Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

Wang, Zhongwei; Smith, Alexander Thomas T.; Gu, Fengshou; Zhang, Chi; Ball, Andrew

doi:10.1016/j.jfranklin.2020.05.037

Cited by 8 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improving the competence of fault diagnosis and ground maintenance, so as to improve the functionality and reliability of UAV, which is as important as exploring the modern UAV control methods such as using a brain-computer interface [1], has currently become the subject of further research emphasis [2][3][4]. With the continuous development of Prognostics Health Management (PHM) technology, a great quantity of sensors are employed in the new generation of large fixed-wing UAV, bringing the explosive growth of flight data scale [5]. The data-driven methods, thanks to the growth of the data scale, are gradually replacing the traditional Physics of Failure (PoF) methods [6], becoming the mainstream for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

et al. 2021

View full text Add to dashboard Cite

In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.

show abstract

Section: Introductionmentioning

confidence: 99%

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Improving the competence of fault diagnosis and ground maintenance, so as to improve the functionality and reliability of UAVs has thus become an essential research area [1][2][3]. With the development of Prognostics Health Management (PHM) technology, abundant onboard sensors and multisource analysis records have brought about the swift growth of operation and maintenance data of UAVs [4]. These data-driven methods, thanks to the growth of data scales, are gradually replacing the traditional Physics of Failure (PoF) methods [5,6], becoming the mainstream of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

et al. 2021

View full text Add to dashboard Cite

In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

show abstract

Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example

Smith

Lee

2022

Proceedings of IncoME-VI and TEPEN 2021

View full text Add to dashboard Cite

Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

Cited by 8 publications

References 28 publications

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example

Contact Info

Product

Resources

About