scholarly journals Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Rodrigo P. Monteiro ◽  
Mariela Cerrada ◽  
Diego R. Cabrera ◽  
René V. Sánchez ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Activation Function ◽  
Economic Losses ◽  
Support Vector ◽  
Training Time ◽  
Decision Stage ◽  
Average Accuracy ◽  
Graphical Processing ◽  
Mechanical Devices

Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical processing units spreads the use of deep learning-based solutions to many problems, which includes the fault diagnosis on gearboxes. Those solutions usually require a significant amount of data, high computational power, and a long training process. The training of deep learning-based systems may not be feasible when GPUs are not available. This paper proposes a solution to reduce the training time of deep learning-based fault diagnosis systems without compromising their accuracy. The solution is based on the use of a decision stage to interpret all the probability outputs of a classifier whose output layer has the softmax activation function. Two classification algorithms were applied to perform the decision. We have reduced the training time by almost 80% without compromising the average accuracy of the fault diagnosis system.

scholarly journals Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1161
Author(s):  
Kuo-Hao Fanchiang ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Monitoring System ◽  
Absolute Difference ◽  
Generative Adversarial Networks ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Full Time ◽  
Noise Interference ◽  
In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

Classification of plant diseases using machine and deep learning

2021 ◽  
Vol 11 (1) ◽  
pp. 491-508
Author(s):  
Monika Lamba ◽  
Yogita Gigras ◽  
Anuradha Dhull
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Early Recognition ◽  
Activation Function ◽  
Plant Diseases ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Proposed Model

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

scholarly journals A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6540
Author(s):  
Qian Pan ◽  
Maofang Gao ◽  
Pingbo Wu ◽  
Jingwen Yan ◽  
Shilei Li
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Recognition Accuracy ◽  
Yellow Rust ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Economic Losses ◽  
Support Vector ◽  
Aerial Vehicle ◽  
Uav Images

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

scholarly journals Implementation of Deep Learning for Slump Optimization Based on Concrete Quality Using Convolutional Neural Network in PT. SELO PROGO SAKTI

2020 ◽  
Vol 5 (2) ◽  
pp. 83-88
Author(s):  
Hedi Pandowo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Experimental Design ◽  
Convolutional Neural Network ◽  
Processing Unit ◽  
Training Process ◽  
Average Accuracy ◽  
Graphical Processing ◽  
Intelligence Science

Deep Learning is part of the scientific field of Machine Learning and Machine Learning is part of Artificial Intelligence science. Deep Learning has extraordinary capabilities by using a hardware Graphical Processing Unit (GPU) so that the artificial requirement network can run faster than using a Personal Computer Unit (CPU). Especially in terms of object classification in images using existing methods in the Convolutional Neural Network (CNN). The method used in this research is Preprocessing and Processing of Input Data, Training Process in which CNN is trained to obtain high accuracy from the classification carried out and the Testing Process which is a classification process using weights and bias from the results of the training process. This type of research is a pre experimental design (pre experimental design). The results of the object image classification test with different levels of confusion in the Concrete database with the Mix Design K-125, K-150, K-250 and K-300 produce an average accuracy value. This is also relevant to measuring the failure rate of concrete or slump

scholarly journals Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

2021 ◽  
Vol 72 (6) ◽  
pp. 374-380
Author(s):  
Bhavinkumar Gajjar ◽  
Hiren Mewada ◽  
Ashwin Patani
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Scene Classification ◽  
Scale Invariant ◽  
Training Time ◽  
Multi Scale ◽  
Non Linear ◽  
Pyramid Matching ◽  
Scale Invariant Feature ◽  
Distinct Features

Abstract Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.

Detection of Internally Bruised Blueberries Using Hyperspectral Transmittance Imaging

2017 ◽  
Vol 60 (5) ◽  
pp. 1489-1502 ◽  
Author(s):  
Mengyun Zhang ◽  
Changying Li ◽  
Fumiomi Takeda ◽  
Fuzeng Yang
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Mechanical Damage ◽  
Color Images ◽  
Economic Losses ◽  
Support Vector ◽  
Svm Classifier ◽  
Average Accuracy ◽  
Fruit Samples ◽  
And Control

