The Feature Extraction Method of Gear Magnetic Memory Signal

2013 ◽  
Vol 819 ◽  
pp. 206-211
Author(s):  
Yong Gang Xu ◽  
Zhi Cong Xie ◽  
Lin Li Cui ◽  
Jing Wang
Keyword(s):  
Gear Tooth ◽  
Low Frequency ◽  
Magnetic Memory ◽  
Magnetic Signal ◽  
Heavy Load ◽  
Low Speed ◽  
Feature Extraction Method ◽  
Test Technology ◽  
Time Scale Decomposition ◽  
Memory Signals

Magnetic memory test technology is a new nondestructive testing technique, which is able to detect of the stress concentration area and potential fault of low speed and heavy load gear. Because the magnetic memory signals are easy to be disturbed by various sources of noises, a new method based on the intrinsic time-scale decomposition (ITD) is proposed to achieve the extraction of magnetic memory signal. Firstly, the magnetic memory signals are decomposed into several proper rotation components (PRC) and a trend component by ITD. Then reconstruct the first four order PRCs to eliminate the low frequency cyclic composition of magnetic memory signal and magnetic noise. Finally, the magnetic signal strengths of each gear tooth root are extracted using cycle average and local statistic method. The results of Experiments show that the method is suitable to pick up effective ingredients of signal to extract signal feature and has important application value in potential fault diagnosis of low speed and heavy load gearbox.

scholarly journals Accuracy Improvement of Stator Inductance Identification Method Based on Low-Frequency Current Injection for Three-Level NPC Inverter-Fed IM Drives in Locked-Rotor Standstill Condition

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 488
Author(s):  
Yerganat Khojakhan ◽  
Kyoung-Min Choo ◽  
Junsin Yi ◽  
Chung-Yuen Won
Keyword(s):  
Induction Motor ◽  
Low Frequency ◽  
Current Injection ◽  
Heavy Load ◽  
Injection Test ◽  
Low Speed ◽  
Injection Speed ◽  
Speed Test ◽  
Identification Method ◽  
Drive Systems

In this paper, a stator inductance identification process is proposed. The process is based on a three-level neutral-point-clamped (NPC) inverter-fed induction motor (IM) drive with a standstill condition. Previously, a low-speed alternating current (AC) injection test for stator inductance identification was proposed to overcome practical problems in conventional identification methods for three-level NPC inverter-based IM drives. However, the low-speed AC injection test-based identification method has some problems if a heavy load or mechanical brake is connected, as these can forcibly bring the rotor to a standstill during parameter identification. Since this low-speed testing-based identification assumes the motor torque is considerably lower in low-speed operations, some inaccuracy is inevitable in this kind of standstill condition. In this paper, the proposed current injection speed generator is based on the previously studied low-speed test-based stator inductance identification method, but the proposed approach gives more accurate estimates under the aforementioned standstill conditions. The proposed method regulates the speed for sinusoidal low-frequency AC injection on the basis of the instantaneous reactive and air-gap active power ratio. This proposed stator inductance identification method is more accurate than conventional fixed low-frequency AC signal injection identification method for three-level NPC inverter-fed IM drive systems with a locked-rotor standstill condition. The proposed method’s accuracy and reliability were verified by simulation and experiment using an 18.5 kW induction motor.

scholarly journals Cancellation exponents and multifractal scaling laws in the solar wind magnetohydrodynamic turbulence

1996 ◽  
Vol 14 (8) ◽  
pp. 777-785 ◽  
Author(s):  
V. Carbone ◽  
R. Bruno
Keyword(s):  
Solar Wind ◽  
Velocity Field ◽  
Scaling Laws ◽  
Interplanetary Medium ◽  
Low Frequency ◽  
Scaling Exponent ◽  
Magnetohydrodynamic Turbulence ◽  
Low Speed ◽  
Multifractal Scaling

Abstract. Some signed measures in turbulence are found to be sign-singular, that is their sign reverses continuously on arbitrary finer scales with a reduction of the cancellation between positive and negative contributions. The strength of the singularity is characterized by a scaling exponent κ, the cancellation exponent. In the present study by using some turbulent samples of the velocity field obtained from spacecraft measurements in the interplanetary medium, we show that sign-singularity is present everywhere in low-frequency turbulent samples. The cancellation exponent can be related to the characteristic scaling laws of turbulence. Differences in the values of κ, calculated in both high- and low-speed streams, allow us to outline some physical differences in the samples with different velocities.

Effect of tensile stress on metal magnetic memory signals during on-line measurement in ferromagnetic steel

2021 ◽  
Vol 117 ◽  
pp. 102378
Author(s):  
Huipeng Wang ◽  
Lihong Dong ◽  
Haidou Wang ◽  
Guozheng Ma ◽  
Binshi Xu ◽  
...  
Keyword(s):  
Tensile Stress ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Line Measurement ◽  
On Line ◽  
Memory Signals ◽  
Ferromagnetic Steel

scholarly journals Stator Inductance Identification Based on Low-Speed Tests for Three-Level NPC Inverter-Fed Induction Motor Drives

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 183 ◽  
Author(s):  
Yerganat Khojakhan ◽  
Kyoung-Min Choo ◽  
Chung-Yuen Won
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Low Frequency ◽  
Test Drive ◽  
Identification Process ◽  
Low Speed ◽  
Speed Test ◽  
Simulation And Experiment ◽  
Sinusoidal Current ◽  
Stator Flux

This paper proposes a stator inductance identification process for three-level neutral point clamped (NPC), inverter-fed Induction Motor (IM) drives based on a low-speed test drive. Conventionally, the stator inductance of an IM is identified by methods based on standstill or rotational tests. Since conventional standstill test-based methods have several practical problems when used with three-level inverters because of their nonlinearity, an identification method based on rotational tests is superior in such applications. However, conventional rotational tests cause unintended behavior because of the high speeds used during the test. In the proposed stator inductance identification process, the stator inductance is identified based on a low-speed test drive. In the proposed method, the stator flux is estimated using the instantaneous reactive power of the IM during low-frequency sinusoidal current excitation, and the stator inductance is then identified based upon this. Therefore, the proposed identification process is safer than conventional approaches, as it uses only a low-speed test. The accuracy and reliability of this method are verified by simulation and experiment using three motors with different rated voltage and power.

scholarly journals A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7155
Author(s):  
Zejun Zheng ◽  
Dongli Song ◽  
Xiao Xu ◽  
Lei Lei
Keyword(s):  
Feature Extraction ◽  
Health Status ◽  
Fault Diagnosis ◽  
High Speed ◽  
Energy Operator ◽  
Ar Model ◽  
Diagnostic Efficiency ◽  
Heavy Load ◽  
Feature Extraction Method ◽  
Vibration Data

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

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