A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Shen, Chen; Chen, Youping; Chen, Bing; Xie, Jingming

doi:10.32604/cmc.2019.04883

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, they are denoted by x 1 , x 2 , and x 3 , respectively. One hidden layer is utilized to perform the processing, which is with any number of possible nodes [23]. Finally, the output layer has only one output, which is the eccentric displacement between the real rotor center and the rotating center of the SynRel motor.…”

Section: Figure 14: Multilayer Feed-forward Artificial Neural Networkmentioning

confidence: 99%

Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

Shu-guang¹,

Li²,

Zhao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the stator and the whole motor are performed, respectively, to explore the natural frequency and the modal shape of the motor, by which the further vibrational analysis is possible to be conducted. The vibration level of the housing is furtherly studied to investigate its relationship with the rotor eccentricity, which is validated by the prototype test. Furthermore, an artificial neural network, which has 3 layers, is proposed. By taking the air-gap flux density, the electromagnetic force, and the vibrational level as inputs, and taking the eccentric distance as output, the proposed neural network is trained till the error smaller than 5%. Therefore, this neural network is obtaining the input parameters of the tested motor, based on which it is automatically monitoring and reporting the eccentric error to the upper-level control center.

show abstract

Section: Figure 14: Multilayer Feed-forward Artificial Neural Networkmentioning

confidence: 99%

Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

Shu-guang¹,

Li²,

Zhao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In unclear data, they often search for patterns and connections and are specifically tailored for complex problems where there are no classical mathematical and conventional procedures or formal underlying theories. ANN differs from statistical and algorithmic techniques such as regression sampling in that ANN learns from examples to give generalized solutions [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. ANN consists of multiple layers, and, in every layer, there exist nonlinear processing and fundamental computation units called neurons that perform tasks such as feature extraction.…”

Section: Artificial Intelligence and Its Application In Shear Strementioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

View full text Add to dashboard Cite

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

show abstract

A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Cited by 2 publications

References 16 publications

Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Contact Info

Product

Resources

About