Feedforward Chaotic Neural Network Model for Rotor Rub-Impact Fault Recognition Using Acoustic Emission Method

Peng, Wei; Liu, Weidong; Cheng, Xinbin; Shi, Liping

doi:10.1155/2018/9718951

Cited by 2 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, as a comparison, we have compared our method with the other four different state-of-the-art classification methods, namely, feedforward chaotic neural network (FCNN), 15 Bp network, light gradient boosting machine (LightGBM), 16 and convolution neural network (CNN). In the experiment, the LightGBM parameters include learning rate h = 0:1, the num_leaves a = 100, the max_depth h = 12, and the number of trees n estimators = 300, negative binomial log-likelihood as the objective function for classification.…”

Section: Wsd-gan Model Results Analysismentioning

confidence: 99%

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Yang

et al. 2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Water inrush occurred in mines, threatens the safety of working miners which triggers severe accidents in China. To make full use of existing distinctive hydro chemical and physical characteristics of different aquifers and different water sources, this article proposes a new water source discrimination method using laser-induced fluorescence technology and generative adversarial nets. The fluorescence spectrum from the water sample is stimulated by 405-nm lasers and improved by recursive mean filtering method to alleviate interference and auto-correlation to enhance the feature difference. Based on generative adversarial nets framework and improved spectra features, the article proposes a novel water source discrimination-generative adversarial nets model in mines to solve the problem of data limitation and improve the discrimination ability. The results show that the proposed method is an effective method to distinguish water inrush types. It provides a new idea to discriminate the sources of water inrush in mines timely and accurately.

show abstract

Section: Wsd-gan Model Results Analysismentioning

confidence: 99%

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Yang

et al. 2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…36,37 In modern industrial applications, 38,39 rotating machinery (RM) [40][41][42] Introduction of CM and FD of IM based on AE Importantly, CM and FD based on the AE of RM have been growing over recent years. AE is widely used in rotating electric motors CM and FD [186][187][188] for both electrical and mechanical faults including, the shaft misalignment, 189 the bearing, [190][191][192] the rotor 193,194 and the stator. 195 In addition, AE is extensively used in CM and FD for the gearbox.…”

Section: Introductionmentioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

126

View full text Add to dashboard Cite

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

show abstract

Feedforward Chaotic Neural Network Model for Rotor Rub-Impact Fault Recognition Using Acoustic Emission Method

Cited by 2 publications

References 22 publications

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Contact Info

Product

Resources

About