Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals

2021 ◽  
Author(s):  
Qun Chao ◽  
HaoHan Gao ◽  
JianFeng Tao ◽  
YuanHang Wang ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Multiple Channels ◽  
Vibration Signals ◽  
Fusion Strategy ◽  
Axial Piston Pumps ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Axial Piston ◽  
Level Fusion

scholarly journals Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3950
Author(s):  
Shuai Teng ◽  
Gongfa Chen ◽  
Zongchao Liu ◽  
Li Cheng ◽  
Xiaoli Sun
Keyword(s):  
Structural Damage ◽  
Control Experiment ◽  
Experimental Models ◽  
Detection Accuracy ◽  
Vibration Signals ◽  
One Dimensional ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Acceleration Signals ◽  
Level Fusion

This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16–30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

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