Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Liang, Yantao; Zhang, Wenhu; Cui, Yongcun; Deng, Sier

doi:10.3390/s23115325

Cited by 4 publications

(5 citation statements)

References 31 publications

(41 reference statements)

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“…In Figure 7, the reliability results calculated by the model are close to the estimated results. The maximum error between the calculated values and the estimated values is 9.6%, which is better than the accuracy results reported in related literature [43] [44]. The quantified test results show that the estimation accuracy θ, R 2 , and MSE are 88.84%, 0.88172, and 0.0087, respectively.…”

Section: ) Reliability Estimation For Anfis-shamode-iwoa Modelscontrasting

confidence: 47%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

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In the process of daily product development and design, optimizing the shape and external dimensions of the vehicle chassis truss structure while considering both weight and reliability indicators often involves interdisciplinary collaboration and inefficient communication, leading to repetitive mechanical labor and low efficiency. This is widely regarded as a difficult and challenging task. In order to improve the design quality of the chassis truss structure and enhance work efficiency, this paper proposes a novel lightweight multi-objective optimization design system for vehicle chassis trusses based on the ANFIS-SHAMODE-IWOA model. Firstly, an adaptive spiral search strategy is introduced based on the multi-objective hybrid metaheuristic algorithm (SHAMODE) which is based on successful history. Then, a new SHAMODE-IWOA algorithm is proposed. In order to estimate the reliability level of the chassis truss structure under different design parameter combinations, a new ANFIS-SHAMODE-IWOA model is constructed by using the proposed SHAMODE-IWOA algorithm to learn the ANFIS model. Finally, in order to obtain the optimal design parameter combination, multi-objective optimization based on minimum design quality and optimal reliability measurement functions is studied using the SHAMODE-IWOA algorithm. Experimental results show that SHAMODE-IWOA has leading global optimization capability on CEC2017 test set benchmark functions. Compared with other intelligent models, the ANFIS-SHAMODE-IWOA model has better performance in reliability coefficient estimation. At the same time, the SHAMODE-IWOA algorithm obtains a more optimal design parameter combination for the chassis truss, with improvements of 9.5%, 12.5%, and 15% in Mass, Bending, and Torsion indicators, respectively. Finally, the proposed ANFIS-SHAMODE-IWOA multi-objective optimization design system, as a novel intelligent model, can be used to evaluate the reliability of chassis truss structures, improve development and design efficiency, and obtain the best design parameter combination, which is beneficial to improving the level of green intelligent manufacturing design.

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Section: ) Reliability Estimation For Anfis-shamode-iwoa Modelscontrasting

confidence: 47%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

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show abstract

“…The C1 subset was used as the real-time observed degradation dataset during the online prediction phase. The degradation models for multiple degradation features were established based on Equation (1). At time t k , the model parameter µ θ,k of each degradation feature was updated using the Bayesian formula, i.e., Equations ( 7) and ( 8), based on the online monitored data.…”

Section: Number Featurementioning

confidence: 99%

“…Machine tools play a critical role in the development of modern manufacturing. Various industries, including automotive manufacturing, aerospace, and military industries, rely heavily on the precision-processed components produced by machine tools [ 1 , 2 , 3 ]. The efficient and accurate processing capability of machine tools is indispensable.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression

Rong,

Li,

et al. 2024

Sensors

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Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

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“…Generally, approaches for predicting the RUL of bearings can be roughly classified as two main groups: mechanism model methods [10][11][12] and data-driven methods [13][14][15]. Specifically, mechanism model methods typically rely on failure principles of bearings and try to establish accurate mathematical models to describe the degradation process of bearings [16].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

Yang,

Jiang,

Zeng

et al. 2024

Sensors

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Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

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Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Cited by 4 publications

References 31 publications

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

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