scholarly journals Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network

2018 ◽  
Vol 8 (11) ◽  
pp. 2332 ◽  
Author(s):  
Hung Nguyen ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Evaluation Method ◽  
Crack Size ◽  
Correct Prediction ◽  
Bearing Defects ◽  
Window Method ◽  
Bearing Condition Monitoring ◽  
Health Related ◽  
Prediction Approach

Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults.

scholarly journals A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Cheng ◽  
Yingying Cai ◽  
Haomai Chen ◽  
Zhuang Cai ◽  
Gang Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Learning Strategies ◽  
Deep Neural Network ◽  
Evaluation Method ◽  
Bloom's Taxonomy ◽  
Automatic Evaluation ◽  
Cognitive Level ◽  
Text Data ◽  
Bloom’S Taxonomy

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

scholarly journals An improved advertising CTR prediction approach based on the fuzzy deep neural network

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0190831 ◽  
Author(s):  
Zilong Jiang ◽  
Shu Gao ◽  
Mingjiang Li
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Approach

scholarly journals Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms

2021 ◽  
Vol 6 (9) ◽  
pp. 129
Author(s):  
T. Pradeep ◽  
Abidhan Bardhan ◽  
Avijit Burman ◽  
Pijush Samui
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Optimization Algorithms ◽  
Grey Wolf Optimizer ◽  
Statistical Parameters ◽  
Unconfined Compression Test ◽  
Rock Stratum ◽  
Inference System ◽  
Prediction Approach

The majority of natural ground vibrations are caused by the release of strain energy accumulated in the rock strata. The strain reacts to the formation of crack patterns and rock stratum failure. Rock strain prediction is one of the significant works for the assessment of the failure of rock material. The purpose of this paper is to investigate the development of a new strain prediction approach in rock samples utilizing deep neural network (DNN) and hybrid ANFIS (adaptive neuro-fuzzy inference system) models. Four optimization algorithms, namely particle swarm optimization (PSO), Fireflies algorithm (FF), genetic algorithm (GA), and grey wolf optimizer (GWO), were used to optimize the learning parameters of ANFIS and ANFIS-PSO, ANFIS-FF, ANFIS-GA, and ANFIS-GWO were constructed. For this purpose, the necessary datasets were obtained from an experimental setup of an unconfined compression test of rocks in lateral and longitudinal directions. Various statistical parameters were used to investigate the accuracy of the proposed prediction models. In addition, rank analysis was performed to select the most robust model for accurate rock sample prediction. Based on the experimental results, the constructed DNN is very potential to be a new alternative to assist engineers to estimate the rock strain in the design phase of many engineering projects.

scholarly journals A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

2019 ◽  
Vol 16 (7) ◽  
pp. 1281 ◽  
Author(s):  
Jihye Lim ◽  
Jungyoon Kim ◽  
Songhee Cheon
Keyword(s):  
Neural Network ◽  
Health Behavior ◽  
Deep Neural Network ◽  
Statistical Data ◽  
Medical Images ◽  
Area Under The Curve ◽  
Principal Component ◽  
Medical Utilization ◽  
Correct Prediction ◽  
Promising Tool

A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.

scholarly journals Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Liang Guo ◽  
Hongli Gao ◽  
Haifeng Huang ◽  
Xiang He ◽  
ShiChao Li
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Frequency Domain ◽  
Deep Neural Network ◽  
Features Selection ◽  
Time Frequency ◽  
Nonlinear Dimension Reduction ◽  
Bearing Condition Monitoring ◽  
Nonlinear Dimension ◽  
Condition Recognition

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

scholarly journals Risk Evaluation Method of Import and Export Goods Based on Fuzzy Reasoning and DeepFM

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yuanyuan Xu ◽  
Huijuan Fang ◽  
Jiliang Luo ◽  
Jianan He ◽  
Tao Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Evaluation Method ◽  
Fuzzy Reasoning ◽  
Experimental Tests ◽  
Material Resources ◽  
Inspection And Quarantine ◽  
Risk Sources ◽  
Import And Export ◽  
Document Review

At present, the inspection mode of China's import ports is generally manual based on experience, or random inspection by the document review system according to a preset random inspection ratio. In order to improve the detection rate of unqualified goods and realize the best allocation of limited human and material resources of inspection and quarantine institutions, a method composed of fuzzy reasoning, deep neural network, and factorization machine (DeepFM) was proposed for the intelligent evaluation of risk sources of imported goods. Fuzzy reasoning is used to realize the fuzzy normalization of the dataset samples, the DeepFM deep neural network is finally used for training and learning to classify and evaluate the risks of goods. Results of experimental tests on a specific customs import and export dataset verify the effectiveness of the proposed research method.

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