A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Hu, Sinian; Xiao, Han; Yi, Cancan

doi:10.1155/2018/7045127

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…ese attacks forced by power constrained pulse widthmodulated jammers are supposed to be moderately recognized, which is jammer period and unchanging inferior destined and jammers asleep periods are identified. Controller synthesis problem that is an event based for network control systems and strong event triggered communication scheme was studied in [27]. In conclusion, piecewise Lyapunov function is applied to guarantee exponential stability of the system.…”

Section: Dos Attackmentioning

confidence: 99%

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

Aslam

Ahmed

et al. 2022

Computational Intelligence and Neuroscience

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Consensus control of multiagent systems (MASs) has been one of the most extensive research topics in the field of robotics and automation. The information sharing among the agents in the MASs depends upon the communication network because the interaction of agents may affect the consensus performance of the agents in a communication network. An unexpected fault and attack may occur on one agent and can propagate through the communication network into other agents. Thus, this may cause severe degradation of the whole MASs. In this paper, we first discussed MAS technologies. After that available technologies for the modeling of attacks and fundamental issues due to attacks on MAS attacks were discussed. We also introduced cooperative attack methodologies and model-based attack methodology. Objective of this article is to provide comprehensive study on recent advances in consensus control of MASs under attacks covering the published results until 2021. This survey presents different kinds of attacks, their estimation and detection, and resilient control against attacks. At the end, the survey accomplishes some potential recommendations for future direction to solve the key issues and challenges reported for secure consensus control of MASs.

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Section: Dos Attackmentioning

confidence: 99%

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

Aslam

Ahmed

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Current fault diagnosis methods can be generally divided into two categories. First, fault diagnosis methods based on traditional signal processing methods, such as wavelet transform (WT) [9][10][11], empirical mode decomposition (EMD) [12,13], variational mode decomposition (VMD) [14], bandpass filter [15,16], and filter bank [17,18]. Second, datadriven methods based on deep learning [19], such as convolutional neural networks (CNNs) [20], deformable convolution networks (DCNs) [21], deep auto-encoders [22], and stacked denoising auto-encoders [23].…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on inverted Mel-scale frequency cepstral coefficients and deformable convolution networks

Zhao¹,

baofu²,

yuhang³

et al. 2023

Meas. Sci. Technol.

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In the online test of bearing fault diagnosis algorithm based on data-driven, it is difficult to guarantee that the training and testing data come from the same time series distribution. Inconsistent distribution will lead to a decline in diagnostic performance. In addition, the convolutional neural network is limited by the fixed shape of its convolution kernel, which makes it difficult to fully extract the spatial constraint features between fault data. To solve the above problems, this paper proposes a bearing fault diagnosis method based on inverted Mel-scale frequency cepstrum coefficients and deformable convolution networks. The core of traditional Mel-scale frequency cepstrum coefficients is to construct a non-uniformly distributed frequency domain filter bank. It is characterized by the dense distribution of low-frequency regions and the sparse distribution of high-frequency regions. Considering that the features that can well characterize the fault information are concentrated in the high-frequency part, we reconstruct the traditional Mel-scale frequency cepstrum coefficients filter bank and propose a feature extraction method of inverted Mel-scale frequency cepstrum coefficients. This method can obtain the frequency domain characteristics of bearing vibration signals, highlight the fault information contained in the high-frequency region, and reduce the influence of time series distribution inconsistency between training samples and testing samples on the diagnosis accuracy. In order to further improve the spatial discrimination between different fault categories, the deformable convolution networks model is introduced to extract the spatial distribution information of fault features and improve the accuracy of fault diagnosis. Finally, two public datasets and an experimental platform data verify that the method can achieve high-precision fault diagnosis, and inverted Mel-scale Frequency cepstrum coefficients are effective in solving the problem of inconsistent distribution.

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“…In the case of fewer learning samples, the SVM classification method has stronger adaptability, better classification ability and higher computational efficiency than the neural network classification method [10,11]. Xu Yuxiu et al [12] extracted the time-domain energy value of the vehicle engine vibration signal according to the cycle of the crankshaft angle, and used SVM to classify the fault features.…”

Section: Introductionmentioning

confidence: 99%

Research on the application of artificial intelligence method in automobile engine fault diagnosis

Wen

Rong

et al. 2021

Eng. Res. Express

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The application of artificial intelligence methods in fault diagnosis is becoming more and more extensive, and exploring and researching intelligent fault diagnosis methods for automobile engines is also a hot spot in the field of automotive engineering. Different machine learning methods have their own advantages and disadvantages. By extracting different characteristic parameters and optimizing the combination of multiple algorithms, faster and stable diagnosis results can be achieved, so that faults can be discovered and repaired in time. Aiming at the potential fluctuation and impact characteristics of vibration plus signal caused by different failure states of automobile engines, this paper proposes two engine fault identification methods using vibration acceleration signals as diagnostic parameters. They are Cross Validation -Support Vector Machine (CV-SVM)and Particle Swarm Optimization-Probabilistic Neural Network (PSO-PNN) engine fault identification methods. The advantages and disadvantages of the two methods are compared and analyzed. Obtain the amplitude corresponding to the frequency multiplication of the vibration acceleration signal through the spectrum analysis method, which is used as the main component of the input feature vector, and establish the SVM fault diagnosis model combined with the cross-validation method (CV); In addition, multiple one-dimensional arrays composed of time-domain signals are directly used as input feature vectors, and the particle swarm optimization (PSO) parameter optimization is used to obtain the best Probabilistic Neural Network(PNN) fault diagnosis model. The results show that both the CV-SVM (small sample) method and the PSO-PNN method (large sample) can realize the identification and diagnosis of the established engine fault type. The CV-SVM method has more advantages in diagnostic fault tolerance, but the PSO-PNN method can simplify the process of feature sample preparation, save a lot of manual feature extraction tasks, and is more convenient in practical application.

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A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Cited by 10 publications

References 32 publications

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

Bearing fault diagnosis based on inverted Mel-scale frequency cepstral coefficients and deformable convolution networks

Research on the application of artificial intelligence method in automobile engine fault diagnosis

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