Failure Prognosis and Applications—A Survey of Recent Literature

Kordestani, Mojtaba; Saif, Mehrdad; Orchard, Marcos E.; Razavi–Far, Roozbeh; Khorasani, K.

doi:10.1109/tr.2019.2930195

Cited by 224 publications

(89 citation statements)

References 213 publications

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“…A multiscale CNN has been shown to provide better predictions for other applications in engineering systems compared to the CNN [37]. Therefore, in future work, it is suggested to implement this algorithm for DRM and make a comparison with the CNN performance.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Ortíz

Tarchi

Martínez

et al. 2021

IEEE Systems Journal

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Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.

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Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Ortíz

Tarchi

Martínez

et al. 2021

IEEE Systems Journal

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“…Compared with the traditional RNN, LSTM introduces a specially designed unit that can accurately control the hidden state information flow from one time step to another [37]- [39]. The structure of the LSTM is shown in Figure 13.…”

Section: B Lstm Algorithmmentioning

confidence: 99%

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Zuo

et al. 2020

IEEE Access

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Insulated conductors can improve the stability of power transmission and reduce the construction space compared with traditional bare conductors. Therefore, insulated conductors are used more and more in overhead power transmission. However, a major challenge of using insulated overhead conductors (IOC) is that the ordinary protection devices are not able to detect the phase-to-ground faults and something, such as tree branch, hitting conductor events. This may cause an accident such as power failure or electrical fire and result in serious damage. In this paper, a new approach, which is based on Discrete Wavelet Transform (DWT) and Long Short Term Memory network (LSTM) for detecting of IOC fault according to partial discharge, is presented. Firstly, the original signal is denoised by DWT. Secondly, the denoised signal is decomposed and extracted features on different layers by DWT. Finally, IOC fault is detected by LSTM. This method can improve the detection accuracy of IOC fault which is tested on the ENET public data set and compared with other classification methods. INDEX TERMS Insulated overhead conductors, partial discharge, discrete wavelet transform, long short term memory network, fault detection.

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“…Fusion techniques are often categorized into three groups: 1) probabilistic-based fusion methods such as Bayesian reasoning methods [7]- [11], robust statistics methods [12], [13] 2) intelligent-based fusion methods like neural networks [14]- [19], Fuzzy logic [20]- [23], and 3) least square-based fusion methods such as Kalman filtering [24], [25] and OWA operator [26]- [29].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…For this aim, the data of the position sensor (y 1 ) is utilized at each sampling time and Eqs. (7)(8)(9)(10)(11) are recursively computed to observe the target position (x 1 ). Furthermore, Eqs.…”

Section: Estimation Based On Kalman Filter With the Highest Samplimentioning

confidence: 99%

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

et al. 2020

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A new fusion strategy is introduced in this article to estimate state for multi-rate multisensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology. INDEX TERMS Multi-rate Kalman filter, estimation, sensor data fusion, missing measurements, OWA operator.

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Failure Prognosis and Applications—A Survey of Recent Literature

Cited by 224 publications

References 213 publications

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

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