The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics cause...The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.展开更多
Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that...Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that maps inputs directly to outputs, making it less difficult to optimize. In this paper, we incorporate differential information into the original residual block to improve the representative ability of the ResNet, allowing the modified network to capture more complex and metaphysical features. The proposed DFNet preserves the features after each convolutional operation in the residual block, and combines the feature maps of different levels of abstraction through the differential information. To verify the effectiveness of DFNet on image recognition, we select six distinct classification datasets. The experimental results show that our proposed DFNet has better performance and generalization ability than other state-of-the-art variants of ResNet in terms of classification accuracy and other statistical analysis.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary image of zip code box and message of the two characte...We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary image of zip code box and message of the two characters binary image; analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.展开更多
This series of papers deals with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. This paper is the last in the series. It deals with the ...This series of papers deals with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. This paper is the last in the series. It deals with the application of fuzzy neural network to the recognition of targets. The neural network is a multi-layered forward network and the learning algorithm is BP (error Back Propagation). In the paper, the adust formula of parameter of fuzzier is given. The paper provides a recognition result which is drawn from 1049 samples gathered from 41 vessels in 63 operating conditions, with an original recording time of about 3.5 hours. The identifications are more than 92% correct.展开更多
For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by th...For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.展开更多
The Lower Ganchaigou Formation in the Yingxi area of the Qaidam Basin is a typical lacustrine mixed rock reservoir in western China.It is characterized by strong interlayer heterogeneity,development of diverse lithofa...The Lower Ganchaigou Formation in the Yingxi area of the Qaidam Basin is a typical lacustrine mixed rock reservoir in western China.It is characterized by strong interlayer heterogeneity,development of diverse lithofacies types,and complex response features in logging curves.These complexities make lithofacies identification of the Ganchaigou Formation particularly challenging for non-coring wells,demanding a more efficient and accurate approach.Based on lithology and structural patterns,a lithofacies classification scheme was established.Three intelligent logging identification methods based on improved long short-term memory(LSTM)networks were constructed for lithofacies identification.The accuracy of these methods was evaluated,and the most suitable intelligent logging identification method for the reservoir lithofacies in the Yingxi area was selected.In the Upper Xiaganchaigou Formation(E_(3)^(2) section)of the Yingxi area,a total of eight lithofacies types were identified:laminated lime-dolostone,stratified lime-dolostone,laminated dolostonelime,stratified dolostone-lime,laminated lime-dolomitic shale,massive mudstone,sandstone,and gypsum.The overall recognition accuracies of the LSTM,Bi-LSTM,and Attention-based Bi-LSTM intelligent identification models are 81%,85%,and 87%,respectively.The overall recognition accuracies of the three intelligent algorithms are relatively high,with the Attention-based Bi-LSTM model achieving the highest accuracy.This model demonstrates superior applicability for intelligent lithofacies identification in lacustrine mixed rock reservoirs,particularly those dominated by carbonates in the Yingxi area.It effectively interprets the lithofacies types of non-coring wells in the study area and provides a valuable reference for interpreting lithofacies logs in similar depositional environments.展开更多
基金Supported by National Natural Science Foundation of China (Grant No.11972129)National Science and Technology Major Project of China (Grant No.2017-IV-0008-0045)+1 种基金Heilongjiang Provincial Natural Science Foundation (Grant No.YQ2022A008)the Fundamental Research Funds for the Central Universities。
文摘The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI under Grant JP22H03643Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)under Grant JPMJSP2145JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115.
文摘Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that maps inputs directly to outputs, making it less difficult to optimize. In this paper, we incorporate differential information into the original residual block to improve the representative ability of the ResNet, allowing the modified network to capture more complex and metaphysical features. The proposed DFNet preserves the features after each convolutional operation in the residual block, and combines the feature maps of different levels of abstraction through the differential information. To verify the effectiveness of DFNet on image recognition, we select six distinct classification datasets. The experimental results show that our proposed DFNet has better performance and generalization ability than other state-of-the-art variants of ResNet in terms of classification accuracy and other statistical analysis.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
文摘We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary image of zip code box and message of the two characters binary image; analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.
文摘This series of papers deals with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. This paper is the last in the series. It deals with the application of fuzzy neural network to the recognition of targets. The neural network is a multi-layered forward network and the learning algorithm is BP (error Back Propagation). In the paper, the adust formula of parameter of fuzzier is given. The paper provides a recognition result which is drawn from 1049 samples gathered from 41 vessels in 63 operating conditions, with an original recording time of about 3.5 hours. The identifications are more than 92% correct.
文摘For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.
基金supported by the the National Natural Science Foundation of China(No.42272186,42302128,42202109 and 42472179)
文摘The Lower Ganchaigou Formation in the Yingxi area of the Qaidam Basin is a typical lacustrine mixed rock reservoir in western China.It is characterized by strong interlayer heterogeneity,development of diverse lithofacies types,and complex response features in logging curves.These complexities make lithofacies identification of the Ganchaigou Formation particularly challenging for non-coring wells,demanding a more efficient and accurate approach.Based on lithology and structural patterns,a lithofacies classification scheme was established.Three intelligent logging identification methods based on improved long short-term memory(LSTM)networks were constructed for lithofacies identification.The accuracy of these methods was evaluated,and the most suitable intelligent logging identification method for the reservoir lithofacies in the Yingxi area was selected.In the Upper Xiaganchaigou Formation(E_(3)^(2) section)of the Yingxi area,a total of eight lithofacies types were identified:laminated lime-dolostone,stratified lime-dolostone,laminated dolostonelime,stratified dolostone-lime,laminated lime-dolomitic shale,massive mudstone,sandstone,and gypsum.The overall recognition accuracies of the LSTM,Bi-LSTM,and Attention-based Bi-LSTM intelligent identification models are 81%,85%,and 87%,respectively.The overall recognition accuracies of the three intelligent algorithms are relatively high,with the Attention-based Bi-LSTM model achieving the highest accuracy.This model demonstrates superior applicability for intelligent lithofacies identification in lacustrine mixed rock reservoirs,particularly those dominated by carbonates in the Yingxi area.It effectively interprets the lithofacies types of non-coring wells in the study area and provides a valuable reference for interpreting lithofacies logs in similar depositional environments.
