation timely once the failure occurred during aircraft turnaround time, which thus may cause flight delay or even cancelation. The original contribution of this work is to propose a Bayesian network–based fault diagnosis method for commercial aircraft environmental control system where a multi-information fusion mechanism is used to incorporate the system first principle, expert experience and condition monitoring data. It incorporates extraction technology of sensor feature parameters and the structural learning of Bayesian network to realize the effective diagnosis of multiple faults. A case study is conducted based on a data set from a commercial aircraft fleet. The results show that the fault isolation ratio of this method is greater than 89%. The proposed Bayesian fault diagnosis network method can be used as a troubleshooting tool for airline maintenance technicians in fault isolation of environmental control system, reducing the time spent on-line troubleshooting and aircraft downtime.

show abstract

System-Level Fault Diagnosis with Application to the Environmental Control System of an Aircraft

Cited by 10 publications

References 17 publications

Aircraft system-level diagnosis with emphasis on maintenance decisions

Aircraft system-level diagnosis with emphasis on maintenance decisions

A review of model based and data driven methods targeting hardware systems diagnostics

Bayesian network method for fault diagnosis of civil aircraft environment control system

Contact Info

Product

Resources

About