An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM

et al. 2021

Sensors

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

Section: Wavelet Kernel Extreme Learning Machinementioning

confidence: 99%

A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Jiang

et al. 2021

Sensors

“…The nonlinear positioning can be addressed by geometric correction techniques. 14 Similarly, Zhang 15 further measured data spots and performed online calibration of the GS.…”

Section: Modulationmentioning

confidence: 99%

“…Offset/drift errors can be addressed by online correction on the fly, in a similar method to Zhang. 15 Offset errors are measured in microradians. A typical value from a high-quality GS manufacturer is less than 10 microradians.…”

Section: Offset/drift Errorsmentioning

confidence: 99%

Lens-Free Optical Scanners for Metal Additive Manufacturing

Bibas

2022

JOM

Galvanometer scanners (GSs) driving selective laser sintering (SLS)/selective laser melting (SLM) printers for additive manufacturing (AM) have mechanical limits. They provide inconsistent energy density across the print surface because of changes in optical path length, surface beam speed, and angle of incidence. The resulting thermal gradients may be particularly problematic for metal, whose high heat conductivity makes temperature prediction during printing critical. In this paper, we mathematically analyze and compare GSs with a new lens-free optical scanner. The results show that the latter can facilitate metal printing by providing consistent energy deposition across the print surface.

“…In order to achieve target tracking, the system needs to calculate the target moving amount (Δ X , Δ Z ) according to the spot position value false(xδ,yδfalse), then calculate the adjusting angles of the 2D galvanometer ( θx , θz ), and adjust the rotation of the galvanometer servo motor, until the value of spot position detection is (0,0) and the indicator laser is re-projected into the center of the target prism. The relationship between the target moving distance (Δ X , Δ Z ) and the rotation angles of the 2D galvanometer can be described as in [18]. {leftleftθx=12normalarcsinΔXD−lleftθz=12normalarcsinΔZ(D−l)2−ΔX2−e…”

Section: The Principle and Experimental Equipment Of The Systemmentioning

confidence: 99%

“…According to the angles ( θx , θz ) of the 2D galvanometer and the target distance D, the positioning mathematical model of LTPS can be obtained as follows [18]:[xyz]=false(D−lfalse)·[cos2θx·sin2θzcos2θx·cos2θzsin2θx]+[−e·sin2θz−e·cos2θz0] where false(x,y,zfalse) is the current coordinate of the moving target.…”

Section: The Principle and Experimental Equipment Of The Systemmentioning

confidence: 99%

An Improved Method for Spot Position Detection of a Laser Tracking and Positioning System Based on a Four-Quadrant Detector

Guo

et al. 2019

Sensors

For the laser tracking and positioning system of a moving target using a four-quadrant detector, the accuracy of laser spot position detection has a serious impact on the tracking performance of the system. For moving target tracking, the traditional spot position detection method of a four-quadrant detector cannot give better consideration to both detection accuracy and operation speed. In view of this, an improved method based on piecewise low-order polynomial least squares fitting and a Kalman filter is proposed. Firstly, the tracking and positioning mathematical model of the system is created, and the experimental device is established. Then, the shortcomings of traditional methods are analyzed, and the improved method and the real-time tracking and positioning algorithm of the system are studied. Finally, through the experiment, the system operation effects are compared and analyzed before and after the improvement. The experimental results of system dynamic tracking show that, the least squares fitting of the experimental data using a 5-segment and quadratic polynomial can achieve better results. By using the improved method, the maximum tracking distance of a moving object is increased from 12 m to more than 30 m. At a distance of 7.5 m, the maximum tracking speed can reach 2.11 m/s, and the root mean square error (RMSE) of the position is less than 4.59 mm. At 15.5 m, the maximum tracking speed is 2.04 m/s and the RMSE is less than 5.42 mm. Additionally, at 23.5 m, it is 1.13 m/s and 5.71 mm.