An evidence clustering DSmT approximate reasoning method for more than two sources

Guo, Qiang; He, You; Jian, Tao; Wang, Haipeng; Xia, Shunren

doi:10.1016/j.dsp.2016.05.007

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…The combination characteristic of DSmT can solve this problem effectively. The conflict rules are omitted, and the loss of effective information can be avoided [ 34 , 35 ].…”

Section: Key Fault Diagnosis Technologiesmentioning

confidence: 99%

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

Xie

Zhao-jian

et al. 2018

Sensors

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To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability.

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“…The combination characteristic of DSmT can solve this problem effectively. The conflict rules are omitted, and the loss of effective information can be avoided [ 34 , 35 ].…”

Section: Key Fault Diagnosis Technologiesmentioning

confidence: 99%

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

Xie

Zhao-jian

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Uncertainty management is a key point in uncertain information modeling; many theories and methods have been proposed for intelligent information processing, such as Shannon entropy, 17 probability theory, 18 fuzzy sets, 19 fuzzy inference, 20,21 D-S theory, 22,23 rough sets, 24 support vector machine, 25,26 belief function, [27][28][29] evidence reasoning, [30][31][32] and so on. 33,34 Among all these methods, D-S theory can process vague information modeled in fuzzy sets, and it can lead to information convergence in data fusion.…”

Section: Introductionmentioning

confidence: 99%

An improved belief entropy–based uncertainty management approach for sensor data fusion

Tang

Zhou

et al. 2017

International Journal of Distributed Sensor Networks

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In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy-based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster-Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertainty measure is expressed as the improved belief entropy. Evidence modification of conflict sensor data is based on the proposed uncertainty management approach before evidence fusion with Dempster's rule of combination. Finally, the fusion result can be applied in real applications. A case study on sensor data fusion for fault diagnosis is presented to show the rationality of the proposed method.

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DSmT-Based Group DEMATEL Method with Reaching Consensus

Zhou

2019

Group Decis Negot

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An evidence clustering DSmT approximate reasoning method for more than two sources

Cited by 3 publications

References 32 publications

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

An improved belief entropy–based uncertainty management approach for sensor data fusion

DSmT-Based Group DEMATEL Method with Reaching Consensus

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