scholarly journals A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69907-69917 ◽  
Author(s):  
Weiwei Qian ◽  
Shunming Li ◽  
Jinrui Wang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Learning Method ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Machine Fault

Development of a Master-Slave Architecture Handheld Rotating Machine Fault Diagnosis Instrument

2011 ◽  
Vol 141 ◽  
pp. 244-250
Author(s):  
Jian Wan ◽  
Tai Yong Wang ◽  
Jing Chuan Dong ◽  
Pan Zhang ◽  
Yan Hao
Keyword(s):  
Fault Diagnosis ◽  
Signal Integrity ◽  
Module Design ◽  
Time Performance ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Slave Processor ◽  
Design Idea ◽  
Machine Fault ◽  
Communication Methods

To insure that sampling signal integrity, accuracy and real-time performance can adapt to the development of rotating machine fault diagnosis technology, a master-slave architecture handheld rotating machine fault diagnosis instrument was developed based on S3C2410 ARM IC and TMS320VC5509A DSP IC. It provided an effective method for the field monitoring and diagnosis of the large rotating machine. The whole design idea and the structure of the hardware and the software were systematically introduced. The paper focused on the master-slave architecture design of the hardware, the communication methods between the master and the slave processor, and the signal pretreatment module design. Put into practice, the practicability, reliability and stability of the instrument were confirmed.

Enhanced Rotating Machine Fault Diagnosis Based on Time-Delayed Feedback Stochastic Resonance

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Siliang Lu ◽  
Qingbo He ◽  
Haibin Zhang ◽  
Fanrang Kong
Keyword(s):  
Fault Diagnosis ◽  
Signal Detection ◽  
Stochastic Resonance ◽  
Delayed Feedback ◽  
Weak Signal ◽  
Noise Interference ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Time Delayed Feedback

The fault-induced impulses with uneven amplitudes and durations are always accompanied with amplitude modulation and (or) frequency modulation, which leads to that the acquired vibration/acoustic signals for rotating machine fault diagnosis always present nonlinear and nonstationary properties. Such an effect affects precise fault detection, especially when the impulses are submerged in heavy background noise. To address this issue, a nonstationary weak signal detection strategy is proposed based on a time-delayed feedback stochastic resonance (TFSR) model. The TFSR is a long-memory system that can utilize historical information to enhance the signal periodicity in the feedback process, and such an effect is beneficial to periodic signal detection. By selecting the proper parameters including time delay, feedback intensity, and calculation step in the regime of TFSR, the weak signal, the noise, and the potential can be matched with each other to an extreme, and consequently a regular output waveform with low-noise interference can be obtained with the assistant of the distinct band-pass filtering effect. Simulation study and experimental verification are performed to evaluate the effectiveness and superiority of the proposed TFSR method in comparison with a traditional stochastic resonance (SR) method. The proposed method is suitable for detecting signals with strong nonlinear and nonstationary properties and (or) being subjected to heavy multiscale noise interference.

Multiple Discolored Cyclic Harmonic Ratio Diagram Based on Meyer Wavelet Filters for Rotating Machine Fault Diagnosis

2020 ◽  
Vol 20 (6) ◽  
pp. 3132-3141 ◽  
Author(s):  
Chong Luo ◽  
Zhenling Mo ◽  
Jianyu Wang ◽  
Jing Jiang ◽  
Wenxin Dai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Filters ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Meyer Wavelet ◽  
Harmonic Ratio
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