Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

2021

IEEE Access

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This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.

“…Step 6: Update particle velocity using (18) and the particle position is updated by converting binary values using transfer function (19) and the binary value is updated using (11).…”

Section: ) Proposed E-bpso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

2021

IEEE Access

Self Cite

“…It is a set of potential statistical features from the time-domain, frequency-domain, and frequency-time domain by signal analysis methods. Inheriting our previous research [33], a feature set including 76 potential features extracted by discrete wavelet transform (DWT), envelope analysis (EA), and fast Fourier transform (FFT), methods were applied in this study. They are considered as inputs to two classifiers SVM and ANN.…”

Section: ) Evaluation and Comparisonmentioning

confidence: 99%

Identifying Faults of Rolling Element Based on Persistence Spectrum and Convolutional Neural Network With ResNet Structure

2021

IEEE Access

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The task of accurately bearing fault diagnosis of the rotary machinery from the measured signal remains a major problem that attracts a lot of attention. This paper proposed a new approach to build an efficient bearing fault diagnostic model for rotary machinery. The model is based on the persistence spectrum image and convolutional neural network (CNN) with ResNet structure. The persistence spectrum is extracted from the envelope of the raw vibration signal. Then, the persistence spectrum image is constructed based on short-time Fourier transform, which presents a new relationship between the frequency, magnitude, and energy of each signal with time, which the traditional spectrum analysis methods have not been given before. To explore the discriminant features from the persistence spectrum image of the envelope signal, an improved CNN with ResNet structure allows direct connection feature maps from the lower-level layer to the higherlevel layer. That helps to exploit the granularity features in the low-level layer which can be lost when feedforward through adjacent layers in a traditional CNN. As a result, the proposed model operates efficiently with high accuracy not only under various working loads but also under noise conditions. Besides, its performance is very satisfactory compared to other types of two-dimensional images and other state-of-theart diagnosis models. Overall, the proposed approach is highly feasible for an intelligent bearing fault diagnostic model. INDEX TERMSBearing fault diagnosis, convolutional neural network, persistence spectrum, ResNet.

“…Although there is no optimization algorithm that can guarantee the best feature subset, PSO has successfully solved many nonlinear optimization problems in the engineering field due to its excellent computational efficiency and simple operation [31,32]. Therefore, PSO is still an optimization algorithm that many researchers are dedicated to researching [33][34][35]. Therefore, this study proposes an improved binary particle swarm optimization (IBPSO) as the feature selection task of the fault diagnosis model.…”

Section: Introductionmentioning

confidence: 99%

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

Zhuo

2021

Mathematics

Self Cite

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.