scholarly journals Method of gear fault diagnosis based on EEMD and improved Elman neural network

2017 ◽  
Author(s):  
Qi Zhang ◽  
Wei Zhao ◽  
Shungen Xiao ◽  
Mengmeng Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Elman Neural Network ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2683-2687 ◽  
Author(s):  
Chen Jiang ◽  
Xue Tao Weng ◽  
Jing Jun Lou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

The gear fault diagnosis system is proposed based on harmonic wavelet packet transform (WPT) and BP neural network techniques. The WPT is a well-known signal processing technique for fault detection and identification in mechanical system，In the preprocessing of vibration signals, WPT coefficients are used for evaluating their energy and treated as the features to distinguish the fault conditions.In the experimental work, the harmonic wavelets are used as mother wavelets to build and perform the proposed WPT technique. The experimental results showed that the proposed system achieved an average classification accuracy of over 95% for various gear working conditions.

scholarly journals Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiakai Ding ◽  
Dongming Xiao ◽  
Liangpei Huang ◽  
Xuejun Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hopfield Neural Network ◽  
Sample Entropy ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

scholarly journals New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091659 ◽  
Author(s):  
Adel Afia ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz ◽  
Boualem Merainani ◽  
Semcheddine Fedala
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Discrete Wavelet Packet Transform ◽  
Radial Basis ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all cases.

scholarly journals Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network

2019 ◽  
Vol 9 (24) ◽  
pp. 5424 ◽  
Author(s):  
Dongming Xiao ◽  
Jiakai Ding ◽  
Xuejun Li ◽  
Liangpei Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Characteristic Curve ◽  
Vibration Signal ◽  
Good Effect ◽  
Feature Vectors ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Gear Fault Diagnosis

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

scholarly journals Gear Fault Diagnosis through Vibration and Acoustic Signal Combination Based on Convolutional Neural Network

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 266
Author(s):  
Liya Yu ◽  
Xuemei Yao ◽  
Jing Yang ◽  
Chuanjiang Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Working Conditions ◽  
Evidence Theory ◽  
Mechanical Equipment ◽  
Measuring Device ◽  
Gear Fault ◽  
Fusion Methods ◽  
Gear Fault Diagnosis

Equipment condition monitoring and diagnosis is an important means to detect and eliminate mechanical faults in real time, thereby ensuring safe and reliable operation of equipment. This traditional method uses contact measurement vibration signals to perform fault diagnosis. However, a special environment of high temperature and high corrosion in the industrial field exists. Industrial needs cannot be met through measurement. Mechanical equipment with complex working conditions has various types of faults and different fault characterizations. The sound signal of the microphone non-contact measuring device can effectively adapt to the complex environment and also reflect the operating state of the device. For the same workpiece, if it can simultaneously collect its vibration and sound signals, the two complement each other, which is beneficial for fault diagnosis. One of the limitations of the signal source and sensor is the difficulty in assessing the gear state under different working conditions. This study proposes a method based on improved evidence theory method (IDS theory), which uses convolutional neural network to combine vibration and sound signals to realize gear fault diagnosis. Experimental results show that our fusion method based on IDS theory obtains a more accurate and reliable diagnostic rate than the other fusion methods.

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