Hierarchical fault classification for resource constrained systems

Adams, Stephen; Meekins, Ryan; Beling, Peter A.; Farinholt, Kevin; Brown, Nigel L.; Polter, Sherwood; Dong, Qing

doi:10.1016/j.ymssp.2019.106266

Cited by 14 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent work, we extensively studied PHM for hydraulic actuators in order to better place related data-driven modeling in a systems context, including cost and power constraints [30]- [33]. These studies have used a fault-simulating test-bed that consists of two matched rotary actuators, where one acts as the actuator and the other acts as the load [34].…”

Section: Prognostics and Health Managementmentioning

confidence: 99%

Empirically Measuring Transfer Distance for System Design and Operation

Cody

Adams

Beling

2022

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

Classical machine learning approaches are sensitive to nonstationarity. Transfer learning can address nonstationarity by sharing knowledge from one system to another, however, in areas like machine prognostics and defense, data are fundamentally limited. Therefore, transfer learning algorithms have little, if any, examples from which to learn. Herein, the authors suggest that these constraints on algorithmic learning can be addressed by systems engineering. We formally define transfer distance in general terms and demonstrate its use in empirically quantifying the transferability of models. We consider the use of transfer distance in the design of machine rebuild procedures to allow for transferable prognostic models. We also consider the use of transfer distance in predicting operational performance in computer vision. Practitioners can use the presented methodology to design and operate systems with consideration for the learning theoretic challenges faced by component learning systems.

show abstract

Section: Prognostics and Health Managementmentioning

confidence: 99%

Empirically Measuring Transfer Distance for System Design and Operation

Cody

Adams

Beling

2022

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent work, we extensively studied PHM for hydraulic actuators in order to better place related data-driven modeling in a systems context, including cost and power constraints [26]- [29]. These studies have used a fault-simulating testbed that consists of two matched rotary actuators, where one acts as the actuator and the other acts as the load [30].…”

Section: Phmmentioning

confidence: 99%

Empirically Measuring Transfer Distance for System Design and Operation

Cody,

Adams,

Beling

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Classical machine learning approaches are sensitive to non-stationarity. Transfer learning can address nonstationarity by sharing knowledge from one system to another, however, in areas like machine prognostics and defense, data is fundamentally limited. Therefore, transfer learning algorithms have little, if any, examples from which to learn. Herein, we suggest that these constraints on algorithmic learning can be addressed by systems engineering. We formally define transfer distance in general terms and demonstrate its use in empirically quantifying the transferability of models. We consider the use of transfer distance in the design of machine rebuild procedures to allow for transferable prognostic models. We also consider the use of transfer distance in predicting operational performance in computer vision. Practitioners can use the presented methodology to design and operate systems with consideration for the learning theoretic challenges faced by component learning systems.

show abstract

“…The training time for each classifier was used as a surrogate for power consumption. A hierarchical classification approach has been proposed to minimize resource consumption [6]. These studies use a relatively small feature set and do not focus on the feature selection problem.…”

Section: Hydraulic Actuator Problemmentioning

confidence: 99%

ROC with Cost Pareto Frontier Feature Selection Using Search Methods

Meekins

Adams

Farinholt

et al. 2020

Data-Enabled Discov. Appl.

Self Cite

View full text Add to dashboard Cite

Cyber-physical systems (CPS) are finding increasing application in many domains. CPS are composed of sensors, actuators, a central decision-making unit, and a network connecting all of these components. The design of CPS involves the selection of these hardware and software components, and this design process could be limited by a cost constraint. This study assumes that the central decision-making unit is a binary classifier, and casts the design problem as a feature selection problem for the binary classifier where each feature has an associated cost. Receiver operating characteristic (ROC) curves are a useful tool for comparing and selecting binary classifiers; however, ROC curves only consider the misclassification cost of the classifier and ignore other costs such as the cost of the features. The authors previously proposed a method called ROC Convex Hull with Cost (ROCCHC) that is used to select ROC optimal classifiers when cost is a factor. ROCCHC extends the widely used ROC Convex Hull (ROCCH) method by combining it with the Pareto analysis for cost optimization. This paper proposes using the ROCCHC analysis as the evaluation function for feature selection search methods without requiring an exhaustive search over the feature space. This analysis is performed on 6 real-world data sets, including a diagnostic cyber-physical system for hydraulic actuators. The ROCCHC analysis is demonstrated using sequential forward and backward search. The results are compared with the ROCCH selection method and a popular Pareto selection method that uses classification accuracy and feature cost.

show abstract

Hierarchical fault classification for resource constrained systems

Cited by 14 publications

References 31 publications

Empirically Measuring Transfer Distance for System Design and Operation

Empirically Measuring Transfer Distance for System Design and Operation

Empirically Measuring Transfer Distance for System Design and Operation

ROC with Cost Pareto Frontier Feature Selection Using Search Methods

Contact Info

Product

Resources

About