Fault diagnosis of aircraft heat exchangers based on RELS method

He, Jie; Zhu, Lei; Zhao, Jiwen; Guo, Yuanyuan

doi:10.1109/icicip.2011.6008294

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Due to the lack of an effective system health monitoring solution, it is difficult to realize the early detection of ECS faults, which may cause unplanned maintenance. On the premise of building the simulation model of the ECS, He et al 1 made use of the parameter data obtained from the simulation to study the fault diagnosis. The recursive extended least squares (RELS) method was adopted to identify heat exchanger parameters in ECS, and fault diagnosis was carried out by identifying the change of parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The recursive extended least squares (RELS) method was adopted to identify heat exchanger parameters in ECS, and fault diagnosis was carried out by identifying the change of parameters. 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.…”

Section: Introductionmentioning

confidence: 99%

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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:

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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.

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Section: Introductionmentioning

confidence: 99%