Transfer learning-based deep CNN model for multiple faults detection in SCIM

Kumar, Prashant; Hati, Ananda Shankar

doi:10.1007/s00521-021-06205-1

Cited by 21 publications

(8 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The upper layer performs convolutional operations with multiple trainable convolutional kernels of size k*k , plus a bias, and acquires a feature map of size ( N - k + 1)*( N - k + 1) and the same number as the convolutional kernels after an excitation function. The equation for the above process is as follows ( Caldelli et al, 2021 ; Khaydarova et al, 2021 ; Kumar and Hati, 2021 ; Moccia et al, 2021 ; Shi et al, 2021 ; Szajna et al, 2021 ):…”

Section: Basic Methodsmentioning

confidence: 99%

Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

Fan

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

In order to better assess the mental health status, combining online text data and considering the problems of lexicon sparsity and small lexicon size in feature statistics of word frequency of the traditional linguistic inquiry and word count (LIWC) dictionary, and combining the advantages of constructive neural network (CNN) convolutional neural network in contextual semantic extraction, a CNN-based mental health assessment method is proposed and evaluated with the measurement indicators in CLPsych2017. The results showed that the results obtained from the mental health assessment by CNN were superior in all indicators, in which F1 = 0.51 and ACC = 0.69. Meanwhile, ACC evaluated by FastText, CNN, and CNN + Word2Vec were 0.66, 0.67, 0.67, and F1 were 0.37, 0.47, and 0.49, respectively, which indicates the use of CNN in mental health assessment has feasibility.

show abstract

Section: Basic Methodsmentioning

confidence: 99%

Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

Fan

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…To overcome these shortcomings, the most common approach applies the concept of transfer learning. First, to quickly build models to detect motor faults in different machines of the same type, Kumar and Hati [38] and Skowron [39] used deep learning models and transfer learning. Liu et al [40] proposed construction chiller defect detection based on transfer learning, claiming that it could be used to establish individual diagnostic models for similar chillers.…”

Section: Related Workmentioning

confidence: 99%

Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.0: A Case Study of a Spring Factory

Loh,

Chen,

2024

Applied Sciences

View full text Add to dashboard Cite

In the era of Industry 3.0, product fault detection systems became important auxiliary systems for factories. These systems efficiently monitor product quality, and as such, substantial amounts of capital were invested in their development. However, with the arrival of Industry 4.0, high-volume low-mix production modes are gradually being replaced by low-volume high-mix production modes, reducing the applicability of existing systems. The extent of investment has prompted factories to seek upgrades to tailor existing systems to suit new production modes. In this paper, we propose an approach to upgrading based on the concept of transfer learning. The key elements are (1) using a framework with a basic model and an add-on model rather than fine-tuning parameters and (2) designing a radial basis function deep neural network (RBF-DNN) to extract important features to construct the basic and add-on models. The effectiveness of the proposed approach is verified using real-world data from a spring factory.

show abstract

“…With the aid of a deep convolutional neural network, [75] achieved knowledge transfer and, based on it, proposed a fault detection scheme for squirrel cage induction motors. By adopting a fully-connected neural network, [76] designed a state information prediction method for multiple-input multipleoutput systems, which can address a small number of labeled data in the target domain.…”

Section: Developments Of Transfer Learning-based Fdmentioning

confidence: 99%

Transfer Learning-motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives

Chen¹,

Luo²,

Huang³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two subclasses are developed based on knowledge calibration and knowledge compromise, is carried out in this survey paper. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.</p>

show abstract

Transfer learning-based deep CNN model for multiple faults detection in SCIM

Cited by 21 publications

References 48 publications

Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.0: A Case Study of a Spring Factory

Transfer Learning-motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives

Contact Info

Product

Resources

About