scholarly journals An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 133
Author(s):  
Pu Yang ◽  
Huilin Geng ◽  
Chenwan Wen ◽  
Peng Liu
Keyword(s):  
Fault Diagnosis ◽  
Window Method ◽  
Signal Features ◽  
Fault Features ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Sliding Window Method ◽  
Diagnosis Algorithm ◽  
Soft Threshold ◽  
Fitting Ability

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.

scholarly journals An Anti-Noise Fault Diagnosis Method of Bearing based on Multi-Scale 1DCNN

2020 ◽  
Author(s):  
Jie Cao ◽  
Zhidong He ◽  
Jinhua Wang ◽  
Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Activation Function ◽  
Diagnostic Model ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Features ◽  
Convolution Structure ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Comparison Algorithms

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

scholarly journals Graph-domain features and their application in rotating machinery fault diagnosis

2021 ◽  
Vol 1207 (1) ◽  
pp. 012008
Author(s):  
Yiyuan Gao ◽  
Wenliao Du ◽  
Xiaoyun Gong ◽  
Dejie Yu
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Mechanical Vibration ◽  
Rotating Machinery ◽  
Support Vector ◽  
Fisher Score ◽  
Fault Features ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

Abstract To more effectively extract the non-stationary and non-linear fault features of mechanical vibration signals, a novel fault diagnosis method for rotating machinery is proposed combining time-domain, frequency-domain with graph-domain features. Different from the conventional time-domain and frequency-domain features, the graph-domain features generated from horizontal visibility graphs can extract the fault information hidden in the graph topology. Aiming at the problem that too many features will lead to information redundancy, the Fisher score algorithm is applied to select several of sensitive features which are then fed into the support vector machine to diagnose the faults of rotating machinery. Experimental results indicate features extracted from the three domains can be used to obtain higher diagnosis accuracy than that extracted from any single domain or dual domains.

Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

The Application of Improved SVM in Mixed Circuit

2012 ◽  
Vol 224 ◽  
pp. 493-496 ◽  
Author(s):  
Huai Long Wang ◽  
Qiang Pan ◽  
Hong Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Lifting Wavelet Transform ◽  
Fault Features ◽  
Diagnosis Method ◽  
Lifting Wavelet ◽  
Current Characteristics ◽  
Mixed Circuit ◽  
Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

Full Information Fusion and its Application in Fault Diagnosis for Rotary Machinery

2011 ◽  
Vol 66-68 ◽  
pp. 1315-1319 ◽  
Author(s):  
Xin Min Dong ◽  
Jie Han ◽  
Wang Shen Hao
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Engineering Practice ◽  
Full Information ◽  
Single Section ◽  
Rotary Machinery ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

The rotor motion and the information fusion of single section were discussed; the fault diagnosis method for rotary machinery based on the full information fusion of two sections was put forward, and the back propagation neural network model was established. Engineering practice indicated that the fault diagnosis accuracy based on the information fusion of two sections was higher than that based on the information fusion of single section.

scholarly journals End-to-end diagnosis of cloud systems against intermittent faults

2020 ◽  
pp. 40-40
Author(s):  
Chao Wang ◽  
Zhongchuan Fu ◽  
Yanyan Huo
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Computing Environment ◽  
Bp Network ◽  
Cloud Computing Environment ◽  
Intermittent Faults ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
End To End ◽  
Detection Mechanisms

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

scholarly journals Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yong Yan ◽  
Qiang Liu ◽  
Xiao qin Gao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Attention Mechanism ◽  
Time Dimension ◽  
High Frequency Signal ◽  
Diagnosis Accuracy ◽  
Diagnosis Algorithm ◽  
Motor Vibration

In order to improve the maintenance efficiency of the motor and realize the real-time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high-frequency signal is denoised to improve the signal-to-noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.

scholarly journals An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1041 ◽  
Author(s):  
Yang Liu ◽  
Lixiang Duan ◽  
Zhuang Yuan ◽  
Ning Wang ◽  
Jianping Zhao
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Vibration Signal ◽  
Intelligent Fault Diagnosis ◽  
Traditional Methods ◽  
Learning Abilities ◽  
Fault Features ◽  
Correlation Criterion ◽  
Long Time ◽  
Diagnosis Method

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

scholarly journals Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

2018 ◽  
Vol 173 ◽  
pp. 03090
Author(s):  
WANG Ying-chen ◽  
DUAN Xiu-sheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Network Application ◽  
Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

scholarly journals Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Changfan Zhang ◽  
Xiang Cheng ◽  
Jianhua Liu ◽  
Jing He ◽  
Guangwei Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Single Layer ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Sparse Autoencoder ◽  
Single Layer Network ◽  
Network Studies ◽  
Fine Tune

The model is difficult to establish because the principle of the locomotive adhesion process is complex. This paper presents a data-driven adhesion status fault diagnosis method based on deep learning theory. The adhesion coefficient and creep speed of a locomotive constitute the characteristic vector. The sparse autoencoder unsupervised learning network studies the input vector, and the single-layer network is superimposed to form a deep neural network. Finally, a small amount of labeled data is used to fine-tune training the entire deep neural network, and the locomotive adhesion state fault diagnosis model is established. Experimental results show that the proposed method can achieve a 99.3% locomotive adhesion state diagnosis accuracy and satisfy actual engineering monitoring requirements.

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