An analog PID controller-based galvanometer scanner is widely used by fractional laser medical equipment (FLME) to scan lasers across tissue surfaces, achieving the desired therapeutic effect. This type of driver, primarily composed of passive components and operational amplifiers, can only accept commands from the central controller of the FLME, with a simple hardware circuit-based fault diagnosis; thus, the safety of the FLME is compromised. To address these issues, the failure mechanisms of galvanometers and their impact on the safety of FLME are thoroughly analyzed first. Then, an adaptive limit protection method, a coil open circuit fault diagnosis, a communication timeout protection based on two handshakes, and a galvanometer control timeout protection are proposed, respectively, based on a digital driver platform, to supplement the deficiencies in the original fault diagnosis and protection system. This ensures the safety of the FLME. Finally, the effectiveness of the proposed strategies is validated through experiments.
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http://dx.doi.org/10.12455/j.issn.1671-7104.230568 | DOI Listing |
Med Phys
January 2025
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Background: High-resolution brain imaging is crucial in clinical diagnosis and neuroscience, with ultra-high field strength MRI systems ( ) offering significant advantages for imaging neuronal microstructures. However, achieving magnetic field homogeneity is challenging due to engineering faults during the installation of superconducting strip windings and the primary magnet.
Purpose: This study aims to design and optimize active superconducting shim coils for a 7 T animal MRI system, focusing on the impact of safety margin, size, and adjustability of the second-order shim coils on the MRI system's optimization.
Sensors (Basel)
January 2025
Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China.
High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath grounding fault. However, there is a paucity of research on the defect of the reverse direction between the inner core and the outer shield of the coaxial cable.
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January 2025
Heime (Tianjin) Electrical Engineering Systems Co., Ltd., Tianjin 301700, China.
This paper introduces a novel geometry-based synchrosqueezing S-transform (GSSST) for advanced gearbox fault diagnosis, designed to enhance diagnostic precision in both planetary and parallel gearboxes. Traditional time-frequency analysis (TFA) methods, such as the Synchrosqueezing S-transform (SSST), often face challenges in accurately representing fault-related features when significant mode closely spaced components are present. The proposed GSSST method overcomes these limitations by implementing an intuitive geometric reassignment framework, which reassigns time-frequency (TF) coefficients to maximize energy concentration, thereby allowing fault components to be distinctly isolated even under challenging conditions.
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January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
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January 2025
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well as the issue of low fault diagnosis accuracy resulting from small sample quantities. This approach transforms the one-dimensional vibration signal into time-frequency diagrams using an SWT based on complex Morlet wavelet basis functions, which redistributes (squeezes) the values of the wavelet coefficients at different localized points in a time-frequency plane to the estimated instantaneous frequencies. This allows the energy to be more fully concentrated in actual corresponding frequency components.
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