scholarly journals A Continual Learning Fault Diagnosis Method for Discontinuous Time-varying Sample Space

2021 ◽  
Vol 57 (1) ◽  
pp. 157
Keyword(s):  
Fault Diagnosis ◽  
Sample Space ◽  
Time Varying ◽  
Diagnosis Method ◽  
Continual Learning

The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane

2011 ◽  
Vol 308-310 ◽  
pp. 88-91
Author(s):  
Hong Bo Xu ◽  
Guo Hua Chen ◽  
Xin Hua Wang ◽  
Jun Liang
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Moving Average ◽  
Arma Model ◽  
Time Varying ◽  
Stability Evaluation ◽  
Test Results ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Auto Regressive

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

scholarly journals Study on the Fault Diagnosis Method of Scraper Conveyor Gear under Time-Varying Load Condition

2020 ◽  
Vol 10 (15) ◽  
pp. 5053
Author(s):  
Shuanfeng Zhao ◽  
Pengfei Wang ◽  
Shijun Li
Keyword(s):  
Fault Diagnosis ◽  
Load Condition ◽  
Analytical Signal ◽  
Vibration Signal ◽  
Frequency Component ◽  
Time Varying ◽  
Fault Strength ◽  
Scraper Conveyor ◽  
Gear Fault ◽  
Diagnosis Method

Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

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