Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network

Guo, Chengjun; Li, Feng; Zhong, Tian; Guo, Wei; Tan, Shusen

doi:10.1007/s00521-018-03975-z

Cited by 15 publications

(14 citation statements)

References 18 publications

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“…Autonomous Integrity Monitoring by Extrapolation [10], Innovation-Based (IB) method [11], Residual-Based (RB) method [11], Solution Separation (SS) method [12,13], Quality Control (QC) [14], Generalized Likelihood Ratio (GLR) [15], Extended RAIM (ERAIM) [16], and Rate Detector (RD) method [17] are the most representative hypothesis-test FDE algorithms. Recently, some novel methods have been proposed, including Support Vector Machine (SVM) approach [18], neural network approach [19], robust estimator approaches [20][21][22] and nonlinear filter approaches [23,24]. However, these novel methods aim to improve the performance of FD in terms of time-to-detect, but make it very difficult to evaluate the integrity risk (i.e., the probability of hazardously misleading information).…”

Section: Of 21mentioning

confidence: 99%

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Wang

Zhan

Zhai

et al. 2020

Sensors

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To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

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Section: Of 21mentioning

confidence: 99%

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Wang

Zhan

Zhai

et al. 2020

Sensors

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“…It should be indicated that the modular of the KF, SIM and MPCA processor, and OC-SVM model can be considered as sub-filters in more integrated navigation systems. 16 Meanwhile, the sub-filter design can be applied in a federal filter structure. The FD module serves as a filter and controls a switch to determine whether the connected sensor is in a good condition or not.…”

Section: Framework Of Hybrid Data-driven Fdmentioning

confidence: 99%

“…In this section, it is intended to apply several OC-SVM FD methods for the navigation system to evaluate the FD efficiency of the HS. To this end, the OC-SVM method based on the phase space reconstruction (PSR + OC-SVM) 16 and the OC-SVM…”

Section: Comparison Study With Hsmentioning

confidence: 99%

A hybrid data-driven fault detection strategy with application to navigation sensors

Yang¹,

Chen²,

Wang³

2020

Measurement and Control

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The integrated navigation system highly relies on the accuracy of measurements of sensors that are susceptible to unknown disturbances. In order to improve the reliability and safety of the navigation system, there is an increasing need for the fault detection of the sensors. In the present study, a hybrid data-driven fault detection strategy is proposed, which is based on residual sequence analysis. Currently, the one-class support vector machine is one of the most popular fault detection methods for navigation systems with many successful cases. Therefore, the one-class support vector machine is combined with time-series similarity measure and modified principal components analysis approaches. The similarity measurement of multi-sequence residuals between a real-time sample and normal condition samples is computed to construct learning features for one-class support vector machine. Similarly, the modified principal components analysis scheme is applied to project residuals onto subspaces and obtain learning features. Moreover, the one-class support vector machine model is applied for abnormal detection if unexpected sensor faults exhibit in measurements and residuals. Finally, experiments are carried out to evaluate the performance of the proposed strategy for abrupt faults and soft faults on navigation sensors. Experimental results show that the hybrid data-driven fault detection strategy can effectively detect these faults with short time delay and high accuracy.

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“…The proposed approach coordinates multiple measurements at different buses while taking system topology into account. The work in Guo et al (2020) proposed an intelligent, active fault-tolerant system based on a deep neural network. The proposed algorithm used the neural network such that when there is no fault the NN trains each sub-filter.…”

Section: Introductionmentioning

confidence: 99%

Toward fault tolerant modelling for SCADA based electricity distribution networks, machine learning approach

Masri

Al-Jabi

2021

PeerJ Computer Science

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Maintaining electrical energy is a crucial issue, especially in developing countries with very limited possibilities and recourses. However, the increasing reliance on electrical appliances generates many challenges for operators to fix any fault optimally within minimum time. Even with numerous researches conducted in this area, very few were interested in minimizing the fault duration, especially in the developing countries with very limited resources. Since decision-making requires enough information within minimum time, the integration of information technology with the existing electrical grids is the most appropriate. In this paper, we propose precise and accurate load redistribution estimation models. While several modeling techniques exist, the proposed modeling techniques in this work are based on machine learning models: multiple linear regression, nonlinear regression, and classifier neural network models. The novelty of this work is it introduces a fault-tolerant approach that relies on machine learning and supervisory control and data acquisition system (SCADA). The purpose of this approach is to help electricity distribution companies to maintain power for the customers and to shorten the fault duration from many hours to the minimum possible time. The work was performed based on real data of smart grids split into zones of about 20 transformers. The models’ input data collected from the sensors allocated in the power grid, make the grid becomes able to redistribute the loads by sufficient strategies. To test and validate the models, two powerful modeling tools were used: MATLAB and Anaconda–Python. The results showed an accuracy of about 97% with a standard deviation of 2.3%. The load redistribution was also presented in details. With such eager results, they approve the validity of our model in minimizing the fault duration, by helping the system in taking ideal actions within the optimal time.

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Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network

Cited by 15 publications

References 18 publications

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

A hybrid data-driven fault detection strategy with application to navigation sensors

Toward fault tolerant modelling for SCADA based electricity distribution networks, machine learning approach

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