Fault Identification Via Nonparametric Belief Propagation

Bickson, Danny; Baron, Dror; Ihler, Alexander T.; Avissar, Harel; Dolev, Danny

doi:10.1109/tsp.2011.2116014

Cited by 18 publications

(19 citation statements)

References 52 publications

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“…We showed the applicability of the proposed methods in this paper through numerical examples. We note that when accurate a priori statistical characterisation of the faults are available application of the method proposed in [7] possibly yields better results.…”

Section: Discussionmentioning

confidence: 97%

“…The main differences between the scenario studied in [7] and our result are that, first, we do not make any assumptions on knowing the probability of occurrence of a given fault or a set of faults. Second, in addition to identifying the faulty elements of the network, we are interested in the calculation of the fault values when the fault vector is sparse without making any further assumption on its sparsity.…”

mentioning

confidence: 84%

“…We note that recently the problem of identifying faulty measurements through linear models has attracted much attention. For example, in [7] the authors propose fault identification via belief propagation in a network and have shown their method's superiority comparing with other state-of-the-art methods.…”

mentioning

confidence: 99%

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Fault Detection and Mitigation in Kirchhoff Networks

Shames

Teixeira

Johansson

2012

IEEE Signal Process. Lett.

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In this letter, we study the problem of fault detection and mitigation in networks where the measurements satisfy Kirchhoff's voltage law. First, we characterise the class of faults appearing as an additive fault vector (injected by a malicious adversary or due to equipment failures) that can be detected by taking into account the topology of the network. Second, we consider the problem of estimating the fault vector via tools from compressive sensing. Moreover, we comment on the applicability of the developed methods to the case where the measurements satisfy Kirchhoff's current law. The proposed methods are validated via numerical examples with application to time synchronization networks.

QC 20121112

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Section: Discussionmentioning

confidence: 97%

mentioning

confidence: 84%

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Fault Detection and Mitigation in Kirchhoff Networks

Shames

Teixeira

Johansson

2012

IEEE Signal Process. Lett.

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QC 20121112

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“…Mechanical vibration signal contains a lot of information of equipment in the work process, on-line monitor, collection and extracting useful information of machinery vibration signal is a key technology in mechanical engineering field, especially in the fault diagnosis and remote fault diagnosis technology [1] [2] .…”

Section: Introductionmentioning

confidence: 99%

Adaptability Analysis of Common Measurement Matrices for Mechanical Vibration Signal

Guo¹,

Shi²,

Lei³

et al. 2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

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Abstract-In this paper, the construction method of common measurement matrices is studied, adaptability of common measurement matrices for mechanical vibration signal is analyzed. Typical measurement matrices are selected from commonly used measurement matrices, Gaussian random measurement matrix and Bernoulli random measurement matrix are chosen from totally random measurement matrices, Circulant measurement matrix and Toeplitz measurement matrix are selected from deterministic matrices, partially random Fourier measurement matrix and Hadamard matrix are chosen from deterministic measurement matrices. The sensing performance of common measurement matrices for mechanical vibration signal is evaluated from the two perspective of reconstruction error and memory space. The simulation results show that two kinds of complete random matrices, Gaussian and Bernoulli matrices, can exactly reconstruct original vibration signal, but they occupy large memory space; deterministic matrices, Circulant and Toeplitz matrices, although need fewer memory space, obtained measurements which do not have information of global vibration signal lead to lower reconstruction results; partially random Fourier matrix is extremely coherent with sparse transforming base of vibration signal, so it has not exact result in the process of reconstructing original vibration signal, the requirement of Exponentiation of 2 seriously restrict its application.

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“…Then the latter can be solved using orthogonal matching pursuit (OMP) algorithm. 21,22 Nevertheless, these methods only deal with the additive faults. Moreover, the physical limitation of the fault is not considered in Wang et al., 20 which limits the practicability of these methods.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis and robust fault-tolerant control for linear over-actuated systems

Zhou

Wang

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper proposes a novel fault diagnosis and robust fault-tolerant control strategy for over-actuated systems which subject to actuator faults. Firstly, the considered fault is formulated into an additive term (which is referred to as virtual fault). Then an augmented state observer is used to estimate this virtual additive fault. To achieve fault isolation and estimation for the over-actuated system, which is more difficult than that of an ordinary system, this paper considers the actuator fault as a sparse signal and proposes a constrained sparse optimization approach to recover the actual multiplicative fault in the over-actuated system. Based on the fault diagnosis result, a robust fault-tolerant controller is designed by the virtual actuator approach, which aims to construct a reconfiguration block to force the output of the reconfigured faulty plant approximated the behavior of a nominal system. Finally, the proposed fault diagnosis and faulttolerant scheme is applied to an ADMIRE model. Simulation results demonstrate the effectiveness of the proposed method.

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Fault Identification Via Nonparametric Belief Propagation

Cited by 18 publications

References 52 publications

Fault Detection and Mitigation in Kirchhoff Networks

Fault Detection and Mitigation in Kirchhoff Networks

Adaptability Analysis of Common Measurement Matrices for Mechanical Vibration Signal

Fault diagnosis and robust fault-tolerant control for linear over-actuated systems

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