scholarly journals A Novel Multimode Fault Classification Method Based on Deep Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Funa Zhou ◽  
Yulin Gao ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Rolling Bearing ◽  
Classification Method ◽  
Fault Classification ◽  
Classification Models ◽  
Classification Result ◽  
The Third ◽  
Fundamental Step ◽  
Fault Severity

Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition. In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result. For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity. The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method.

scholarly journals Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

2021 ◽  
Vol 2070 (1) ◽  
pp. 012141
Author(s):  
Pavan Sharma ◽  
Hemant Amhia ◽  
Sunil Datt Sharma
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Manufacturing Industry ◽  
Rolling Bearing ◽  
Performance Comparison ◽  
Learning Methods ◽  
Training Time ◽  
Bearing Faults ◽  
Artificial Intelligence Techniques

Abstract Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.

scholarly journals Deep Learning Classification of Lake Zooplankton

2021 ◽  
Author(s):  
Sreenath Pruthviraj Kyathanahally ◽  
Tommy Hardeman ◽  
Ewa Merz ◽  
Thea Kozakiewicz ◽  
Marta reyes ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Frequency ◽  
Ecosystem Health ◽  
Manual Annotation ◽  
Classification Models ◽  
Automated Imaging ◽  
Freshwater Habitats ◽  
Plankton Communities ◽  
Better Than

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor- intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

scholarly journals A Feature Extraction Method for Fault Classification of Rolling Bearing based on PCA

2015 ◽  
Vol 628 ◽  
pp. 012079 ◽  
Author(s):  
Fengtao Wang ◽  
Jian Sun ◽  
Dawen Yan ◽  
Shenghua Zhang ◽  
Liming Cui ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Feature Extraction Method

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

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