Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition

Zhang, Ying; Wang, Anchen

doi:10.1155/2020/6216903

Cited by 15 publications

(24 citation statements)

References 36 publications

(51 reference statements)

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“…e method described in this paper uses the amplitude of the frequency response function feature vector. erefore, the increase of this energy component would have a certain impact on load identification [26][27][28]. In summary, within a certain range of motion speed, the load identification method has proved to have a high identification accuracy in this paper.…”

Section: Resultsmentioning

confidence: 70%

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Li¹,

Zhai²,

Wang³

et al. 2021

Shock and Vibration

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To explore the relationship between the cutting vibration and the cutting load of a single pick, this paper studied a new method for a single pick cutting rock load identification. This paper improved the low accuracy problem of the regularization method in the inverse process of frequency response function in the traditional load identification method by introducing a filter operator. By combining the inverse pseudoexcitation method and the improved regularization method, the identification of the load dependent on the vibration signal was realized. A single pick cutting rock test equipment was built, which could simulate the actual working conditions of pick cutting rock in the underground or tunnel. By changing cutting speed, cutting angle, cutting spacing, and cutting depth of the single pick, the change trends of real cutting load and identification load were obtained. The load identification method proposed in this paper was consistent with the change trend of the real load under the single pick cutting state. Therefore, the method had good recognition accuracy and the maximum load recognition error was 17.35%. Compared with other traditional load identification methods, the identification error was reduced by a maximum of 1.98%. This method can identify the cutting load of single pick and modify the morbidity problem of frequency response function matrix. The method has a better recognition effect on the cutting load of the pick than the traditional recognition methods. The research could benefit the design of the cutting system and the arrangement of the pick on the coal mine or tunneling machinery.

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Section: Resultsmentioning

confidence: 70%

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Li¹,

Zhai²,

Wang³

et al. 2021

Shock and Vibration

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show abstract

“…PSO has been chosen due to its simplicity, easy implementation, high accuracy, faster convergence, lesser control parameters, and computational efficiency (Zang et al, 2021;Zhang et al, 2014). Hence, owing to its numerous advantages, PSO has gained a lot of attention in many areas like signal denoising (Zhang et al, 2017), load forecasting (Kumar & Veerakumari, 2012), filter design (Sharma et al, 2016), image processing (Satish & Kumar, 2020), fault diagnosis (Zhang & Wang, 2020), etc. However, PSO suffers from the local optimum problem.…”

Section: Improved Particle Swarm Optimization (Ipso)mentioning

confidence: 99%

An efficient approach for anti-jamming in IRNSS receivers using improved PSO based parametric wavelet packet thresholding

Lorraine

Madhu

2022

Satell Navig

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The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India, extending up to 1500 km. However, when a receiver encounters a Continuous Wave Interference, its positioning accuracy degrades, or sometimes it even fails to work. Wavelet Packet Transform (WPT) is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers. But the conventional method suffers from threshold drifting and employs inflexible thresholding functions. So, to address these issues, an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding (IPSO-PWPT) is proposed. Firstly, a new parameter adaptive thresholding function is constructed. Then, a new form of inertia weight is presented to enhance the performance of PSO. Later, IPSO is used to optimize the key parameters of WPT. Finally, the implementation of the IPSO-PWPT anti-jamming algorithm is discussed. The performance of the proposed technique is evaluated for various performance metrics in four jamming environments. The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability. Also, the results show the ability of the new thresholding function to process various signals effectively. Furthermore, the findings reveal that the improved PSO outperforms the variants of PSO.

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“…Baseline data were gathered at a sampling frequency of 97,656 Hz and under a load of 270 lbs. Outer race fault data were gathered at a sampling frequency of 48,828 Hz and under seven different loads (25,50,100,150,200, 250, 300 lbs), and inner race fault data were gathered at a sampling frequency of 48,828 Hz and under seven different loads (0, 50, 100, 150, 200, 250, and 300 lbs). In this paper, five conditions of bearing data are used for fault diagnosis, shown in Table 8.…”

Section: Experimental Data and Parameter Settingsmentioning

confidence: 99%

Bearing fault diagnosis based on optimized variational mode decomposition and 1D convolutional neural networks

Wang

Yang

Wan

et al. 2021

Meas. Sci. Technol.

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Due to the fact that measured vibration signals from a bearing are complex and non-stationary in nature, and that impulse characteristics are always immersed in stochastic noise, it is usually difficult to diagnose fault symptoms manually. A novel hybrid fault diagnosis approach is developed for the denoising signals and fault classification in this work, which combines successfully the variational mode decomposition (VMD) and one dimensional convolutional neural network (1-D CNN). VMD is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the modal number and penalty parameter are very important in VMD, a particle swarm mutation optimization (PSMO) as a novel optimization method and the weighted signal difference average (WSDA) as a new fitness function are proposed to optimize the parameters of VMD. The reconstructed signals of mode components decomposed by optimized VMD are used as the input of the 1-D CNN to obtain fault diagnosis models. The performance of the proposed hybrid approach has been evaluated using the sets of experimental data of rolling bearings. The experimental results demonstrate that the VMD can eliminate signal noise and strengthen status characteristics, and the proposed hybrid approach has a superior capability for fault diagnosis from vibration signals of bearings.

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Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition

Cited by 15 publications

References 36 publications

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

Single Pick Cutting Rock Load Identification Based on Improved Regularization Method

An efficient approach for anti-jamming in IRNSS receivers using improved PSO based parametric wavelet packet thresholding

Bearing fault diagnosis based on optimized variational mode decomposition and 1D convolutional neural networks

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