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Abstract Quasi-periodic motions can be numerically found in piecewise-linear systems, however, their characteristics have not been well understood. To illustrate this, an incremental harmonic balance (IHB) method with two timescales is extended in this work to analyze quasi-periodic motions of a non-smooth dynamic system, i.e., a gear transmission system with piecewise linearity stiffness. The gear transmission system is simplified to a four degree-of-freedom nonlinear dynamic model by using a lumped mass method. Nonlinear governing equations of the gear transmission system are formulated by utilizing the Newton’s second law. The IHB method with two timescales applicable to piecewise-linear systems is employed to examine quasi-periodic motions of the gear transmission system whose Fourier spectra display uniformly spaced sideband frequencies around carrier frequencies. The Floquet theory is extended to analyze quasi-periodic solutions of piecewise-linear systems based on introduction of a small perturbation on a steady-state quasi-periodic solution of the gear transmission system with piecewise linearities. Comparison with numerical results calculated using the fourth-order Runge-Kutta method confirms that excellent accuracy of the IHB method with two timescales can be achieved with an appropriate number of harmonic terms. 

Abstract A modified incremental harmonic balance (IHB) method is used to determine periodic solutions of wave propagation in discrete, strongly nonlinear, periodic structures, and solutions are found to be in a two-dimensional hyperplane. A novel method based on the Hill’s method is developed to analyze stability and bifurcations of periodic solutions. A simplified model of wave propagation in a strongly nonlinear monatomic chain is examined in detail. The study reveals the amplitude-dependent property of nonlinear wave propagation in the structure and relationships among the frequency, the amplitude, the propagation constant, and the nonlinear stiffness. Numerous bifurcations are identified for the strongly nonlinear chain. Attenuation zones for wave propagation that are determined using an analysis of results from the modified IHB method and directly using the modified IHB method are in excellent agreement. Two frequency formulae for weakly and strongly nonlinear monatomic chains are obtained by a fitting method for results from the modified IHB method, and the one for a weakly nonlinear monatomic chain is consistent with the result from a perturbation method in the literature. 

Abstract A tidal current energy converter (TCEC) is a device specifically designed to harness the kinetic energy present in tidal energy and convert it into stable mechanical rotational energy, which can then be used to generate electricity. The core component of the TCEC is an infinitely variable transmission (IVT), which adjusts the speed ratio to maintain a stable output speed regardless of the input speed changes caused by tidal changes. In order to ensure the efficient driving performance of the IVT system, a closed-loop control strategy based on IVT state measurement data is studied in this paper. This method can effectively track the expected output speed of the IVT system in general TCEC. Based on the proposed speed control strategy, the speed regulation of the whole IVT system under different conditions is studied in theory and simulation. These promising results could directly contribute to future research to improve the efficiency of tidal energy harvesting. 

This paper presents a practical framework that integrates wind speed forecasting with proton exchange membrane (PEM) electrolyzer design to optimize hydrogen production. Due to wind speed fluctuations, excess electrical energy is sometimes produced and left unused. A wind-to-hydrogen system addresses this challenge by converting surplus energy into storable hydrogen using a PEM electrolyzer. The proposed approach employs a multivariate supervisory control and data acquisition (SCADA) dataset and applies a convolutional neural network with bi-directional long short-term memory (CNN-Bi-LSTM) for multivariate wind speed temporal forecasting, enabling more efficient PEM operations. Compared to standard deep learning models, the CNN-Bi-LSTM architecture reduces the root mean square error by 52.5% and the mean absolute error by 56%, thereby enhancing hydrogen production forecasting. Simulation results show that a membrane thickness of 0.0252 mm and an operating temperature of 70% achieve the highest overall PEM efficiency of 63.611%. This study demonstrates the integration of deep learning-based forecasting with electrochemical modeling and SCADA datasets as a novel approach for wind-to-hydrogen production systems. 

This study proposes an intelligent techno-economic assessment framework for wind energy end users, using a novel dual-input convolutional bidirectional long short-term memory (Dual-ConvBiLSTM) architecture to predict dynamic levelized cost of energy (LCOE). The proposed architecture separates weight matrices for wind supervisory control and data acquisition (SCADA) data and financial data. This allows the model to integrate both data streams at every time step through a custom dual-input cell. This approach is compared with five baseline architectures: Recurrent Neural Network (RNN), LSTM, BiLSTM, ConvLSTM, and ConvBiLSTM, which process data through separate parallel branches and concatenate outputs before final prediction. The Dual-ConvBiLSTM achieves an LCOE estimate of $4.0391 cents/kWh, closest to the actual value of $4.0450 cents/kWh, with a root mean squared error reduction of 51.8% compared to RNN, 47.0% to LSTM, 40.0% to BiLSTM, 36.7% to ConvLSTM, and 34.4% to ConvBiLSTM, demonstrating superior capability in capturing complex interactions between SCADA data and financial parameters. This intelligent framework potentially enhances economic assessment and enables end users to accelerate renewable energy deployment through more reliable financial prediction. 

Predictive maintenance for underground high-pressure fluid-filled (HPFF) power cables remains a critical challenge due to the weak and intermittent nature of fault-induced signals and the limited accessibility of buried infrastructure. This paper proposes a physics-informed Seq2Seq-attentionautoencoder acoustic monitoring (Echo-AE) model for predictive maintenance in underground HPFF cable systems. The Echo-AE model is developed based on a physics-informed loss function that incorporates both physics-based constraints and prediction errors. A controlled experimental setup of underground HPFF cable systems was used to capture continuous acoustic monitoring data, where three fault severity levels were generated, resulting in 4 million acoustic samples spanning normal operations and 15 fault events, and producing an imbalanced dataset with a 117:1 normal-tofault ratio to simulate real-world scenarios in which early-stage faults are rare. Results demonstrated Echo-AE’s superior early-stage fault detection capability compared with traditional models, with an F1-score of 0.8313, precision of 0.7864, recall of 0.8816, and an accuracy of 0.9936. The model exhibits fast convergence (20 epochs) and an area under the receiver operating characteristic curve of 0.998. Threshold sensitivity analysis revealed an optimal operation point that balances false positives and false negatives. 

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions.