An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis

The multisensor data fusion method has been extensively utilized in many practical applications involving testability evaluation. Due to the flexibility and effectiveness of Dempster–Shafer evidence theory in modeling and processing uncertain information, this theory has been widely used in various fields of multisensor data fusion method. However, it may lead to wrong results when fusing conflicting multisensor data. In order to deal with this problem, a testability evaluation method of equipment based on multisensor data fusion method is proposed. First, a novel multisensor data fusion method, based on the improvement of Dempster–Shafer evidence theory via the Lance distance and the belief entropy, is proposed. Next, based on the analysis of testability multisensor data, such as testability virtual test data, testability test data of replaceable unit, and testability growth test data, the corresponding prior distribution conversion schemes of testability multisensor data are formulated according to their different characteristics. Finally, the testability evaluation method of equipment based on the multisensor data fusion method is proposed. The result of experiment illustrated that the proposed method is feasible and effective in handling the conflicting evidence; besides, the accuracy of fusion of the proposed method is higher and the result of evaluation is more reliable than other testability evaluation methods, which shows that the basic probability assignment of the true target is 94.71%.

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A Robust Short-Circuit Fault Diagnosis for High Voltage DC Power Supply Based on Multisensor Data Fusion

2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC) ◽

10.1109/pedstc.2019.8697746 ◽

2019 ◽

Author(s):

Ramin Ayoubi ◽

Shahriyar Kaboli

Keyword(s):

Fault Diagnosis ◽

Data Fusion ◽

High Voltage ◽

Power Supply ◽

Short Circuit ◽

Multisensor Data Fusion ◽

Dc Power ◽

Circuit Fault Diagnosis ◽

Short Circuit Fault ◽

Multisensor Data

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Multisensor Data Fusion Based on Independent Component Analysis for Fault Diagnosis of Rotor

Advances in Neural Networks – ISNN 2004 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-28647-9_122 ◽

2004 ◽

pp. 744-749 ◽

Cited By ~ 1

Author(s):

Xiaojiang Ma ◽

Zhihua Hao

Keyword(s):

Fault Diagnosis ◽

Independent Component Analysis ◽

Data Fusion ◽

Component Analysis ◽

Independent Component ◽

Multisensor Data Fusion ◽

Multisensor Data

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Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2020.106861 ◽

2020 ◽

Vol 144 ◽

pp. 106861 ◽

Cited By ~ 10

Author(s):

Moslem Azamfar ◽

Jaskaran Singh ◽

Inaki Bravo-Imaz ◽

Jay Lee

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Data Fusion ◽

Convolutional Neural Network ◽

Signature Analysis ◽

Multisensor Data Fusion ◽

Motor Current ◽

Motor Current Signature Analysis ◽

Current Signature ◽

Multisensor Data

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Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS

7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002. ◽

10.1109/icarcv.2002.1235006 ◽

2004 ◽

Cited By ~ 10

Author(s):

Chunling Fan ◽

Zhihua Jin ◽

Jing Zhang ◽

Weifeng Tian

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Data Fusion ◽

Rbf Neural Networks ◽

Multisensor Data Fusion ◽

Multisensor Data

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Study on Multisensor Data Fusion of Ultrasonic Sensor

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.722.44 ◽

2013 ◽

Vol 722 ◽

pp. 44-48

Author(s):

Bao Jun Liu

Keyword(s):

Embedded Systems ◽

Data Fusion ◽

Fuzzy Set ◽

Measured Data ◽

Fusion Method ◽

Multisensor Data Fusion ◽

Mean And Variance ◽

Outlier Data ◽

Final Expression ◽

Multisensor Data

Aiming at data fusion of Autonomous car multisensors experiment at many times on distances, a novel fusion method is proposed based on the approach degree and weights. The method calculate mean and variance based on the measured sensors data, Using the maximum and minimum approach degree of this fuzzy set , the approach degree of the measured data from various sensors is processed quantitatively ,eliminating outlier data by Grubbs method, assigned the weights of data measured in the fusion process reasonably , so that the final expression of the data fusion is obtained, thus the data fusion of multisensor is realized.Test results demonstrate that this method can bring higher fusion precision, and more suitable for microcontroller and embedded systems applications.

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A Multisensor Data Fusion Method Based on Gaussian Process Model for Precision Measurement of Complex Surfaces

Sensors ◽

10.3390/s20010278 ◽

2020 ◽

Vol 20 (1) ◽

pp. 278

Author(s):

Ji Ding ◽

Qiang Liu ◽

Mingxuan Bai ◽

Pengpeng Sun

Keyword(s):

Data Fusion ◽

Gaussian Process ◽

Process Model ◽

Surface Registration ◽

Measurement Technology ◽

Fusion Method ◽

Registration Method ◽

Gaussian Process Model ◽

Multisensor Data Fusion ◽

Multisensor Data

As multisensor measurement technology is rapidly applied in industrial production, one key issue is the data fusion procedure by combining several datasets from multiple sensors to obtain the overall geometric measurement. In this paper, a multisensor data fusion method based on a Gaussian process model is proposed for complex surface measurements. A robust surface registration method based on the adaptive distance function is firstly used to unify the coordinate systems of different measurement datasets. By introducing an adjustment model, the residuals between several independent datasets from different sensors are then approximated to construct a Gaussian process model-based data fusion system. The proposed method is verified through both simulation verification and actual experiments, indicating that the proposed method can fuse multisensor measurement datasets with better fusion accuracy and faster computational efficiency compared to the existing method.

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A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion

Shock and Vibration ◽

10.1155/2019/7490750 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Xiafei Long ◽

Ping Yang ◽

Hongxia Guo ◽

Zhuoli Zhao ◽

Xiwen Wu

Keyword(s):

Fault Diagnosis ◽

Data Fusion ◽

Wind Turbines ◽

Time Domain ◽

Time Domain Analysis ◽

Domain Analysis ◽

Wind Turbine Gearbox ◽

Operational Conditions ◽

Multisensor Data Fusion ◽

Multisensor Data

Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. Then, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. The dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. The effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances.

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