scholarly journals Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Chen ◽  
Jie Han ◽  
Wenping Lei ◽  
Yongxiang Cui ◽  
Zhenhong Guan
Keyword(s):  
Data Acquisition ◽  
Information Fusion ◽  
Prediction Method ◽  
Fault Prediction ◽  
Vibration Measurement ◽  
Signal Acquisition ◽  
Vector Data ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Full Vector

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.

scholarly journals Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Chen ◽  
Jie Han ◽  
Wenping Lei ◽  
ZhenHong Guan ◽  
Yajuan Gao
Keyword(s):  
Prediction Model ◽  
Information Fusion ◽  
Health Management ◽  
Vibration Signal ◽  
Fault Prediction ◽  
Order Structure ◽  
Vibration Prediction ◽  
Potential Failure ◽  
Change Trend ◽  
Full Vector

Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.

Information Fusion Algorithm for Electromechanical Equipment Based on DS Evidence Theory

2013 ◽  
Vol 380-384 ◽  
pp. 1125-1128 ◽  
Author(s):  
Yao Hui Zhang ◽  
Jun Xu ◽  
Kang Du
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Prediction Method ◽  
Electrical Equipment ◽  
Fusion Algorithm ◽  
State Prediction ◽  
Level Information ◽  
The Difference ◽  
Equipment State ◽  
Electromechanical Equipment

According to the problem that the difference of test mode, mixed quantitative and qualitative information of electromechanical equipment state prediction, a state prediction method based on information fusion was proposed in this paper. It was used DS evidence theory to fuse decision level information of electromechanical equipments at this method. Simulation results showed that it is feasible and effective that information fusion technology is applied on the state prediction for mechanical and electrical equipment. Information for decision-making integrated repeatedly by different forecasting methods, can greatly reduce the blindness of judgment and improve the accuracy of state prediction.

scholarly journals Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhengna Qin ◽  
Haojie Liao ◽  
Ling Chen ◽  
Lei Zhang
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Information Fusion ◽  
High Efficiency ◽  
Enterprise Management ◽  
Enterprise Performance ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Maximum Acceleration ◽  
Index Weight

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.

Research of Selective and Incremental Information Fusion Method Based on Bayesian Network

2013 ◽  
Vol 397-400 ◽  
pp. 2060-2063
Author(s):  
Li Wei Zhang ◽  
Jing Zhang ◽  
Yan Sun
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Information Fusion ◽  
Relevant Information ◽  
Fusion Method ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Information Selection ◽  
Incremental Information ◽  
Better Than

This paper presents a selective incremental information fusion method based on Bayesian network, so that the fusion algorithm can actively select the most relevant information and decision-making, and can make the fusion model to adapt to the dynamic changes in the external environment, and sensor information selection, fusion, decision-making integrated in the framework of Bayesian network . The experimental results show that this method is better than the traditional method.

Research on Information Fusion of Two New DLL Discriminator Algorithms

2014 ◽  
Vol 543-547 ◽  
pp. 1223-1226
Author(s):  
Jian Cao ◽  
Cong Yan
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Information Fusion ◽  
Fuzzy Neural Network ◽  
Theoretical Method ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Feature Level Fusion ◽  
Fuzzy Neural ◽  
Level Fusion

After information fusion model has been established, the feature-level fusion algorithm based on fuzzy neural network and expert system is proposed, in which the expert system has been embedded into fuzzy neural network so that it could choose the membership function and adjust the network structure. At the same time, for code tracking loop, two new code phase discriminator algorithms based on DLL structure is proposed. Evidence theory has been applied to achieve the decision-making level fusion. The performances of the two algorithms were studied by using theoretical method and experimental method with analog IF signal data and actual IF signal data respectively. Then, the results of feature-level fusion have been taken as the evidences to construct the frame of discernment. The research results show that the process of information fusion has abilities of adapting and self-learning.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

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