A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

Fan, Yerui; Zhang, Chao; Xue, Yu; Wang, Jianguo; Gu, Fengshou

doi:10.1155/2020/9096852

Cited by 18 publications

(9 citation statements)

References 17 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relationships between these factors for successive iterations at time ( t ) and ( t + 1 ) are presented in Equations (10 and 11), where r represents a random number in the range of 0 and 1. The C p and C g control in this algorithm results in the exploitation of optimal values in the objective function [ 46,51–56 ]

X_{i} (t + 1) = X_{i} (t) + V_{i} (t + 1)

\begin{matrix} true \\ right V_{i} ((), t + 1) & = & left w \times V_{i} ((), t) + C_{p} \times r \times ((), X_{i}^{p b e s t} - X_{i} ((), t)) \\ left + C_{g} \times r \times ((), X_{i}^{g b e s t} - X_{i} ((), t)) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

View full text Add to dashboard Cite

Numerous studies have been performed to modify ferrites to achieve the desired magnetic properties for the intended applications. Many variables with interactions between themselves are involved in modifying these properties. This study aims to provide an accurate and effective method for predicting the magnetic properties of M‐type ferrites under the influence of involved variables. For this purpose, ferrites are synthesized by doping 17 different ions and the results of the analysis of samples form the experimental data. The support vector regression (SVR) model is selected for prediction and in order to optimize hyper parameters, genetic, particle swarm optimization, and sequential minimal optimization techniques are used. Based on the comparison between the outcomes of these algorithms, the model with the best performance is used to predict coercivity and residual magnetism in M‐type magnetic ferrites. Furthermore, with the cross validation technique, the accuracy and robustness of the model designed for new samples are evaluated. The results show that the selected SVR model, with an average absolute relative error of 2% and 0.7%, is able to predict coercivity and residual magnetism, respectively. Consequently, the prediction method presented has the capability to accelerate and develop the modification of magnetic properties for different applications.

show abstract

X_{i} (t + 1) = X_{i} (t) + V_{i} (t + 1)

\begin{matrix} true \\ right V_{i} ((), t + 1) & = & left w \times V_{i} ((), t) + C_{p} \times r \times ((), X_{i}^{p b e s t} - X_{i} ((), t)) \\ left + C_{g} \times r \times ((), X_{i}^{g b e s t} - X_{i} ((), t)) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…SVMs have been used extensively for rolling element bearing fault diagnosis. 18,19 Decision Trees (DT) are models that create if/else questions that ultimately lead to a prediction of the value of the target variable. Each question splits the data into smaller groups.…”

Section: Common ML Algorithmsmentioning

confidence: 99%

A journal bearing performance prediction method utilizing a machine learning technique

Rossopoulos

Papadopoulos

2021

Proceedings of the Institution of Mechanical Engineers, Part J:

View full text Add to dashboard Cite

A predictive analytics methodology is presented, utilizing machine learning algorithms to identify the performance state of marine journal bearings in terms of maximum pressure, minimum film thickness, Sommerfeld number, load and shaft speed. A dataset of different bearing operation states has been generated by solving numerically the Reynolds equation in the hydrodynamic lubrication regime, for steady-state loading conditions and assuming isothermal and isoviscous lubricant flow. The shaft has been modelled with four different values of misalignment angle, lying within the acceptable operating range, as defined in the existing regulatory framework. The journal bearing was modelled parametrically using generic geometric parameters of a marine stern tube bearing. The lift-off speed was estimated for each loading scenario to ensure operation in the hydrodynamic lubrication regime and the effect of shaft misalignment on lift-off speed has been evaluated. The generated dataset was utilised for training, testing and validation of several machine learning algorithms, as well as feature selection analysis, in order to solve several classification problems and identify the various bearing operational states.

show abstract

“…Fault identification is also an important step for establishing the correlation between fault features and class labels. Fan et al [ 15 ] proposed a high-performance SVM multi-feature fusion and self-tuning particle swarm optimization algorithm. The method extracted multi-dimensional fault features by EMD.…”

Section: Introductionmentioning

confidence: 99%

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Zhuang

Liu

Zheng

et al. 2023

Sensors

View full text Add to dashboard Cite

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings.

show abstract

A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

Cited by 18 publications

References 17 publications

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

A journal bearing performance prediction method utilizing a machine learning technique

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Contact Info

Product

Resources

About