A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

Lee, Chun‐Yao; Le, Truong-An; Hung, Chih‐Cheng

doi:10.1109/access.2023.3274696

Cited by 6 publications

(1 citation statement)

References 48 publications

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“…In addition, it extracts frequency domain features from the signal analyzed by FFT and then uses EA to reorganize the extracted features into feature sets. The feature set generated through feature extraction usually still contains redundant features, especially in high-dimensional feature sets [13], which will affect the final classification and lead to a decrease in accuracy. Therefore, the role of feature selection in the next stage will be crucial.…”

Section: Introductionmentioning

confidence: 99%

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Lee,

Le,

Chen

et al. 2024

Mathematics

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Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The proposed model is a combination of three processes: feature extraction of signals collected from the motor based on multi-resolution analysis, fast Fourier transform, and envelope analysis. Next, redundant or irrelevant features are removed using the feature selection technique. A binary salps swarm algorithm combined with an extended repository is the proposed method to remove unnecessary features. As a result, an optimal feature subset is obtained to improve the performance of the classification model. Finally, two classifiers, k-nearest neighbor and support vector machine, are used to classify the fault of the electric motor. There are four input datasets used to evaluate the model performance, and UCI is the benchmark dataset to verify the effectiveness of the proposed feature selection technique. The remaining three datasets include the bearing dataset collected from experiments, with an average classification accuracy of 99.9%, as well as Case Western Reserve University (CWRU) and Machinery Failure Prevention Technology (MFPT), which are public datasets with average classification accuracies of 99.6% and 98.98%, respectively. The experimental results show that this method is more effective in diagnosing bearing faults than other traditional methods and prove its robustness.

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Section: Introductionmentioning

confidence: 99%

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Lee,

Le,

Chen

et al. 2024

Mathematics

Self Cite

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GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

Bagci Das,

Das

2024

J. Vib. Eng. Technol.

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Purpose As the concept of Industry 4.0 is introduced, the significance of Smart Fault Diagnosis in the industry is increased. Therefore, it is essential to develop accurate, robust, and lightweight intelligent fault diagnosis approach that can be executed in real-time even with embedded systems. Additionally, it is preferable to use a single method for multi-purposes such as the fault detection, identification, and severity assessment. This study proposed a new approach called GaBoT for fault diagnosis of rotating machinery to satisfy those requirements. Method The proposed approach adopted the concept of the ensemble of ensembles by boosting random forest. The statistical features of discrete wavelet transform were considered since they are easy and fast to obtain. Model optimization was conducted by employing genetic algorithm to alleviate the computational load without decreasing the model performance. The proposed approach has been validated by unseen data from an experimental dataset including shaft, rotor, and bearing faults. Results The experimental results indicate that the proposed approach can effectively find the fault type with 99.85% accuracy. Besides, it successfully determines the fault severity by accuracy values between 96.45 and 99.72%. GABoT can also determine the imbalance severity in the presence of three bearing faults. Conclusion Employing GA eliminated most of the redundant features and reduced the model execution time consumption. The results yielded that GABoT is a highly accurate model, and can be utilized in real-time fault diagnosis of rotating machinery.

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Application of symmetric uncertainty and emperor penguin–grey wolf optimisation for feature selection in motor fault classification

Lee,

Le,

Chien

et al. 2024

IET Electric Power Appl

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The authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three primary phases: feature extraction, feature selection and classification. During the feature extraction phase, crucial features are identified using empirical mode decomposition, fast Fourier transform and multiresolution analysis, resulting in a total of 144 features. The feature selection stage employs a new strategy that combines symmetrical uncertainty in the filter approach with the binary grey wolf optimiser and emperor penguin optimiser in the wrapper approach. Finally, a support vector machine is used for classification to generate fitness values. To validate the model's effectiveness and accuracy, motor fault current signal datasets, case Western Reserve University (CWRU) benchmark datasets and mechanical failure prevention technology benchmark datasets are utilised. In the motor fault current signal dataset, the highest average accuracy achieved is 99.95%, with a minimum average running time of 88.02 s obtained under ∞dB conditions. Regarding benchmark datasets and mechanical failures at CWRU, using the prevention technology benchmark dataset resulted in classification accuracies of 99.54% and 99.52%, respectively. Comparative analysis with traditional algorithms reveals that symmetric uncertainty and emperor penguin–grey wolf optimisation model outperforms traditional models in terms of performance.

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A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

Cited by 6 publications

References 48 publications

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

Application of symmetric uncertainty and emperor penguin–grey wolf optimisation for feature selection in motor fault classification

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