Abstract. Internal bruise damage that occurs in blueberry fruit during harvest operations and postharvest handling lowers the overall quality and causes significant economic losses. The main goal of this study was to nondestructively detect internal bruises in blueberries after mechanical damage using hyperspectral transmittance imaging. A total of 600 hand-harvested blueberries were divided into 20 groups of four storage times (30 min, 3 h, 12 h, and 24 h), two storage temperatures (22°C and 4°C), and three treatments (stem bruise, equator bruise, and control). A near-infrared hyperspectral imaging system was used to acquire transmittance images from 970 to 1400 nm with 5 nm bandwidth. Images were acquired from three orientations (calyx-up, stem-up, and equator-up) for fruit in the control and stem bruise groups and from four orientations (calyx-up, stem-up, equator-up, and equator-down) in the equator bruise groups. Immediately after imaging, the fruit samples were sliced, and the sliced surfaces were photographed. The color images of sliced fruit were used as references. By comparing with the reference color images, the profiles of spatial and spectral intensities were evaluated to observe the effect of orientation and help extract regions of interest (ROIs) of bruised and healthy tissues. A support vector machine (SVM) classifier was trained and tested to classify pixels of bruised and healthy tissues. Classification maps were produced, and the bruise ratio was calculated to identify bruised blueberries (bruise ratio >25%). The average accuracy of blueberry identification was 94.5% with the stem-up orientation. The results indicate that detecting bruised blueberries as soon as 30 min after mechanical damage is feasible using hyperspectral transmittance imaging. Keywords: Blueberry, Bruise detection, Classification, Hyperspectral imagery, Transmittance mode.

scholarly journals Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3940-3943
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Maintenance Cost ◽  
Support Vector ◽  
Machine Condition Monitoring ◽  
Learning Techniques ◽  
Catastrophic Failures ◽  
Better Than

Gearbox is an important component used for automobiles, machine tools, industries etc. Failure of any component in gearbox will cause huge maintenance cost and production loss. Failure should be detected as early as possible in order to avoid sudden breakdown which even cause catastrophic failures. Vibration signals are used for machine condition monitoring for predictive maintenance and efficiently predicts fault in the gearbox. In this paper signals from vibration is used for diagnosis of gearbox fault. The experiment uses four different conditions of gearbox in four different load conditions. Then statistical feature extraction is done and obtained result is given to Decision Tree, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for fault diagnosis. The efficiency of these four techniques is compared and shows that machine learning is better than deep learning in gearbox fault diagnosis.

scholarly journals Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

2021 ◽  
pp. 26-40
Author(s):  
Ahmed N. Al Al-Masri ◽  
◽  
◽  
Hamam Mokayed
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Vector ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Spectral Features ◽  
State Recognition

Gear faults have always been a problem encountered in mechanical processing. For gear fault diagnosis, using mathematical-statistical feature extraction methods, deep learning neural networks (DLNN), particle swarm algorithm (PSA), and support vector machines (SVM), etc. According to the feature extraction of deep learning and particle swarm SVM state recognition, the intelligent diagnosis model is established, and the reliability of the model is verified by experiments. The model uses the combination of spectral features extracted by deep learning adaptively and the time domain features extracted by mathematical statistics methods to form a joint feature vector and then uses particle swarm SVM to diagnose the joint feature vector. After research, this paper draws a classification fitness curve combining the fault spectrum features extracted by DLNN and traditional time-domain statistical features. The classification result obtained by using this method is 95.3%. The reliability of the model is verified, and satisfactory diagnosis results are obtained. In addition, the application results also verify the effectiveness of adaptively extracting spectral features based on deep learning.

scholarly journals An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning

2021 ◽  
Vol 11 (18) ◽  
pp. 8441
Author(s):  
Anh-Cang Phan ◽  
Ngoc-Hoang-Quyen Nguyen  ◽  
Thanh-Ngoan Trieu ◽  
Thuong-Cang Phan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Training Dataset ◽  
Economic Losses ◽  
Drowsy Driving ◽  
Training Time ◽  
Road Users ◽  
Fast Training ◽  
Learning Techniques

Drowsy driving is one of the common causes of road accidents resulting in injuries, even death, and significant economic losses to drivers, road users, families, and society. There have been many studies carried out in an attempt to detect drowsiness for alert systems. However, a majority of the studies focused on determining eyelid and mouth movements, which have revealed many limitations for drowsiness detection. Besides, physiological measures-based studies may not be feasible in practice because the measuring devices are often not available on vehicles and often uncomfortable for drivers. In this research, we therefore propose two efficient methods with three scenarios for doze alert systems. The former applies facial landmarks to detect blinks and yawns based on appropriate thresholds for each driver. The latter uses deep learning techniques with two adaptive deep neural networks based on MobileNet-V2 and ResNet-50V2. The second method analyzes the videos and detects driver’s activities in every frame to learn all features automatically. We leverage the advantage of the transfer learning technique to train the proposed networks on our training dataset. This solves the problem of limited training datasets, provides fast training time, and keeps the advantage of the deep neural networks. Experiments were conducted to test the effectiveness of our methods compared with other methods. Empirical results demonstrate that the proposed method using deep learning techniques can achieve a high accuracy of 97% . This study provides meaningful solutions in practice to prevent unfortunate automobile accidents caused by drowsiness.

scholarly journals Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5150
Author(s):  
Shiza Mushtaq ◽  
M. M. Manjurul Islam ◽  
Muhammad Sohaib
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Comprehensive Review ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

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