Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources

Wen, Peng; Li, Yong; Chen, Shaowei; Zhao, Shengzhi

doi:10.1109/jiot.2021.3055977

Cited by 42 publications

(12 citation statements)

References 47 publications

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“…Then, Eq (10) can express the difference between evidence i and all others that affects the conclusion.…”

Section: Plos Onementioning

confidence: 99%

“…Information fusion technology has solved many troubles [1][2][3][4][5][6][7] in the military, engineering, and environment since it developed in the 1970s [8]. The application have expanded to much more areas, such as extra energy, new materials, manufacturing, medicine, agriculture, transportation, and economy [9][10][11][12][13][14][15]. The utilization of information fusion technology enhances the system fault tolerance, self-adaptability, and reduces inference fuzziness.…”

Section: Introductionmentioning

confidence: 99%

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Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

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“…Then, Eq (10) can express the difference between evidence i and all others that affects the conclusion.…”

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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show abstract

“…Deep-Air [95] introduced a novel hybrid CNN with LSTM to provide fine-grained city-wide air pollution estimation and station-wide forecasts on air quality. In smart industry, Wen et al [279] developed a hybrid sensor fusion system to accurately predict the remaining useful life (RUL) of IoT-enabled complex industrial systems. These advanced learning algorithms have also been deployed to help in smart health, smart grid, and other domains [77,98,149,267].…”

Section: Iot Sensor Interconnection (Isi)mentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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“…Guel et al 12 directly fused multiple raw AE data by calculating the average value to detect damage, which can merge complementary AE sensors and enhance detection reliability. Zhang et al 13 adopted genetic programming to integrate sensor sources in data level, and the fusion effect was improved by combining the randomness and transmissibility of genes. Sheida et al 14 proposed data-level fusion method by direct splicing raw data after dimensionality reduction with principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Song

Shen

et al. 2022

Structural Health Monitoring

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Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions.

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Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources

Cited by 42 publications

References 47 publications

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Graph Neural Networks in IoT: A Survey

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

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