One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills.

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Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

Remote Sensing ◽

10.3390/rs13030335 ◽

2021 ◽

Vol 13 (3) ◽

pp. 335

Author(s):

Yuhao Qing ◽

Wenyi Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Principal Component ◽

Hyperspectral Image Classification ◽

Deep Network ◽

Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

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Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

Engineering Research Express ◽

10.1088/2631-8695/ac4834 ◽

2022 ◽

Author(s):

Canyi Du ◽

Rui Zhong ◽

Yishen Zhuo ◽

Xinyu Zhang ◽

Feifei Yu ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Pattern Recognition Method ◽

Recognition Method ◽

Vibration Signals ◽

One Dimensional ◽

Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

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Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

Remote Sensing ◽

10.3390/rs11091006 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1006 ◽

Cited By ~ 16

Author(s):

Quanlong Feng ◽

Jianyu Yang ◽

Dehai Zhu ◽

Jiantao Liu ◽

Hao Guo ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Land Cover ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Yellow River ◽

Rapid Development ◽

Land Cover Classification ◽

Coastal Land

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

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Deep learning application for box-office evaluation of images

Computer Optics ◽

10.18287/2412-6179-co-515 ◽

2020 ◽

Vol 44 (1) ◽

pp. 127-132

Author(s):

V.G. Efremtsev ◽

N.G. Efremtsev ◽

E.P. Teterin ◽

P.E. Teterin ◽

V.V. Gantsovsky

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sample Preparation ◽

Convolutional Neural Network ◽

Preliminary Data ◽

Classification Accuracy ◽

Training Sample ◽

Data Preparation ◽

Box Office ◽

The Neural Network

The possibility of application a convolutional neural network to assess the box-office effect of digital images is reviewed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%.

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Four-Dimension Deep Learning Method for Flower Quality Grading with Depth Information

Electronics ◽

10.3390/electronics10192353 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2353

Author(s):

Xinyan Sun ◽

Zhenye Li ◽

Tingting Zhu ◽

Chao Ni

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Network Models ◽

Depth Image ◽

Depth Information ◽

Neural Network Models ◽

Flower Bud ◽

Flower Quality

Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstly, the RGB image and the depth image of a flower bud were collected and transformed into fused RGBD information. Then, the RGBD information of a flower was set as inputs of a convolutional neural network to determine the flower bud maturing status. Four convolutional neural network models (VGG16, ResNet18, MobileNetV2, and InceptionV3) were adjusted for a four-dimensional (4D) RGBD input to classify flowers, and their classification performances were compared with and without depth information. The experimental results show that the classification accuracy was improved with depth information, and the improved InceptionV3 network with RGBD achieved the highest classification accuracy (up to 98%), which means that the depth information can effectively reflect the characteristics of the flower bud and is helpful for the classification of the maturing status. These results have a certain significance for the intelligent classification and sorting of fresh flowers.

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Gas pipeline event classification based on one-dimensional convolutional neural network

Structural Health Monitoring ◽

10.1177/14759217211010270 ◽

2021 ◽

pp. 147592172110102

Author(s):

Yang An ◽

Xueyan Ma ◽

Xiaocen Wang ◽

Zhigang Qu ◽

Xixin Zhu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Short Term Memory ◽

Gas Pipeline ◽

Economic Losses ◽

Short Term ◽

Term Memory ◽

One Dimensional ◽

Memory Network

Pipeline block and pipeline leak may lead to serious accidents and cause huge economic losses, which have been urgent problems for gas transportation. In this work, active acoustic pulse-compression technology is first introduced to detect and locate these two anomalous events. The matched filtered signals are then normalized and input into one-dimensional convolutional neural network to achieve classification of not only pipeline block and pipeline leak but also normal event such as pipeline elbow which causes acoustic wave reflection as well. Neural network parameter optimization has also been carried out as well as the comparison with long- and short-term memory network. Experimental results demonstrate that compared with long- and short-term memory network, one-dimensional convolutional neural network has an improvement in efficiency due to the great reduction of running time. For non-aliasing pipeline events, both of the models can reach 100% classification accuracy. For aliasing pipeline events, despite the shorter time series and fewer features, the classification accuracy of one-dimensional convolutional neural network still reaches 100.00%, but that of long- and short-term memory network is only 93.89%. Furthermore, the smoothing and slight fluctuation of receiver operating characteristic curve and the high value of area under curve also verify the stability and good classification performance of the proposed trained model. Therefore, the one-dimensional convolutional neural network shows significant performance for pipeline events classification and has considerable potential and application prospect in gas pipeline safety monitoring.

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Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010028 ◽

2019 ◽

Vol 8 (1) ◽

pp. 28 ◽

Cited By ~ 12

Author(s):

Quanlong Feng ◽

Dehai Zhu ◽

Jianyu Yang ◽

Baoguo Li

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Land Use ◽

Deep Learning ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Urban Land ◽

Urban Land Use ◽

Lidar Data ◽

Adaptive Fusion

Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on "Squeeze-and-Excitation Networks"). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.

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Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026819500056 ◽

2019 ◽

Vol 18 (01) ◽

pp. 1950005 ◽

Cited By ~ 3

Author(s):

Amine Chemchem ◽

François Alin ◽

Michael Krajecki

Keyword(s):

Neural Network ◽

Deep Learning ◽

Comparative Study ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Rule Base ◽

Learning Method ◽

Cognitive Agent ◽

Agent Intelligence ◽

Knowledge Classification

In this paper, a new idea is developed for improving the agent intelligence. In fact with the presented convolutional neural network (CNN) approach for knowledge classification, the agent will be able to manage its knowledge. This new concept allows the agent to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively. In addition, through this research, we developed a comparative study between the proposed CNN approach and the classical classification approaches. As foreseeable the deep learning method outperforms the others in term of classification accuracy.

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Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Sensors ◽

10.3390/s21113608 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3608

Author(s):

Chiao-Sheng Wang ◽

I-Hsi Kao ◽

Jau-Woei Perng

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Permanent Magnet ◽

Support Vector ◽

One Dimensional ◽

Proposed Model ◽

Wide Range ◽

Fault State

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

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Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning

Applied Engineering in Agriculture ◽

10.13031/aea.13794 ◽

2020 ◽

Vol 36 (5) ◽

pp. 743-749

Author(s):

Xingwang Li ◽

Xiaofei Fan ◽

Lili Zhao ◽

Sheng Huang ◽

Yi He ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Multispectral Imaging ◽

Classification Model ◽

One Dimensional ◽

Pepper Seed ◽

Better Than

HighlightsThis study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN).Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM.In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement.Abstract. When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification.

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