Detecting intermittent sensor faults for linear stochastic systems subject to unknown disturbance

Yan, Rongyi; He, Xiao; Zhou, Donghua

doi:10.1016/j.jfranklin.2016.09.001

Cited by 19 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be found that

\hat{\rho }_{k}

for the ways in refs. [ 28 and 29 ] deviates severely from true

\rho _{k}

. Using the proposed IF detection way, the trend of

\hat{\rho }_{k}

is the same as true HIs.…”

Section: Application To Rul Prediction On Drilling Tool Systemmentioning

confidence: 98%

“…Remark When states in system () remain unchanged, the degradation states of faults can be directly constructed by measurements due to their consistent degradation trends 2,15 . Indeed, linear systems are widely used for target tracking, cooperative positioning, process monitoring, and so forth, due to their advantages in describing the dynamic characteristics of system states 28,29 . In these cases, the dynamic of measurements cannot reflect the degradation state of faults, resulting in difficulties in HI extraction.…”

Section: Problem Formulationmentioning

confidence: 99%

“…2,15 Indeed, linear systems are widely used for target tracking, cooperative positioning, process monitoring, and so forth, due to their advantages in describing the dynamic characteristics of system states. 28,29 In these cases, the dynamic of measurements cannot reflect the degradation state of faults, resulting in difficulties in HI extraction. For the purpose of dealing with the above difficulties, our work is necessary and significant.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Generally, PF detection under strong noise often involves a trade‐off between detection delay and accuracy. For IFs, the fault appearance should be detected within the fault duration time and its disappearance should be detected within the fault interval time, making it more challenging than PF detection 28–31 . Currently, due to difficulties associated with detecting IFs under strong noise, the question of how to extract HIs in such conditions remains open.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

Huai,

Gao,

Liu

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

In this paper, the problem of fault prognosis is investigated for linear stochastic systems with intermittent faults (IFs) and strong noise. Different from permanent faults, the degradation state of IFs are related to the fault duration time, leading to difficulties in the first step of fault prognosis, that is, the health indicator (HI) extraction. Generally, HI extraction of IFs is achieved by fault detection techniques. Therefore, the proposed fault prognosis approach mainly covers the following steps. First, the HI and degradation model are introduced for linear stochastic systems with IFs. Then, considering dynamic characteristic of systems and influence of strong noise, an IF detection algorithm under strong noise is proposed using weighted moving average methods to extract the HI. Moreover, an alternative HI extraction approach is given by an HI estimation way, which avoids using fault detection techniques. Subsequently, the degradation model is fitted by virtue of HIs and the least square method, and the remaining useful life is predicted with the aid of the obtained model. Finally, an experiment concerning the rotary steerable drilling tool system is provided to verify the effectiveness of the derived results.

show abstract

“…It can be found that

\hat{\rho }_{k}

for the ways in refs. [ 28 and 29 ] deviates severely from true

\rho _{k}

. Using the proposed IF detection way, the trend of

\hat{\rho }_{k}

is the same as true HIs.…”

Section: Application To Rul Prediction On Drilling Tool Systemmentioning

confidence: 98%

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

Huai,

Gao,

Liu

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Piecewise argument, unifying the advanced and hysteretic arguments [13,21,26], is one of the nonlinear non-smooth actuators that play an important role in the operation of a nonlinear system [2,[22][23][24][25]. For example, in order to describe the stationary state of the wire length temperature, the nonlinear dynamic model with deviation parameters may be used for better fitting.…”

Section: Introductionmentioning

confidence: 99%

Robust Exponential Stability of Recurrent Neural Networks with Deviating Argument and Stochastic Disturbance

Mingxin¹,

Si²,

Xie³

2020

Eur. J. Pure Appl. Math.

View full text Add to dashboard Cite

It is well known that deviating argument and stochastic disturbance may derail the stability of recurrent neural networks (RNNs). This paper discusses the robustness of global exponential stability (GES) of RNNs accompanied with deviating argument and stochastic disturbance. For a given global exponentially stable RNNs, it is interesting to know how much the length of the interval of piecewise function and the interference intensity so that the disturbed system may still be exponentially stable. The available upper boundary of the range of piecewise variables and the interference intensity in the disturbed RNNs to keep GES are the solutions of some transcendental equations. Finally, some examples are provided to demonstrate the efficacy of the inferential results.

show abstract

Intermittent fault detection for linear discrete-time stochastic multi-agent systems

Sheng

Zhang

Gao

2021

Applied Mathematics and Computation

View full text Add to dashboard Cite

Detecting intermittent sensor faults for linear stochastic systems subject to unknown disturbance

Cited by 19 publications

References 23 publications

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

Robust Exponential Stability of Recurrent Neural Networks with Deviating Argument and Stochastic Disturbance

Intermittent fault detection for linear discrete-time stochastic multi-agent systems

Contact Info

Product

Resources

About