Paradigm Shift for Predictive Maintenance and Condition Monitoring from Industry 4.0 to Industry 5.0: A Systematic Review, Challenges and Case Study

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102935 - 102935

Опубликована: Сен. 1, 2024

Язык: Английский

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Insights into modern machine learning approaches for bearing fault classification: A systematic literature review

Results in Engineering, Год журнала: 2024, Номер 23, С. 102700 - 102700

Опубликована: Авг. 10, 2024

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result complete system failure, it accounts for approximately 45 %–50 % failures rotating machinery. Hence, is imperative establish thorough accurate predictive maintenance program that can efficiently foresee prevent mishaps or malfunctions. The literature employed variety techniques approaches, from conventional methods contemporary machine learning (ML) ML-integrated IoT-based solutions, categorise bearing faults. This article provides an overview most recent research models used classification summary highlights various significant challenges current models, issues with function, complexities neural network structure, unrealistic datasets, dynamic working conditions machines, noise dataset, limited data availability, imbalanced datasets. In order tackle problems, researchers have endeavored improve apply different methods, convolutional networks, deep belief LiNet, among others. Researchers primarily developed these approaches using datasets publicly accessible sources. study also identified gaps deficiencies, including imbalance, difficulties integration. nascent technologies field problem diagnosis acknowledged Internet Things-based ML vision-based techniques, which currently their initial phases advancement. Ultimately, puts forth several prospective suggestions recommendations.

Язык: Английский

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Fault Classification in Rotor-Bearing System using Advanced Signal Processing and Machine Learning Techniques

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103892 - 103892

Опубликована: Янв. 1, 2025

Язык: Английский

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Application of an active learning method for cumulative fatigue damage assessment of floating wind turbine mooring lines

Results in Engineering, Год журнала: 2024, Номер 22, С. 102122 - 102122

Опубликована: Апрель 12, 2024

Long-term cumulative fatigue damage of mooring lines is crucial for the design floating wind turbine structures (FWTs). Although many efforts are carried out offshore platforms, there still needs to be an efficient approach assessing long-term due complex loading in FWTs. An active learning named AK-DA (Adaptive Kriging Damage Assessment) was recently proposed assessment structures. However, original work, only tested on a 5MW tower with monopile support It unclear whether it applicable other parts system, especially considering Therefore, this used assess lines. The Gaussian process regression (Kriging) model predict line under different wind-wave cases. IEA 15MW semi-submersible turbines assessed approach. numerical simulation results show that can efficiently and accurately estimate Compared traditional approach, increase efficiency by more than 45 times, absolute error less 1%. This could serve as helpful tool system designers, facilitating during process.

Язык: Английский

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Electrical Fluting Damage of Rolling Element Bearings: Influences of AC Electrical Parameters and Operating Conditions

Tribology International, Год журнала: 2025, Номер unknown, С. 110658 - 110658

Опубликована: Март 1, 2025

Язык: Английский

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Deep neural network based distribution system state estimation using hyperparameter optimization

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102908 - 102908

Опубликована: Сен. 1, 2024

Язык: Английский

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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review

Vibration, Год журнала: 2024, Номер 7(4), С. 1013 - 1062

Опубликована: Окт. 31, 2024

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these often leads costly downtime and potential safety risks, further emphasizing the importance monitoring health state. Vibration signal analysis is now a common approach for this purpose, it provides useful information related dynamic behavior machines. This research aimed conduct comprehensive examination current methodologies employed stages vibration analysis, which encompass preprocessing, post-processing phases, ultimately leading application Artificial Intelligence-based diagnostics prognostics. An extensive search was conducted various databases, including ScienceDirect, IEEE, MDPI, Springer, Google Scholar, 2020 early 2024 following PRISMA guidelines. Articles that aligned with at least one targeted topics cited above provided unique methods explicit results qualified retention, while those were redundant or did not meet established inclusion criteria excluded. Subsequently, 270 articles selected an initial pool 338. The review highlighted several deficiencies preprocessing step experimental validation, implementation rates 15.41% 10.15%, respectively, prototype studies. Examination processing phase revealed time scale decomposition have become essential accurate signals, they facilitate extraction complex remains obscured original, undecomposed signals. Combining such time–frequency shown be ideal combination extraction. In context fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), random forests been identified five most frequently algorithms. Meanwhile, transformer-based models are emerging promising venue prediction RUL values, along data transformation. Given conclusions drawn, future researchers urged investigate interpretability integration diagnosis prognosis developed aim applying them real-time contexts. Furthermore, there need studies disclose details datasets operational conditions machinery, thereby improving reproducibility. Another area warrants investigation differentiation types present signals obtained bearings, defect overall system embedded within

Язык: Английский

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Reduced Order Analytical Modelling of Micro Wind Turbine Rotordynamics with Tower Shadow Effects

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Micro scale wind turbines (µWTs) in the order of 10 kW or less, suffer high vibration levels compared to larger ones. This can be attributed fact that they rotate at higher rotational speeds. For simplicity, blades are directly attached a permanent magnet generator (PMG) an outer rotor type with no need install gearbox. Also due their small size, tail vane represents cost-effective solution for aligning turbine direction. Those vanes generate significant yaw rates as consequence unpredictable variations High rates, along increased speeds, considerable gyroscopic loads. Therefore, µWTs involve unique dynamic loading condition. Considering existing software packages primarily designed large turbines, thorough investigation into dynamics is essential. In this study, we address peculiarity using simple mathematical model helps providing better dynamical insights than numerical calculations. Using model, parametric study conducted involving two parameters: generator’s bearing span and position rotor’s centre gravity (CG) axis. The revealed increasing yields decrease vibrations. Also, shifting towards rear reduces vibrations across all considered degrees freedom, except vertical pitching, which achieve minimum value. An optimal placement calculated used afterwards improve design µWT. A case highlights improvement terms root mean square presented thereby help designers build performance longer lifespan offering understanding effects parameters

Язык: Английский

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Life Extension of Wind Turbine Drivetrains by means of SCADA Data: Case Study of Generator Bearings in an Onshore Wind Farm

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102921 - 102921

Опубликована: Сен. 1, 2024

Язык: Английский

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Fatigue prediction of wind turbine main bearing based on field measurement and three-dimensional elastic drivetrain model

Engineering Failure Analysis, Год журнала: 2024, Номер unknown, С. 108985 - 108985

Опубликована: Окт. 1, 2024

Язык: Английский

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