Quantification and propagation of Aleatoric uncertainties in topological structures

Wang, Zihan; Daeipour, Mohamad; Xu, Hongyi

doi:10.1016/j.ress.2023.109122

Cited by 4 publications

(2 citation statements)

References 75 publications

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“…There have been several papers in this field, but still there is a need for additional investigation [42]. This uncertainty may arise from sensor noise (as mentioned in 4.2), data processing, filtering, estimation methods, model uncertainty, working condition fluctuations, prediction uncertainty, and others [42,64]. Uncertainty is inherent, but techniques such as Bayesian neural networks have arisen to reduce it [64][65][66][67][68].…”

Section: Prognostics Models Challengesmentioning

confidence: 99%

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

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Section: Prognostics Models Challengesmentioning

confidence: 99%

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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“…The gear's local features comprise the local hole, local groove, convex platform, external mounting column, inner gear baffle, and outer gear baffle. These features are identified based on the topological relationship of the surface [30,31], with feature parameters including size, position, and quantity. Taking the convex platform as an example, the characteristic parameters include the width of the convex platform, the length of the convex platform, the height of the convex platform, the number of the convex platform, the distance between the center axis of the convex platform, and the central shaft of the gear, and the convex platform is on one side of the main gear surface or the side of the secondary gear surface.…”

Section: Feature Recognitionmentioning

confidence: 99%

Research on and Application of Feature Recognition and Intelligent Retrieval Method for Multi-Component Alloy Powder Injection Molding Gear Based on Partition Templates

Kong,

Cui,

Zhang

et al. 2024

Metals

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The forming process of multi-alloy gears by metal powder injection molding is tedious, and the current design process mainly depends on the experience of designers, which seriously affects the product development cycle and forming quality. In order to solve the problem of the gear feature expression being missing, which hinders the automatic retrieval of similar parts in the analogical design process, a feature recognition and intelligent retrieval method for a multi-alloy powder injection molding gear based on partition templates is proposed in this paper. The partition templates of the gear are defined, and gear digitization is completed by using the automatic recognition algorithm. Searching for similar gear parts in the knowledge base, designers can analogically design the forming process for new parts according to the mature process of the parts in the knowledge base. The automatic identification and intelligent retrieval system developed according to this method has been implemented in two MIM (metal injection molding) product manufacturing enterprises. Case studies and industrial applications have proved the effectiveness of the system, the efficiency of identification and retrieval has been improved by more than 97%, and the number of mold tests has been reduced by 60%.

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