Application of fault-tree analysis to troubleshooting the NASA GRC icing research tunnel

Zampino, E.J.

doi:10.1109/rams.2001.902435

Cited by 10 publications

(7 citation statements)

References 1 publication

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“…The maximum number of k-means iterations for cluster initialization was set to 50 while the Baum-Welch algorithm used to estimate the transition matrix was bounded to 100 iterations with a threshold of 0.001 between subsequent iterations. The number of explored 1 Torch machine learning library, available at http://www.torch.ch/ states ranges from 3 to 10 while the number of Gaussian components used to build each GMM comes from the {2, 4,8,16,32,64,128,256, and 512} set. We did not apply any data preprocessing techniques, such as normalization, as we wanted to avoid any kind of information loss.…”

Section: Methodsmentioning

confidence: 99%

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Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling

Ntalampiras¹

2015

IEEE Trans. Neural Netw. Learning Syst.

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This paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems, which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one, is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The system is able to understand whether the novel data belong to the fault dictionary, are fault-free, or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.

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Section: Methodsmentioning

confidence: 99%

“…These methods can be based on quantitative (numerical) and/or qualitative (symbolic) information about the process of interest. Qualitative information is used in [16], where a fault-tree analysis was designed as an analytical troubleshooting tool by a team of knowledgeable managers, engineers, and technicians.…”

Section: Related Literaturementioning

confidence: 99%

Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling

Ntalampiras¹

2015

IEEE Trans. Neural Netw. Learning Syst.

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“…These methods can be based on quantitative (numerical) and/or qualitative (symbolic) information about the process of interest. Qualitative information is used in [8] where a fault-tree analysis was designed as an analytical troubleshooting tool by a team of knowledgeable managers, engineers, and technicians. Fault tree analysis is also used by Crosetti [9] with a probability evaluation scheme.…”

Section: Related Literaturementioning

confidence: 99%

A fault diagnosis system for interdependent critical infrastructures based on HMMs

Ntalampiras¹,

Soupionis²,

Giannopoulos³

2015

Reliability Engineering & System Safety

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Modern society depends on the smooth functioning of critical infrastructures which provide services of fundamental importance, e.g. telecommunications and water supply. These infrastructures may suffer from faults/malfunctions coming e.g. from aging effects or they may even comprise targets of terrorist attacks. Prompt detection and accommodation of these situations is of paramount significance. This paper proposes a probabilistic modeling scheme for analyzing malicious events appearing in interdependent critical infrastructures. The proposed scheme is based on modeling the relationship between datastreams coming from two network nodes by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems which estimate the relationships existing among the specific nodes over consecutive time windows. Our study includes an energy network (IEEE 30 model bus) operated via a telecommunications infrastructure. The relationships among the elements of the network of infrastructures are represented by an HMM and the novel data is categorized according to its distance (computed in the probabilistic space) from the training ones. We considered two types of cyber-attacks (denial of service and integrity/replay) and report encouraging results in terms of false positive rate, false negative rate and detection delay

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“…This means that all faults in the system will show up in the FMon observations. Fault-Tree Analysis is used to depict the causes of the error types found during the experiment [26], [27] as shown in Figure 7. There is no way to distinguish which communication link caused Error Type 1 using only FMon observations.…”

Section: Function Monitor Observationsmentioning

confidence: 99%

High-Intensity Radiated Field fault-injection experiment for a fault-tolerant distributed communication system

Yates

Torres-Pomales

Malekpour

et al. 2010

29th Digital Avionics Systems Conference

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Safety-critical distributed flight control systems require robustness in the presence of faults. In general, these systems consist of a number of input/output (I/O) and computation nodes interacting through a fault-tolerant data communication system. The communication system transfers sensor data and control commands and can handle most faults under typical operating conditions. However, the performance of the closed-loop system can be adversely affected as a result of operating in harsh environments. In particular, High-Intensity Radiated Field (HIRF) environments have the potential to cause random fault manifestations in individual avionic components and to generate simultaneous system-wide communication faults that overwhelm existing fault management mechanisms. This paper presents the design of an experiment conducted at the NASA Langley Research Center's HIRF Laboratory to statistically characterize the faults that a HIRF environment can trigger on a single node of a distributed flight control system.

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Application of fault-tree analysis to troubleshooting the NASA GRC icing research tunnel

Cited by 10 publications

References 1 publication

Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling

Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling

A fault diagnosis system for interdependent critical infrastructures based on HMMs

High-Intensity Radiated Field fault-injection experiment for a fault-tolerant distributed communication system

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