Fault Detection Method Using Context-Based Approach

Kalisch, Mateusz

doi:10.1007/978-3-319-23180-8_28

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Brezillon andPomerol, 1999, Dey et al, 2001]. Systems have been successfully designed to track context in data streams [Gomes et al, 2012], identify recurrent concepts [Gama and Kosina, 2014] and perform fault detection [Kalisch, 2016].…”

Section: Context In Machine Learningmentioning

confidence: 99%

Contextual One-Class Classification in Data Streams

Moulton¹,

Viktor²,

Japkowicz³

et al. 2019

Preprint

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In machine learning, the one-class classification problem occurs when training instances are only available from one class. It has been observed that making use of this class's structure, or its different contexts, may improve one-class classifier performance. Although this observation has been demonstrated for static data, a rigorous application of the idea within the data stream environment is lacking. To address this gap, we propose the use of context to guide one-class classifier learning in data streams, paying particular attention to the challenges presented by the dynamic learning environment. We present three frameworks that learn contexts and conduct experiments with synthetic and benchmark data streams. We conclude that the paradigm of contexts in data streams can be used to improve the performance of streaming one-class classifiers.

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Section: Context In Machine Learningmentioning

confidence: 99%

Contextual One-Class Classification in Data Streams

Moulton¹,

Viktor²,

Japkowicz³

et al. 2019

Preprint

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Genetic Optimization of Meta-Learning Schemes for Context-Based Fault Detection

Przystałka

Kalisch

Timofiejczuk

2017

Applied Condition Monitoring

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On Fault Detection and Diagnosis in Robotic Systems

2018

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The use of robots in our daily lives is increasing. Different types of robots perform different tasks that are too dangerous or too dull to be done by humans. These sophisticated machines are susceptible to different types of faults. These faults have to be detected and diagnosed in time to allow recovery and continuous operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches and techniques that are applicable to different types of physical machines. Yet the domain of robotics poses unique requirements that are very challenging for traditional FDD approaches. The study of FDD for robotics is relatively new, and only few surveys were presented. These surveys have focused on traditional FDD approaches and how these approaches may broadly apply to a generic type of robot. Yet robotic systems can be identified by fundamental characteristics, which pose different constraints and requirements from FDD. In this article, we aim to provide the reader with useful insights regarding the use of FDD approaches that best suit the different characteristics of robotic systems. We elaborate on the advantages these approaches have and the challenges they must face. To meet this aim, we use two perspectives: (1) we elaborate on FDD from the perspective of the different characteristics a robotic system may have and give examples of successful FDD approaches, and (2) we elaborate on FDD from the perspective of the different FDD approaches and analyze the advantages and disadvantages of each approach with respect to robotic systems. Finally, we describe research opportunities for robotic systems’ FDD. With these three contributions, readers from the FDD research communities are introduced to FDD for robotic systems, and the robotics research community is introduced to the field of FDD.

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Fault Detection Method Using Context-Based Approach

Cited by 3 publications

References 14 publications

Contextual One-Class Classification in Data Streams

Contextual One-Class Classification in Data Streams

Genetic Optimization of Meta-Learning Schemes for Context-Based Fault Detection

On Fault Detection and Diagnosis in Robotic Systems

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