Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network

Wang, Yu Cheng

doi:10.1007/s12652-018-0997-7

Cited by 15 publications

(2 citation statements)

References 22 publications

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“…, where  is the weight variable and  is the deviation. Otherwise it is non-linear regression [21]. In this case, a non-linear mapping ( ) u  is used to map the data into a high-dimensional feature space, and linear regression is performed in this space, then there is ( ) ( )…”

Section: Design Of System Databasementioning

confidence: 99%

Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm

Jiangping Nan

2024

jes

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This paper proposes a power equipment maintenance management system based on network and deep learning algorithm. First of all, the actual power network equipment maintenance and maintenance management of the power company, so as to clarify the tasks to be completed by the system. ASPNET technology is used to realize the management of business form data and various transaction processes of each system, and the related technology of process engine is applied to the maintenance of each equipment. A load forecasting method of power system based on SVR is proposed. It makes the operation and maintenance work of this system more intelligent. Each function module of the whole system is designed in detail. The system basically meets the needs of power equipment maintenance and repair management.

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Section: Design Of System Databasementioning

confidence: 99%

Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm

Jiangping Nan

2024

jes

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“…Back-propagation compares the gaps between the targeted output and the actual computed output values. The synapse values are re-adjusted to minimize errors [38]. The propagation rule is the basis of the architecture shown in Figure 1.…”

Section: Back-propagation Network Algorithmmentioning

confidence: 99%

Application of Neural Networks to Explore Manufacturing Sales Prediction

2019

Self Cite

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Manufacturing sales prediction is an important measure of national economic development trends. The plastic injection molding machine industry has its own independent R and D energy and mass production technology, with all products sold globally through international brands. However, most previous injection molding machine studies have focused on R and D, production processes, and maintenance, with little consideration of sales activity. With the development and transformation of Industry 4.0 and the impact of the global economy, Taiwan’s injection molding machine industry growth rate has gradually flattened or even declined, with company sales and profits falling below expectations. Therefore, this study collected key indicators for Taiwan’s export economy from 2008 to 2017 to help understand the impact of economic indicators on injection molding sales. We collected 35 indicators, including net entry rate of employees into manufacturing industries, trend indices, manufacturing industry sales volume indices, and customs export values. We used correlation analysis to select variables affecting plastic injection machine sales and artificial neural networks (ANN) were applied to predict injection molding machine sales at each level. Prediction results were verified against the correlation indicators, and seven key external economic factors were identified to predict accurate changes in company annual sales prediction, which will be helpful for effective resource and risk management.

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Community detection in network using chronological gorilla troops optimization algorithm with deep learning based weighted convexity

2023

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Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network

Cited by 15 publications

References 22 publications

Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm

Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm

Application of Neural Networks to Explore Manufacturing Sales Prediction

Community detection in network using chronological gorilla troops optimization algorithm with deep learning based weighted convexity

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