Recognizing Hardware Faults on Mobile Robots Using Situation Analysis Techniques

Zweigle, Oliver; Keil, Benjamin; Wittlinger, Markus; Häußermann, Kai; Levi, Paul

doi:10.1007/978-3-642-33926-4_37

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…This mismatch may be enhanced by the effect of noise and disturbances that cannot be modeled, thus causing the residual to become nonzero during the failure-free operation of the system (Luh et al, 2004). This problem limits the applicability of the model-based failure indicators to well defined operation scenarios that are not subject to uncertainties, unlike non-linear systems (Bocaniala and Palade, 2006;Zweigle et al, 2013). Furthermore, the residual indicator is limited to the specific system functions that are widely known and predictable, hindering its application in highly complex systems where fault propagation across subsystems occurs (Sun et al, 2014).…”

Section: Model-based Fault Detection Methodsmentioning

confidence: 99%

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Ruiz-Arenas

Rusák

Horváth

et al. 2019

Frontiers Inf Technol Electronic Eng

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Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.

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Section: Model-based Fault Detection Methodsmentioning

confidence: 99%

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Ruiz-Arenas

Rusák

Horváth

et al. 2019

Frontiers Inf Technol Electronic Eng

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“…Component faults do not always show themselves as simply being non-functional or disabled. In this research [19], the authors use Evidence, [20], the authors perform tests between similar components to establish if they are correlated to each other. If abnormal behavior is detected, then this could indicate a possible fault in one of the components.…”

Section: Autonomic Self-adaption: Fault Detectionmentioning

confidence: 99%

“…The PC value is calculated using Eq. (5). For this experiment, the acceptable threshold value for how much capacity a robot task uses is set to 80% (AT).…”

Section: Initial Task Setupmentioning

confidence: 99%

“…The use of sensor fusion [4] has also been adapted to compare expected performance models of B Martin Doran doran-m18@ulster.ac.uk Roy Sterritt r.sterritt@ulster.ac.uk George Wilkie fg.wilkie@ulster.ac.uk 1 Ulster University, Jordanstown, Ireland normal sensor outputs with that of outputs from the actual mobile robot sensors. Classification and detection of faults can be established using techniques such as situation analysis [5]. Recognition of behavioral anomalies can be interpreted as symptoms of possible faults within the system.…”

Section: Introductionmentioning

confidence: 99%

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Autonomic architecture for fault handling in mobile robots

Doran

Sterritt

Wilkie

2020

Innovations Syst Softw Eng

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This paper describes a generic autonomic architecture for use in developing systems for managing hardware faults in mobile robots. The method by which the generic architecture was developed is also described. Using autonomic principles, we focused on how to detect faults within a mobile robot and how specialized algorithms can be deployed to compensate for the faults discovered. We design the foundation of a generic architecture using the elements found in the MAPE-K and IMD architectures. We present case studies that show three different fault scenarios that can occur within the effectors, sensors and power units of a mobile robot. For each case study, we have developed algorithms for monitoring and analyzing data stored from previous tasks completed by the robot. We use the results from the case studies to create and refine a generic autonomic architecture that can be utilized for any general mobile robot setup for fault detection and fault compensation. We then describe a further case study which exercises the generic autonomic architecture in order to demonstrate its utility. Our proposal addresses fundamental challenges in operating remote mobile robots with little or no human intervention. If a fault does occur within the mobile robot during field operations, then having a self-automated strategy as part of its processes may result in the mobile robot continuing to function at a productive level. Our research has provided insights into the shortcomings of existing robot design which is also discussed.

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Fault Detection and Analysis of a Mobile Robot using Radial Basis Function Network

Tolossa,

Manohar,

Jahnavi

et al. 2024

2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T)

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Recognizing Hardware Faults on Mobile Robots Using Situation Analysis Techniques

Cited by 3 publications

References 16 publications

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Autonomic architecture for fault handling in mobile robots

Fault Detection and Analysis of a Mobile Robot using Radial Basis Function Network

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