1,256 publications from this institution
No abstract is provided for this article.
Floating wind turbine will suffer from more fatigue and ultimate loads compared with fixed-bottom installation due to its floating foundation, while structural control offers a possible solution for direct load reduction. This paper deals with the modelling and parameter tuning of a spar-type floating wind turbine with a tuned mass damper (TMD) installed in nacelle. First of all, a mathematical model for the platform surge-heave-pitch motion and TMD-nacelle interaction is established based on D’Alembert’s principle. Both intrinsic dynamics and external hydro and mooring effects are captured in the model, while tower flexibility is also featured. Then, different parameter tuning methods are adopted to determine the TMD parameters for effective load reduction. Finally, fully coupled nonlinear wind turbine simulations with different designs are conducted in different wind and wave conditions. The results demonstrate that the design of TMD with small spring and damping coefficients will achieve much load reduction in the above rated condition. However, it will deteriorate system performance when the turbine is working in the below rated or parked situations. In contrast, the design with large spring and damping constants will produce moderate load reduction in all working conditions.
This paper describes the modeling and simulation of High Pressure Roller Crusher (HPRC) for the production of silicon carbide grains. The study is to make a model for simulation of a High Pressure Roller Crusher. A High Pressure Roller Crusher (HPRC) is an important part in the production of silicon carbide, where the grains are crushed into powder form and then sieved into specified sizes based on its usage. This paper will present a model based on Johanson's theory for roller compactors, considering all the delays. The non-linearity or delays were handled using Matlab software. Conclusions are given at end of the paper.
No abstract is provided for this article.
Data-driven intelligent fault diagnosis methods have emerged as powerful tools for monitoring and maintaining the operating conditions of mechanical equipment. However, in real-world engineering scenarios, mechanical equipment typically operates under normal conditions, resulting in limited and imbalanced (L&I) data. This situation gives rise to label bias and biased training. Meanwhile, the current multi-source information fault diagnosis research to date has tended to focus on fault identification rather than effective feature fusion strategies. To solve these issues, a novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion is proposed to model data-level and algorithm-level ideas in a unified deep network for achieving effective multi-source information fusion under the L&I working conditions. From a data-level perspective, a data preprocessing operation is first employed to capture time–frequency information simultaneously. Subsequently, multi-source time–frequency information is fed into feature extractors with information discriminators to construct local and information-invariant feature maps with different scales to eliminate multi-source information domain shift. Then, the multi-source feature vectors are modeled by a multi-source information transformer-based neural network to achieve effective multi-source information fusion through cross-attention mechanism. Next, the global max pooling and global average pooling layers are leveraged to obtain the more representative features. Finally, from an algorithm-level perspective, a dual-stream diagnosis predictor with a binary diagnosis predictor and a multi-class diagnosis predictor is designed to synthesize the diagnostic results through a reweighing activation mechanism for addressing the L&I problems. Extensive experiments on four different multi-source information datasets show the superiority and promising performance of our method compared to the state-of-the-art methods, as evidenced by indicators from various aspects.
Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitable for continuous measurement. Aiming at the deficiencies in existing studies, a novel cuffless BP estimation framework of Receptive Field Parallel Attention Shrinkage Network (RFPASN) and BP range constraint is proposed. Firstly, RFPASN uses the multi-scale large receptive field convolution module to capture the long-term dynamics in the photoplethysmography (PPG) signal without using long short-term memory (LSTM). On this basis, the features acquired by the parallel mixed domain attention module are used as thresholds, and the soft threshold function is used to screen the input features to enhance the discriminability and robustness of features, which can significantly improve the prediction accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Finally, in order to prevent large fluctuations in the prediction results of RFPASN, RFPASN based on BP range constraint is proposed to make the prediction results of RFPASN more accurate and reasonable. The performance of the proposed method is demonstrated on a publically available MIMIC-II database. The database contains normal, hypertensive and hypotensive people. We have achieved MAE of 1.63/1.59 (DBP) and 2.26/2.15 (SBP) mmHg for BP on total population of 1562 subjects. A comparative study shows that the proposed algorithm is more promising than the state-of-the-art.
