Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

Ghosh, Nimisha; Paul, Rourab; Maity, Satyabrata; Maity, Krishanu; Saha, Sayantan

doi:10.1016/j.eswa.2020.113887

Cited by 36 publications

(15 citation statements)

References 36 publications

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“…Model. Firstly, the data of high-resolution tennis flight appearance model are processed by data fusion technology [14,15]. e data fusion algorithm used is D-S evidence theory, which mainly divides the evidence set into multiple irrelevant parts, independently judges the identification framework through the divided parts, and then recombines these parts through Dempster rules.…”

Section: Establishing Tracking Algorithmmentioning

confidence: 99%

Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

Tang¹

2021

Security and Communication Networks

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At present, the existing algorithm for detecting the parabola of tennis serves neglects the pre-estimation of the global motion information of tennis balls, which leads to great error and low recognition rate. Therefore, a new algorithm for detecting the parabola of tennis service based on video image analysis is proposed. The global motion information is estimated in advance, and the motion feature of the target is extracted. A tennis appearance model is established by sparse representation, and the data of high-resolution tennis flight appearance model are processed by data fusion technology to track the parabolic trajectory. Based on the analysis of the characteristics of the serve mechanics, according to the nonlinear transformation of the parabolic trajectory state vector, the parabolic trajectory starting point is determined, the parabolic trajectory is obtained, and the detection algorithm of the parabolic service is designed. Experimental results show that compared with the other two algorithms, the algorithm designed in this paper can recognize the trajectory of the parabola at different stages, and the detection accuracy of the parabola is higher in the three-dimensional space of the tennis service.

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Section: Establishing Tracking Algorithmmentioning

confidence: 99%

Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

Tang¹

2021

Security and Communication Networks

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“…A machine-learning literature review for classification or regression highlights penalizing a technique over another for accuracy enhancement. Analysis of other metrics such as the confusion matrix helps in improving the prediction results, with simple methods such as multichannel data fusion [20,43].…”

Section: Lpc-lstm Early Fusion-based Failure Diagnosismentioning

confidence: 99%

“…Artificial-intelligence-based diagnosis methodologies are a major focus for Industry 4.0 when based on the concept of the Internet of Things (IoT) [17], which allows for connecting everything to the Internet, such as machines and sensors [18]. To follow new Industry 4.0 trends, it is therefore necessary to consider designing autonomous expert systems [19], while benefiting from advantages of multisource data sensing for machine monitoring [20].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

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“…Much research has been done on high-speed train fault diagnosis methods. Fault diagnosis research based on evidence theory applies the improved DS(Dempster Shafer Theory) [12] method to trains for component fault feature extraction, which can effectively solve the fault diagnosis problems of multi-type sensors or data source fusion [13,14]. Empirical mode decomposition (EMD) train fault diagnosis [15][16][17][18] and feature dimension reduction [19,20] can greatly improve the reliability of train equipment and reduce manual maintenance costs.…”

Section: Related Workmentioning

confidence: 99%

A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration

et al. 2021

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Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.

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Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

Cited by 36 publications

References 36 publications

Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration

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