Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

2011 ◽  
Vol 24 (05) ◽  
pp. 899 ◽  
Author(s):  
Guangfu BIN
Keyword(s):  
Correlation Function ◽  
Vibration Signal ◽  
Sensor Data ◽  
Fusion Method ◽  
Level Fusion ◽  
Data Level

scholarly journals Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation

2021 ◽  
Vol 11 (10) ◽  
pp. 4318
Author(s):  
Longhuan Cheng ◽  
Jiantao Lu ◽  
Shunming Li ◽  
Rui Ding ◽  
Kun Xu ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Original Data ◽  
Sensor Data ◽  
Fusion Method ◽  
Vibration Sensors ◽  
Fusion Data ◽  
Single Sensor ◽  
Spatio Temporal ◽  
Signal Processing Methods ◽  
Spatial Locations

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.

Research on Reactor Coolant Pump Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2013 ◽  
Author(s):  
Pan He ◽  
Caixue Liu ◽  
Qiong Ai
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Evidence Theory ◽  
Sensor Data ◽  
Coolant Pump ◽  
Reactor Coolant ◽  
Multi Sensor Data Fusion ◽  
Level Fusion ◽  
Data Level

Usually, more than one sensor is placed to collect vibration signals for reactor coolant pump condition monitoring. The traditional method of reactor coolant pump fault diagnosis does not make full use of the relativity of all vibration signals. In order to make full use of all vibration signals, multi-sensor data fusion is introduced to reactor coolant pump fault diagnosis and a universal reactor coolant pump fault diagnosis model is built up. The reactor coolant pump vibration data fusion diagnosis model is divided into three modules. The three modules are the data level fusion module, the BP (back-propagation) neural networks feature level fusion diagnosis module, the D-S (dempster-shafer) evidence theory decision level fusion module. The data level fusion module is to eliminate the disturbance and extract the feature information about reactor coolant pump faults. The feature information handled by the data level fusion module is used as the inputs of BP neural networks. The neural networks feature level fusion diagnosis module is composed by more than one BP neural networks in condition that the number of input nodes is too large. The feature information is divided into several troops and input into BP neural networks respectively. The outputs of neural networks serve as the basic probability assignment of D-S evidence theory. The D-S evidence theory decision level fusion module fuses the outputs of neural networks and gives the final fusion diagnosis result. The experiment results show that multi-sensor data fusion is successful and promising in reactor coolant pump fault diagnosis.

scholarly journals Multi-source data fusion method for structural safety assessment of water diversion structures

2021 ◽  
Author(s):  
Sherong Zhang ◽  
Ting Liu ◽  
Chao Wang
Keyword(s):  
Data Fusion ◽  
Safety Assessment ◽  
Evidence Theory ◽  
Weighted Average ◽  
Water Diversion ◽  
Sensor Data ◽  
Structural Safety ◽  
Fusion Method ◽  
Bhattacharyya Distance ◽  
Source Data

Abstract Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

Rank Level Fusion

2013 ◽  
pp. 69-79
Keyword(s):  
Information Aggregation ◽  
Regression Method ◽  
Borda Count ◽  
Fusion Method ◽  
Advantages And Disadvantages ◽  
Current State ◽  
Fusion Methods ◽  
Logistic Regression Method ◽  
Level Fusion ◽  
Voting Method

Rank level fusion is one of the after matching fusion methods used in multibiometric systems. The problem of rank information aggregation has been raised before in various fields. This chapter extensively discusses the rank level fusion methodology, starting with existing literature from the last decade in different application scenarios. Several approaches of existing biometric rank level fusion methods, such as plurality voting method, highest rank method, Borda count method, logistic regression method, and quality-based rank fusion method, are discussed along with their advantages and disadvantages in the context of the current state-of-the-art in the discipline.

A Score-Level Fusion Method for Protecting Fingerprint and Palmprint Templates

2021 ◽  
pp. 1-11
Author(s):  
Mulagala Sandhya ◽  
Y. Sreenivasa Rao ◽  
Sahoo Biswajeet ◽  
Vallabhadas Dilip Kumar ◽  
Maurya Anup Kumar
Keyword(s):  
Fusion Method ◽  
Score Level Fusion ◽  
Level Fusion
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