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1 – 4 of 4Ahmed Taibi, Said Touati, Lyes Aomar and Nabil Ikhlef
Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and…
Abstract
Purpose
Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.
Design/methodology/approach
To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.
Findings
The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.
Originality/value
This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.
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Keywords
Xue Xin, Yuepeng Jiao, Yunfeng Zhang, Ming Liang and Zhanyong Yao
This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic…
Abstract
Purpose
This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.
Design/methodology/approach
The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.
Findings
The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.
Originality/value
The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.
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Li Li, Tong Huang, Chujia Pan, J.F. Pan and Wenbin Su
The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the…
Abstract
Purpose
The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.
Design/methodology/approach
An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.
Findings
The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.
Originality/value
The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.
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Keywords
Mamun Mishra and Bibhuti Bhusan Pati
Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability…
Abstract
Purpose
Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability, which are the two major issues with the islanding detection methods, are addressed here. The purpose of this paper is to devise an islanding detection method with zero NDZ and, which will be applicable to all types of renewable energy sources using the sequence components of the point of common coupling voltage.
Design/methodology/approach
Here, a parameter using the sequence components is derived to devise an islanding detection method. The parameter derived from the sequence components of point of common coupling voltage is analysed using wavelet transform. Various operating conditions, such as islanding and non-islanding, are considered for several test systems to evaluate the performance of the proposed method. All the simulations are carried out in Simulink/MATLAB environment.
Findings
The results showed that the proposed method has zero NDZ for both inverter- and synchronous generator-based renewable energy sources. In addition, the proposed method works satisfactorily as per the IEEE 1547 standards requirement.
Originality/value
Performance of the proposed method has been tested in several test systems and is found to be better than some conventional methods.
Details