Neural network-based fault detection method for aileron actuator

Yuan, Hang; Lu, Chen; Ma, Jian; Chen, Zi-han

doi:10.1016/j.apm.2015.02.032

Cited by 20 publications

(4 citation statements)

References 17 publications

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“…Here, the features of training dataset I were extracted and utilized to train the classifiers, and the data samples in the testing dataset were employed to validate the methods. Here, traditional TD, FD, and TFD features were extracted, the classical support vector machine (SVM) and radial basis function (RBF) neural network were employed as classifiers [34]. The accuracies are listed in table 11.…”

Section: Comparisons With Conventional Methodsmentioning

confidence: 99%

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

You,

Wu,

et al. 2024

Meas. Sci. Technol.

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Because of electrification condition, key components of battery swapping system (BSS) for electric heavy trucks (EHT) are always damaged by electric erosion, which poses challenges to the safety and efficiency of high-intensity transportation. Due to the special working condition of BSS, the fault diagnosis of its driving gear encounters several issues, including reciprocation motion, low and fluctuating speed, complicated noises. To solve these problems, audio features, including Mel-frequency cepstral coefficients (MFCC) and Gammatone cepstral coefficients (GTCC), are extracted from the vibration signals, then, these features are utilized to construct original dictionary, after that, based on data augmentation and dictionary learning, a robust dictionary is generated from the original dictionary, finally, with the robust dictionary, sparse representation-based classification is integrated into AdaBoost to achieve an accurate fault diagnosis for the driving gear in BSS. The effectiveness of the fault diagnosis scheme is validated based on the monitoring data of BSS, and the accuracy of fault diagnosis is 99.17%.

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Section: Comparisons With Conventional Methodsmentioning

confidence: 99%

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

You,

Wu,

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…Design of a fault observer and an adaptive threshold generator using the method described in [14] is shown in Figure 4. Two GRNNs are utilized in this method.…”

Section: Design Of the Fault Observer And Adaptive Thresholdmentioning

confidence: 99%

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

Song

et al. 2018

Complexity

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A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks (GRNNs) are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network (PNN) acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments.

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“…Specifically, radial basis function (RBF) neural network has relatively high convergence speed and can approximate to any nonlinear functions [8]. It has been proved that RBF neural network is an effective fault observer to generate residual error [7]. The residual error contains a large amount of state information of the system, which can be used to extract features for performance assessment of the system.…”

Section: Introductionmentioning

confidence: 99%

“…In order to evaluate the performance degradation degree of the superheterodyne receiver system, at the first step, a state observer is usually established to estimate the system output [6,7]. Specifically, radial basis function (RBF) neural network has relatively high convergence speed and can approximate to any nonlinear functions [8].…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment and Prediction for Superheterodyne Receivers Based on Mahalanobis Distance and Time Sequence Analysis

Sun

Wang

et al. 2017

International Journal of Antennas and Propagation

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The superheterodyne receiver is a typical device widely used in electronics and information systems. Thus effective performance assessment and prediction for superheterodyne receiver are necessary for its preventative maintenance. A scheme of performance assessment and prediction based on Mahalanobis distance and time sequence analysis is proposed in this paper. First, a state observer based on radial basis function (RBF) neural network is designed to monitor the superheterodyne receiver and generate the estimated output. The residual error can be calculated by the actual and estimated output. Second, time-domain features of the residual error are then extracted; after that, the Mahalanobis distance measurement is utilized to obtain the health confidence value which represents the performance assessment result of the most recent state. Furthermore, an Elman neural network based time sequence analysis approach is adopted to forecast the future performance of the superheterodyne receiver system. The results of simulation experiments demonstrate the robustness and effectiveness of the proposed performance assessment and prediction method.

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Neural network-based fault detection method for aileron actuator

Cited by 20 publications

References 17 publications

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

Performance Assessment and Prediction for Superheterodyne Receivers Based on Mahalanobis Distance and Time Sequence Analysis

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