scholarly journals An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jie Cao ◽  
Zhidong He ◽  
Jinhua Wang ◽  
Ping Yu
Keyword(s):  
Activation Function ◽  
Vibration Sensor ◽  
Training Set ◽  
Convolutional Network ◽  
Convolution Structure ◽  
Diagnosis Method ◽  
Intense Noise ◽  
Comparison Algorithms ◽  
Classification Information ◽  
Improved Methods

The bearing state signal collected by the vibration sensor contains a large amount of environmental noise in actual processes, which leads to a reduction in the accuracy of the convolutional network in identifying bearing faults. To solve this problem, a one-dimensional convolutional neural network with a multiscale kernel (MSK-1DCNN) is proposed for the classification information enhancement of the input. A two-layer multiscale convolution structure (MSK) is used at the front of the network. MSK has five convolutional kernels with different sizes, and those kernels are used to extract features with varying resolutions in the original signal. In the multiscale convolution structure, the ELU activation function is used instead of the ReLU function to improve the antinoise ability of MSK-1DCNN, also by adding pepper noise to the training set data to destroy the input data and forcing the network to learn more representative features to improve the robustness of the network. Experimental results illustrate that the improved methods proposed in this paper effectively enhance the diagnostic performance of MSK-1DCNN under intense noise, and the diagnostic accuracy is higher than that of other comparison algorithms.

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 Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

2020 ◽  
Vol 10 (6) ◽  
pp. 2104
Author(s):  
Michał Tomaszewski ◽  
Paweł Michalski ◽  
Jakub Osuchowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Detection Efficiency ◽  
Training Data ◽  
Training Dataset ◽  
Training Set ◽  
Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

White Blood Cells Classification with Deep Convolutional Neural Networks

2018 ◽  
Vol 32 (09) ◽  
pp. 1857006 ◽  
Author(s):  
Ming Jiang ◽  
Liu Cheng ◽  
Feiwei Qin ◽  
Lian Du ◽  
Min Zhang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Network Architecture ◽  
White Blood Cells ◽  
Activation Function ◽  
Training Set ◽  
Deep Convolutional Neural Networks ◽  
Cell Image ◽  
Attending Physician

The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending physician to have a wealth of clinical experience. Now the deep learning is very suitable for the study of image recognition classification, and the effect is not good enough to directly use some famous convolution neural network (CNN) models, such as AlexNet model, GoogleNet model, and VGGFace model. In this paper, we construct a new CNN model called WBCNet model that can fully extract features of the microscopic white blood cell image by combining batch normalization algorithm, residual convolution architecture, and improved activation function. WBCNet model has 33 layers of network architecture, whose speed has greatly been improved compared with the traditional CNN model in training period, and it can quickly identify the category of white blood cell images. The accuracy rate is 77.65% for Top-1 and 98.65% for Top-5 on the training set, while 83% for Top-1 on the test set. This study can help doctors diagnose leukemia, and reduce misdiagnosis rate.

scholarly journals Multi-Pig Part Detection and Association with a Fully-Convolutional Network

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 852 ◽  
Author(s):  
Eric Psota ◽  
Mateusz Mittek ◽  
Lance Pérez ◽  
Ty Schmidt ◽  
Benny Mote
Keyword(s):  
Body Part ◽  
Training Set ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Non Invasive ◽  
Significant Challenge ◽  
Lighting Conditions ◽  
Computer Vision Systems ◽  
Public Datasets ◽  
Private Datasets

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download.

scholarly journals A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Guifang Liu ◽  
Huaiqian Bao ◽  
Baokun Han
Keyword(s):  
Fault Diagnosis ◽  
Manufacturing Industry ◽  
Activation Function ◽  
Data Driven ◽  
Training Process ◽  
Training Set ◽  
Analysis Techniques ◽  
Stacked Autoencoders ◽  
Modern Manufacturing ◽  
Machinery Fault Diagnosis

Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function are introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.

scholarly journals Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation

2020 ◽  
Vol 10 (10) ◽  
pp. 3658
Author(s):  
Karshiev Sanjar ◽  
Olimov Bekhzod ◽  
Jaeil Kim ◽  
Jaesoo Kim ◽  
Anand Paul ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Interpolation Method ◽  
Medical Personnel ◽  
Activation Function ◽  
Lesion Segmentation ◽  
Bilinear Interpolation ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Computer Vision Applications ◽  
Parametric Rectified Linear Unit

The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are proposed with a modified U-Net, in which a bilinear interpolation method is used for upsampling with a block of convolution layers followed by parametric rectified linear-unit non-linearity. To avoid overfitting, a dropout is applied after each convolution block. The results demonstrate that our recommended technique achieves state-of-the-art performance for skin-lesion segmentation with 94% pixel accuracy and a 88% dice coefficient, respectively.

Training a Single Sigmoidal Neuron Is Hard

2002 ◽  
Vol 14 (11) ◽  
pp. 2709-2728 ◽  
Author(s):  
Jiří Šíma
Keyword(s):  
Single Neuron ◽  
Learning Algorithm ◽  
Activation Function ◽  
Weighted Sum ◽  
Negative Consequences ◽  
Training Set ◽  
Constructive Learning ◽  
Feed Forward Neural Networks ◽  
Training Error ◽  
Sigmoidal Activation Function

We first present a brief survey of hardness results for training feed forward neural networks. These results are then completed by the proof that the simplest architecture containing only a single neuron that applies a sigmoidal activation function σ:K→ [α, β], satisfying certain natural axioms (e.g., the standard (logistic) sigmoid or saturated-linear function), to the weighted sum of n inputs is hard to train. In particular, the problem of finding the weights of such a unit that minimize the quadratic training error within (β—α)2 or its average (over a training set) within—5(β—α)2 /(12n) of its infimum proves to be NP-hard. Hence, the well-known backpropagation learning algorithm appears not to be efficient even for one neuron, which has negative consequences in constructive learning.

scholarly journals A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

2021 ◽  
Vol 11 (12) ◽  
pp. 5410
Author(s):  
Ke Zheng ◽  
Guozhu Jia ◽  
Linchao Yang ◽  
Jiaqing Wang
Keyword(s):  
Flight Test ◽  
Gradient Boosting ◽  
Learning Needs ◽  
Limit States ◽  
Convolutional Network ◽  
Flight Data ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
Diagnosis Method ◽  
Test Flight

In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.

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