Fault diagnosis method via one vs rest evidence classifier considering imprecise feature samples

Xu, Xiaobin; Guo, Haohao; Zhang, Zhenjie; Shi, Pengfei; Huang, Wenguang; Li, Xiaoding; Brunauer, Georg

doi:10.1016/j.asoc.2024.111761

Applied Soft Computing

2024

DOI: 10.1016/j.asoc.2024.111761

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Fault diagnosis method via one vs rest evidence classifier considering imprecise feature samples

Xiaobin Xu,

Haohao Guo,

Zhenjie Zhang

et al.

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2024

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Cited by 2 publications

References 38 publications

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Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

Qi,

Du,

Huo

et al. 2024

Future Internet

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The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.

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Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

Qi,

Du,

Huo

et al. 2024

Future Internet

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Complex Evidence Theory for Multisource Data Fusion

Xiao,

Wen,

Pedrycz

et al. 2024

Chinese Journal of Information Fusion

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Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.

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Fault diagnosis method via one vs rest evidence classifier considering imprecise feature samples

Cited by 2 publications

References 38 publications

Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

Complex Evidence Theory for Multisource Data Fusion

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