In this paper, the sliding mode control problem is addressed for the automotive electronic valve system, which is described by the Markovian model according to the voltage failure. It is supposed that both the system states and the system modes are unavailable to the controller. In order to avoid data collision on the sensor-to-controller transmission, the scheduling among the sensor nodes is ruled by the weighted try-once-discard protocol. A mode detector via a hidden Markovian model is introduced, and an asynchronous token-dependent state observer is proposed. Dependent on the hidden mode information and current token directive, a sliding mode controller is constructed to assure the reachability of a sliding region. Besides, the hidden Markovian model approach is developed to derive mean-square stability conditions for the augmented system. Eventually, simulation studies are provided to demonstrate the validity of the proposed control scheme for the system under consideration.
No abstract is provided for this article.
Published post-print of an article in the journal: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering Available from the publisher at: http://dx.doi.org/10.1243/09596518jsce730
In this paper, the problems of stability analysis and H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> controller design of a class of switched nonlinear systems are investigated. In a classical way, the modeling of the systems is approached by switched fuzzy systems, and both fast switching and slow switching are considered there. In particular, for slow switching scheme, a new mode-dependent average dwell time switching is proposed for the underlying switched fuzzy systems. Based on a fuzzy-basis-dependent and mode-dependent Lyapunov function, the H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> state-feedback controller is derived. A numerical example is given to show the validity and potential of the theoretical results.
Traditional metaheuristic approaches for structural optimization often encounter challenges such as premature convergence, computational inefficiency, and limited adaptability to complex constraints in high-dimensional search spaces. To address these limitations, this study proposes the mutation-based virus pandemic optimization (MVPO) algorithm, a novel bio-inspired metaheuristic that emulates virus transmission dynamics and mutation mechanisms. MVPO integrates population-based exploration, simulating viral spread through intra-community interactions (guided by wavelet functions to model transmission variability) and inter-community propagation, with a two-phase mutation strategy: the first phase enhances global exploration via elongated search steps during accelerated spread, while the second phase introduces structural perturbations to escape local optima. The algorithm’s efficacy is evaluated through three benchmark problems, two planar (2D) frames and a three-dimensional (3D) space frame, with comparisons against established methods (e.g., particle swarm optimization, cuckoo search) and contemporary algorithms (e.g., marine predators algorithm, pelican optimization algorithm). Results demonstrate MVPO’s superior performance, achieving weight reductions of 0.18 %–24.2 % across all structures, including the complex 3D frame, while requiring 11.2 %–61.62 % fewer structural analyses than competitors. Reliability is evidenced by minimal standard deviations (7.88–69.58) across multiple runs, indicating consistent convergence to near-optimal solutions. Stress-ratio analyses confirm adherence to design constraints, ensuring feasible lightweight configurations. These findings establish MVPO as a computationally efficient and robust tool for structural optimization, capable of navigating intricate 2D and 3D design spaces with enhanced adaptability. The algorithm’s bio-inspired mechanics bridge biological principles with engineering precision, offering practical advancements for complex optimization challenges.
This study is concerned with the stability, l 2 ‐gain analysis and ℋ ∞ control for a class of discrete‐time switched linear parameter‐varying systems with both mode‐dependent average dwell time (MDADT) and asynchronous switching, where ‘asynchronous’ means the switching of controllers has a lag to the switching of system modes. The l 2 ‐gain for general switched systems with MDADT in non‐linear setting is firstly derived. Based on the obtained results, the problem of asynchronous ℋ ∞ control for the studied systems is formulated under the framework of MDADT switching logic, and the conditions for the existence of admissible asynchronous ℋ ∞ controllers are deduced in the form of parameterised linear matrix inequalities. A numerical example is provided to verify the effectiveness of the acquired results.
