scholarly journals Performance evaluation of multi-sensor data fusion technique for test range application

Sadhana ◽  
2004 ◽  
Vol 29 (2) ◽  
pp. 237-247 ◽  
Author(s):  
Shrabani Bhattacharya ◽  
R. Appavu Raj
Keyword(s):  
Performance Evaluation ◽  
Data Fusion ◽  
Sensor Data ◽  
Fusion Technique ◽  
Test Range ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

scholarly journals A review on multi sensor data fusion technique in CNC machining of tailor-made nanocomposites

2020 ◽  
Vol 2 (5) ◽  
Author(s):  
K. V. V. N. R. Chandra Mouli ◽  
Balla Srinivasa Prasad ◽  
A. V. Sridhar ◽  
Sandeep Alanka
Keyword(s):  
Data Fusion ◽  
Cnc Machining ◽  
Sensor Data ◽  
Fusion Technique ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

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.

Analysis of rocket propulsion test data using multi-sensor data fusion technique

2021 ◽  
Vol 46 (1) ◽  
pp. 108-113
Author(s):  
Mallappa ◽  
S. Ramesh ◽  
D. G. Chandra ◽  
A. Rajan ◽  
T. K. Nandi
Keyword(s):  
Data Fusion ◽  
Test Data ◽  
Sensor Data ◽  
Fusion Technique ◽  
Rocket Propulsion ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion
Sign in / Sign up

Export Citation Format

Share Document