scholarly journals Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lili A. Wulandhari ◽  
Antoni Wibowo ◽  
Mohammad I. Desa
Keyword(s):  
Grey Relational Analysis ◽  
Back Propagation ◽  
Vibration Signal ◽  
Features Extraction ◽  
Vibration Signals ◽  
Relational Analysis ◽  
Diagnosis Method ◽  
Multiple Bearings ◽  
Grey Relational ◽  
Condition Diagnosis

Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA.

scholarly journals Combining Independent Component and Grey Relational Analysis for the Real-Time System of Hand Motion Identification Using Bend Sensors and Multichannel Surface EMG

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pei-Jarn Chen ◽  
Yi-Chun Du
Keyword(s):  
Grey Relational Analysis ◽  
Processing Time ◽  
Back Propagation ◽  
Surface Emg ◽  
Independent Component ◽  
Real Time System ◽  
Data Glove ◽  
Hand Motion ◽  
Relational Analysis ◽  
Grey Relational

This paper proposes a portable system for hand motion identification (HMI) using the features from data glove with bend sensors and multichannel surface electromyography (SEMG). SEMG could provide the information of muscle activities indirectly for HMI. However it is difficult to discriminate the finger motion like extension of thumb and little finger just using SEMG; the data glove with five bend sensors is designed to detect finger motions in the proposed system. Independent component analysis (ICA) and grey relational analysis (GRA) are used to data reduction and the core of identification, respectively. Six features are extracted from each SEMG channel, and three features are computed from five bend sensors in the data glove. To test the feasibility of the system, this study quantitatively compares the classification accuracies of twenty hand motions collected from 10 subjects. Compared to the performance with a back-propagation neural network and only using GRA method, the proposed method provides equivalent accuracy (>85%) with three training sets and faster processing time (20 ms). The results also demonstrate that ICA can effectively reduce the size of input features with GRA methods and, in turn, reduce the processing time with the low price of reduced identification rates.

Research on the Application of Weighted Grey Relational Analysis on the Failure Diagnosis of Complicated Electronic Equipment

2013 ◽  
Vol 333-335 ◽  
pp. 1543-1547
Author(s):  
Hong Yan Zhao ◽  
Jun Zhang ◽  
Guo Ping Hu ◽  
Jian Qiang Zhang
Keyword(s):  
Failure Mode ◽  
Grey Relational Analysis ◽  
Failure Diagnosis ◽  
Reference Sequence ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
The Individual ◽  
Grey Relational

Based on weighted grey relational analysis, a new failure diagnosis method for complicated electronic equipments is proposed. First, according to the typical failure samples and weight values to construct grey reference sequence. Secondly, calculating the individual relational coefficient and grade to form grey relational grade sequence. Finally, according to the maximal grey relational grade to choose the corresponding failure mode as the finally diagnosis result. The results of analyses show that the proposed method has higher diagnosis accuracy and reliability than the traditional grey relational method.

Fault diagnosis based on grey relational analysis and synergetic pattern recognition for aero-engine gas-path systems

2019 ◽  
Vol 233 (12) ◽  
pp. 4598-4605
Author(s):  
Bangcheng Zhang ◽  
Jing Chen ◽  
Xiaojing Yin ◽  
Zhi Gao
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Grey Relational Analysis ◽  
Relational Analysis ◽  
Aero Engine ◽  
Diagnosis Method ◽  
Path System ◽  
Aero Engines ◽  
The Many ◽  
Grey Relational

The gas-path system is an important sub-system in aero-engines. There are various indistinguishable faults in aero-engine gas-path systems. These faults are easily misjudged because the characteristic parameters are similar. Due to the many kinds of faults, current studies have poor accuracy in distinguishing similar faults. To improve fault diagnosis accuracy for gas-path systems, a fault diagnosis method based on grey relational analysis and synergetic pattern recognition is proposed. In the proposed method, grey relational analysis is used to initially distinguish the faults into different types and obtain similar fault types. Synergetic pattern recognition contributes to accurately diagnose faults which are difficult to recognize. A case study is used to verify the effectiveness and accuracy of the proposed model. The results show that faults in common types of gas-path systems can be diagnosed accurately by the proposed method.

The Weighting Grey Relational Analysis of the Fault Diagnosis of Frame Cross’s Fracture

2011 ◽  
Vol 65 ◽  
pp. 255-259 ◽  
Author(s):  
Yan Zhang ◽  
Hui Song ◽  
Guan Jun Meng ◽  
Yan Wang
Keyword(s):  
Fault Diagnosis ◽  
Grey Relational Analysis ◽  
Fault Tree Analysis ◽  
New Method ◽  
Relational Analysis ◽  
Accident Probability ◽  
Diagnosis Method ◽  
Incidence Analysis ◽  
Grey Relational ◽  
The Relationship

Fault tree analysis is a fault diagnosis method that is better suited for "top-down" analysis. It can effectively evaluate cause-and-effect relationship and accident probability. In this paper, a new method of weighting grey relational analysis was applied in FTA. The principles of grey incidence analysis were introduced in detail. And the new method was used in the analysis of automobile frame cross’s fracture. The relationship between the system’s failure characters and its inside characters was found, and the hazardous events were worked out. The example results can prove that the weighting grey relational analysis of FTA is available and practicable, and the diagnosis results are reliable.

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