Real-time fault diagnosis

Verma, Vandi; Gordon, Geoffrey J.; Simmons, Reid; Thrun, Sebastian

doi:10.1109/mra.2004.1310942

Cited by 188 publications

(79 citation statements)

References 30 publications

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“…Many studies have been devoted to endogenous fault detection, that is, a robot detecting faults in itself, see for instance [6,7,8,9,10,11,12,13,14]. Some faults are, however, hard to detect in the robot in which they occur.…”

Section: Introductionmentioning

confidence: 99%

From Fireflies to Fault-Tolerant Swarms of Robots

Christensen

O’Grady

Dorigo

2009

IEEE Trans. Evol. Computat.

141

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Abstract. One of the essential benefits of multi-robot systems is redundancy. In case one robot breaks down, another robot can take steps to repair the failed robot or take over the failed robot's task. Although fault tolerance and robustness to individual failures have often been central arguments in favor of multi-robot systems, few studies have been dedicated to the subject. In this study, we take inspiration from the synchronized flashing behavior observed in some species of fireflies. We derive a completely distributed algorithm to detect non-operational individuals in a multi-robot system. Each robot flashes by lighting up its onboard LEDs and neighboring robots are driven to flash in synchrony. Since robots that are suffering catastrophic failures do not flash periodically, they can be detected by operational robots. We explore the performance of the proposed algorithm both on a real world multirobot system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates.

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Section: Introductionmentioning

confidence: 99%

From Fireflies to Fault-Tolerant Swarms of Robots

Christensen

O’Grady

Dorigo

2009

IEEE Trans. Evol. Computat.

141

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“…There is extensive literature on anomaly detection of a hybrid system [7]- [11]; all of them presuppose a model of a system. On the other hand, our method does not assume a system model because preparing the model for CPS and its environment is a difficult task.…”

Section: Related Workmentioning

confidence: 99%

Log-Based Anomaly Detection of CPS Using a Statistical Method

Harada

Yamagata

Mizuno

et al. 2017

2017 8th International Workshop on Empirical Software Engineering in Practice (IWESEP)

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Abstract-Detecting anomalies of a cyber physical system (CPS), which is a complex system consisting of both physical and software parts, is important because a CPS often operates autonomously in an unpredictable environment. However, because of the ever-changing nature and lack of a precise model for a CPS, detecting anomalies is still a challenging task. To address this problem, we propose applying an outlier detection method to a CPS log. By using a log obtained from an actual aquarium management system, we evaluated the effectiveness of our proposed method by analyzing outliers that it detected. By investigating the outliers with the developer of the system, we confirmed that some outliers indicate actual faults in the system. For example, our method detected failures of mutual exclusion in the control system that were unknown to the developer. Our method also detected transient losses of functionalities and unexpected reboots. On the other hand, our method did not detect anomalies that were too many and similar. In addition, our method reported rare but unproblematic concurrent combinations of operations as anomalies. Thus, our approach is effective at finding anomalies, but there is still room for improvement.

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“…Recently, computationally efficient approaches for approximating Bayesian belief using particle filters have been studied as a means for fault detection and identification (Dearden et al 2004;Verma et al 2004;Li and Kadirkamanathan 2001). Particle filters are Monte Carlo methods capable of tracking hybrid state spaces of continuous noisy sensor data and discrete operation states.…”

Section: Related Workmentioning

confidence: 99%

Fault detection in autonomous robots based on fault injection and learning

et al. 2007

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In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

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Real-time fault diagnosis

Cited by 188 publications

References 30 publications

From Fireflies to Fault-Tolerant Swarms of Robots

From Fireflies to Fault-Tolerant Swarms of Robots

Log-Based Anomaly Detection of CPS Using a Statistical Method

Fault detection in autonomous robots based on fault injection and learning

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