Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Yan, Wang; Pin, Wang; He, Lang

doi:10.1109/access.2021.3074505

Cited by 9 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Turbocharges in diesel engine Car engines (data from [17]) Values of Reliability Index and probability of failure are the same as those obtained with other methods [30] Model SDWPSO-BPNN (hybrid of dynamic weight particle swarm optimisation-based sine map and back propagation neural network) Turbocharges in diesel engine (data from [17]) and industrial robot systems NRMSE = 6.9 × 10 −5 for turbocharges NRMSE = 2.3 × 10 −6 for industrial robot systems [21] Cascade feedforward neural network CNC machine tool spindles MAPE: 1.56-2.53% [22] ANN supported stochastic process Simulated data and degradation dataset of spindle systems Mean absolute relative error < 0.13 for all samples of real degradation data [24] ANN supported Wiener process Simulated data and degradation dataset of spindle systems Mean absolute relative error < 0.06 for all samples of real degradation data [25] Model based on convolutional neural networks…”

Section: Introductionmentioning

confidence: 75%

See 1 more Smart Citation

Reliability Analysis of Military Vehicles Based on Censored Failures Data

2022

View full text Add to dashboard Cite

The paper proposes a methodology of reliability testing as applied to vehicles used in military transport systems. After estimating the value of the reliability function using the Kaplan–Meier estimator, reliability models were developed and analysed. The neural model, which achieved the value of the correlation coefficient R exceeding 0.99, was determined to fit the empirical data the best. On the basis of the approximated reliability function of several models, the reliability characteristics of the tested sample of vehicles were determined. Plots of the failure probability density function for all three models had similar courses over a significant part of the function domain. A failure intensity function was also determined, which varied between models. For the exponential and Weibull model, the expected mileage between failures was calculated, which proved impossible for the neural model. The proposed methodology is capable of modelling reliability characteristics based on the observation of an assumed period of the exploitation process of the selected group of military vehicles.

show abstract

Section: Introductionmentioning

confidence: 75%

“…In publication [22], a reliability prediction model was developed based on a cascade neural network. A sigmoidal hidden layer activation function along with linear output neuron activation was proposed.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Military Vehicles Based on Censored Failures Data

2022

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to take CNC machine tool as a repairable system for the influence degree study. Therefore, this paper studies the unrepairable impact degree and repairable impact degree of key components respectively [6][7][8][9]. This paper has the following characteristics: (1) For the irreparable influence degree, the probability influence degree only reflects the change of the system failure probability caused by the change of the failure probability of components [2], but does not involve the difficulty of the change of the failure probability.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of unrepairable and repairable influence degrees of CNC machine tools

Wang

Guo

2023

Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023)

View full text Add to dashboard Cite

After the reliability analysis and maintainability analysis of numerical control machine tools, the influence degree of key components is analyzed respectively under the premise that it is not repairable and repairable. Under the premise that it is not repairable, the probability influence degree analysis is carried out, and on this basis, the critical influence degree analysis is carried out. Under the condition of repairable, Natvig method is used to analyze the comprehensive influence degree. Finally, the analysis results of influence degree were compared and analyzed. The Sorting results of influence degree of key components are consistent with the reality, and obtained results can further guide the reliability design and reliability growth of CNC machine tools. The Natvig method applied in this paper provides an effective way to study the repairable influence degree of CNC machine tools.

show abstract

“…Du et al [19] established a mathematical model of structural fuzzy reliability using the fuzzy random probability method, selected the optimal membership function, and proposed a direct integration method based on a dual neural network for the problem of the difficult multiple integration calculation in the fuzzy reliability mathematical model, which solved the structural fuzzy reliability problem with multidimensional random variables well and had high computational efficiency and accuracy. Xiao et al [20] used existing relevant reliability data to perform error comparison analysis on test set data, conducted simulation training, and established a three-layer continuous optimization feedforward neural network model for the reliability prediction of a CNC machine tool spindle. Compared with the BP neural network, it has a faster learning speed and better nonlinear fitting ability.…”

Section: Introductionmentioning

confidence: 99%

Multi-Extremum Adaptive Fuzzy Network Method for Dynamic Reliability Estimation Method of Vectoring Exhaust Nozzle

Zhang¹,

Yuan

et al. 2023

Aerospace

View full text Add to dashboard Cite

To enhance the accuracy and efficiency of reliability analysis for an aero-engine vectoring exhaust nozzle (VEN), a multi-extremum adaptive fuzzy network (MEAFN) method is developed by absorbing an adaptive neuro-fuzzy inference system (ANFIS) into the multi-extremum surrogate model (MESM) method. In the proposed method, the MERSM is used to establish the surrogate models of many output responses for the multi-objective integrated reliability analysis of the VEN. The ANFIS method is regarded as the basis function of the MESM method and adopted to improve the modeling precision of the MESM by introducing the membership degree into the input parameters and weights to improve the approximation capability of the neural network model to the high nonlinear reliability analysis of the VEN. The mathematical model of the MEAFN method and reliability analysis thoughts of the VEN is provided in this study. Then, the proposed MEAFN method is applied to conduct the dynamic reliability analysis of the expansion sheet and the triangular connecting rod in the VEN by considering the aerodynamic loads, operation temperature, and material parameters as the random input variables and the stresses and deformations as the output responses, compared with the Monte Carlo method and the extremum response surface method. From the comparison of the methods, it is indicated that the MEAFN method is promising to improve computational efficiency while maintaining accuracy. The efforts of this study provide guidance for the optimization design of the VEN and enrich the reliability theory of the flexible mechanism.

show abstract

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Cited by 9 publications

References 9 publications

Reliability Analysis of Military Vehicles Based on Censored Failures Data

Reliability Analysis of Military Vehicles Based on Censored Failures Data

Comparative analysis of unrepairable and repairable influence degrees of CNC machine tools

Multi-Extremum Adaptive Fuzzy Network Method for Dynamic Reliability Estimation Method of Vectoring Exhaust Nozzle

Contact Info

Product

Resources

About