Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization

Lee, Chun‐Yao; Zhuo, Guang-Lin

doi:10.3390/sym13030487

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…Four DL models are compared in this case study, including IFKNMD-CNN, CNN based on LMD with TFR (LMD-TFR-CNN), IFKNMD-1DCNN, and 1D-CNN. In LMD-TFR-CNN, LMD can adaptively decompose the signal into a set of product functions (PFs), and then use PF selection [36] to select the best PF and express it as a time-frequency relationship. IFKNMD-1DCNN is an ablation version of the proposed model, which skips GAF and directly uses one-dimensional signal as the input of CNN.…”

Section: Image Dataset For DL Methodsmentioning

confidence: 99%

“…IFKNMD-1DCNN also performs well in identifying healthy states but suffers many classification errors in classifying faulty states (KA and KI). LMD-TFR-CNN introduces a PF function selection technique to remove unimportant PF functions [36]. However, it can be observed from the classification results that LMD is not sensitive to complex relationships between states, and cannot generate high-quality images for CNN to learn features.…”

Section: Intelligent Diagnosis With Ifknmd-cnnmentioning

confidence: 99%

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A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions

Lee

Zhuo

Le³

2022

Sensors

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This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating machinery. The nonlinear mode decomposition based on the improved fast kurtogram inherits the advantages of the original algorithm while improving the computational efficiency and signal-to-noise ratio. The gramian angular field can construct a two-dimensional image without destroying the time relationship of the signal. Therefore, the proposed method can perform fault diagnosis on rotating machinery under complex operating conditions. The proposed method is verified on the Paderborn dataset under heavy noise and multiple operating conditions to evaluate its effectiveness. Experimental results show that the proposed model outperforms wavelet denoising and the traditional adaptive decomposition method. The proposed model achieves over 99.6% accuracy in all four operating conditions provided by this dataset, and 93.8% accuracy in a strong noise environment with a signal-to-noise ratio of −4 dB.

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Section: Image Dataset For DL Methodsmentioning

confidence: 99%

Section: Intelligent Diagnosis With Ifknmd-cnnmentioning

confidence: 99%

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions

Lee

Zhuo

Le³

2022

Sensors

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“…Traditional NN such as multilayers perceptron (MLP) has the problem of a complex structure and difficult training process [38]. ML has the advantage of being simple and easy to implement, and the classification results are better, especially the support vector machine (SVM) algorithm, which has many papers to prove its classification efficiency and anti-noise capability [39,40]. In recent years, a new type of NN, fully connected neural network (FCNN), achieves powerful performance through a new way of connecting neurons.…”

Section: Introductionmentioning

confidence: 99%

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

Lee

Zhuo

2021

Mathematics

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The accurate localization of the rolling element failure is very important to ensure the reliability of rotating machinery. This paper proposes an efficient and anti-noise fault diagnosis model for rolling elements. The proposed model is composed of feature extraction, feature selection and fault classification. Feature extraction is composed of signal processing and signal noise reduction. Signal processing is carried out by local mean decomposition (LMD), and signal noise reduction is performed by product function (PF) selection and wavelet packet decomposition (WPD). Through the steps of signal noise reduction, high-frequency noise can be effectively removed, and the fault information hidden under the noise can be extracted. To further improve the effectiveness of the diagnostic model, an improved binary particle swarm optimization (IBPSO) is proposed to find the most important features from the feature space. In IBPSO, cycling time-varying inertia weight is introduced to balance exploitation and exploration and improve the capability to escape from local solutions, and crossover and mutation operations are also introduced to improve exploration and exploitation capabilities, respectively. The main contributions of this research are briefly described as follows: (1) The feature extraction process applied in this research can effectively remove noise and establish a high-accuracy feature set. (2) The proposed feature selection algorithm has higher accuracy than the other state-of-the-art feature selection algorithms. (3) In a strong noise environment, the proposed rolling element fault diagnosis model is compared with the state-of-the-art fault diagnosis model in terms of classification accuracy. Experimental results show that the model can maintain high classification accuracy in a strong noise environment. Therefore, it can be proved that the fault diagnosis model proposed in this paper can be effectively applied to the fault diagnosis of rotating machinery.

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Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems

Chang,

Ding,

et al. 2024

Arch Computat Methods Eng

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Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization

Cited by 7 publications

References 48 publications

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems

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