Passive tuned mass dampers are effective near their design frequencies but can lose performance or exceed travel limits when structural properties are imprecise. This paper develops an interval type-2 fuzzy α-plane minimax method for the simultaneous allocation, tuning, and damping design of a three-absorber bank on a six-degree-of-freedom coupled machine–foundation system. Primary mass, stiffness, and damping are represented by lower and upper triangular membership functions, so uncertainty in both parameter values and membership widths is retained. At every α-plane, outer and inner vertex responses are propagated through the complex frequency-response matrix. A membership-weighted objective combines worst peak acceleration, broadband root-mean-square acceleration, footprint width, and a 16 mm relative-stroke constraint. Differential evolution determines nine absorber variables for a fixed 6% auxiliary-mass budget. The frequency-domain solver reproduces classical equal-peak tuning ratios to machine precision and absorber damping ratios within 0.20%. In 2,048 independent outer-support Sobol scenarios, the proposed design decreases the mean peak acceleration from 6.058 g to 1.262 g and the worst peak acceleration from 9.005 g to 1.682 g. The deterministic optimum attains a smaller mean peak of 1.112 g but violates the stroke constraint in 55.42% of the scenarios; the type-1 robust design violates it in 2.05%, whereas the proposed design has no violations and a maximum stroke of 15.992 mm. The results show that explicitly preserving membership-function uncertainty changes the mass allocation and first-mode detuning sufficiently to obtain a feasible broadband design without active control.
Sulieman Ibrahim Mohammad, Yogeesh Nijalingappa, Seif Al Bustanji et al.· Sound & Vibration· 0 citations
Level graphs and strong level graphs are introduced as tools to define and compute the chromatic number of BIFGs, demonstrating that the proposed level-graph method yields exact chromatic numbers for classes of BIFGs.
Fikadu Tesgera Tolasa, V. Repalle, G. A. Ganati et al.· F1000Research· 0 citations
A hybrid data-driven framework integrating a Temporal Convolutional Network, Bidirectional Long Short-Term Memory, and Extreme Gradient Boosting for accurate LIB RUL prediction provides a robust and computationally efficient solution for intelligent battery health monitoring, predictive maintenance, and smart battery management applications in electric vehicles and energy storage systems.
T. Mariprasath, Kumaresh S. S., Seif Al Bustanji et al.· Energy Exploration & Exp...· 0 citations
The reliable estimation of remaining useful life (RUL) of rolling bearings plays a critical role in maintaining the reliability of modern industrial equipment and minimizing machine downtime. However, the conventional vibration-based prognostic methods tend to experience challenges in predicting the remaining useful life of rolling bearings in variable-speed operating environments due to issues with nonstationary signals and the lack of incorporation of physical degradation processes. This paper proposes a physics-informed approach for estimating the remaining useful life of rolling bearings using vibration envelope characteristics and accelerated life testing. The approach starts with the use of order tracking combined with envelope analysis to extract vibration envelope characteristics under variable speed conditions. A health index is constructed to represent the degradation process. The nonlinear degradation process is modelled using a physics-informed exponential degradation model. An ensemble prediction model is proposed for predicting RUL. The results demonstrate that the developed model was significantly more accurate in its predictions, with a maximum of 49% improvement in the RMSE compared to traditional models and consistent results under varied operational conditions. The use of physics-based modelling and envelope analysis increased the clarity and robustness of the model, and the acceleration of the life testing process contributed to better generalizability of the model. Moreover, the introduction of adaptive threshold values improved maintenance time prediction by over 50%, and uncertainty assessment confirmed the validity of the model.
Sulieman Ibrahim Mohammad, A. Vasudevan, Seif Al Bustanji et al.· Sound & Vibration· 0 citations
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