Fault Diagnosis of Induction Motor Based on Multi-Sensor Data Fusion

2014 ◽  
Vol 651-653 ◽  
pp. 729-732
Author(s):  
Shu Ying Li ◽  
Mu Qin Tian ◽  
Lei Xue
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Induction Motor ◽  
Sensor Data ◽  
Fusion Technology ◽  
Accurate Identification ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Early Failures ◽  
Feature Information

For the conclusions of single parameter fault feature diagnosis has some uncertainty, in induction motor early fault, we proposed the use of multi-sensor data fusion technology, acted signal processing to the collected current, vibration and temperature, extracted feature information failure, fused the evidence independent with each other using D-S evidence fusion rules. According to the final combination results of all the evidence, combined with intermediate results of the evidence combination, we achieved the accurate identification of induction motor rotor early failures and composite fault. The diagnosis examples show that the use of multi-sensor data fusion technology can significantly improve the accuracy and reliability of early fault diagnosis.

Research of Dynamic Weighing System Based on Multi-Sensor Data Fusion Technology

2013 ◽  
Vol 721 ◽  
pp. 479-482 ◽  
Author(s):  
Yan Xia Wang ◽  
Chun Hui Bao ◽  
Chun Ling Fan
Keyword(s):  
Data Fusion ◽  
Electromagnetic Interference ◽  
New Method ◽  
Sensor Data ◽  
Fusion Technology ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Load Cells ◽  
Noise Vibration ◽  
Dynamic Weighing

The multi-sensor data fusion techniques is discussed in dynamic weighing system based on the data measured from ultrasonic sensors, speed sensors, capacitive sensors and load cells. This new method can greatly increase the measure precision of the dynamic weighing systems, at the same time it can effectively reduce noise, vibration, electromagnetic interference and other environmental factors on the influence of dynamic weighing system measurement. Judging from the simulation result, this new method proves to be more accurate and stable than ordinary processing methods.

Study on Leak Detecting System for Oil Pipeline Based on Multi-Sensor Data Fusion Technique

2012 ◽  
Vol 220-223 ◽  
pp. 1898-1901
Author(s):  
Jie Zhang ◽  
Xiao Wang ◽  
Zhen Zhao
Keyword(s):  
Wavelet Transform ◽  
Data Fusion ◽  
Real Time ◽  
Sensor Data ◽  
Time Signal ◽  
Fusion Technology ◽  
Transform Method ◽  
Oil Pipeline ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

In this paper, based on the wavelet transform method, the multi-sensor data fusion technology is adopted to solve some key problems in pipeline leak detection system. Compared with the traditional filtering method, the wavelet transform method can better remove the pipeline leakage signal noise. When the corresponding wavelet is selected, after FPGA realized, the method has the advantages of fast speed, arbitrarily data width set, which can better meet the needs of real-time signal processing requirements of pipeline leak. At the same time, VHDL language has the characteristics of portability, greater generality. When multi-sensor fusion technology is used in the paper, the relation, correlation, estimation and integrated processing is done in turn to the information from multiple data sources, the system can achieve better detection performance than a system using the single sensor, so as to form a more complete, reliable pipeline real time detecting conclusion.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

Investigation of a multi-sensor data fusion technique for the fault diagnosis of gearboxes

2019 ◽  
Vol 233 (13) ◽  
pp. 4764-4775 ◽  
Author(s):  
Jun He ◽  
Shixi Yang ◽  
Evangelos Papatheou ◽  
Xin Xiong ◽  
Haibo Wan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Sensor Data ◽  
Health Conditions ◽  
Mechanical Transmission ◽  
Selection Scheme ◽  
Vibration Signals ◽  
Fusion Technique ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

Gearbox is the key functional unit in a mechanical transmission system. As its operating condition being complex and the interference transmitting from diverse paths, the vibration signals collected from an individual sensor may not provide a fully accurate description on the health condition of a gearbox. For this reason, a new method for fault diagnosis of gearboxes based on multi-sensor data fusion is presented in this paper. There are three main steps in this method. First, prior to feature extraction, two signal processing methods, i.e. the energy operator and time synchronous averaging, are applied to multi-sensor vibration signals to remove interference and highlight fault characteristic information, then the statistical features are extracted from both the raw and preprocessed signals to form an original feature set. Second, a coupled feature selection scheme combining the distance evaluation technique and max-relevance and min-redundancy is carried out to obtain an optimal feature set. Finally, the deep belief network, a novel intelligent diagnosis method with a deep architecture, is applied to identify different gearbox health conditions. As the multi-sensor data fusion technique is utilized to provide sufficient and complementary information for fault diagnosis, this method holds the potential to overcome the shortcomings from an individual sensor that may not accurately describe the health conditions of gearboxes. Ten different gearbox health conditions are simulated to validate the performance of the proposed method. The results confirm the superiority of the proposed method in gearbox fault diagnosis.

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