Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.
Transportation of heavy loads is often performed by multi-axle multi-steered heavy duty vehicles In this article a novel nonlinear optimal control method is applied to the kinematic model of the five-axle and three-steering coupled vehicle system. First, it is proven that the dynamic model of this articulated multi-vehicle system is differentially flat. Next. the state-space model of the five-axle and three-steering vehicle system undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization is based on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the five-axle and three-steering vehicle system a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of setpoints under moderate variations of the control inputs and minimal dispersion of energy by the propulsion and steering system of the five-axle and three-steering vehicle system.
Water pipeline leaks pose significant risks to urban infrastructure, leading to water wastage and potential structural damage. Existing leak detection methods often face challenges, such as heavily relying on the manual selection of frequency bands or complex feature extraction, which can be both labour-intensive and less effective. To address these limitations, this paper introduces a Frequency-Informed Transformer model, which integrates the Fast Fourier Transform and self-attention mechanisms to enhance water pipe leak detection accuracy. Experimental results show that FiT achieves 99.9% accuracy in leak detection and 98.7% in leak type classification, surpassing other models in both accuracy and processing speed, with an efficient response time of 0.25 seconds. By significantly simplifying key features and frequency band selection and improving accuracy and response time, the proposed method offers a potential solution for real-time water leak detection, enabling timely interventions and more effective pipeline safety management.
The field of human motion data capture and fusion has a broad range of potential applications and market opportunities. The capture of human motion data for wearable sensors is less costly and more convenient than other methods, but it also suffers from poor data capture accuracy and high latency. Consequently, in order to overcome the limitations of existing wearable sensors in data capture and fusion, the study initially constructed a model of the human joint and bone by combining the quaternion method and root bone human forward kinematics through mathematical modeling. Subsequently, the sensor data calibration was optimized, and the Madgwick algorithm was introduced to address the resulting issues. Finally, a novel human joint motion data capture and fusion model was proposed. The experimental results indicated that the maximum mean error and root mean square error of yaw angle of this new model were 1.21° and 1.17°, respectively. The mean error and root mean square error of pitch angle were maximum 1.24° and 1.19°, respectively. The maximum knee joint and elbow joint data capture errors were 3.8 and 6.1, respectively. The suggested approach, which offers a new path for technological advancement in this area, greatly enhances the precision and dependability of human motion capture, which has a broad variety of application possibilities.
Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency. However, classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity. The advent of Industry 4.0 has increased the use of sensors for monitoring systems, while deep learning (DL) models have allowed for accurate system health predictions, enabling data-driven maintenance planning. Most intelligent maintenance literature has used DL models solely for remaining useful life (RUL) point predictions, and a substantial gap exists in further using predictions to inform maintenance plan optimization. The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models. Hence, this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem (SMP). The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system. Numerical experiments compare the framework’s performance against prior SMP methods and highlight its strengths. When minimizing cost, maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs. The proposed method is usable in large-scale, complex scenarios and various industrial contexts. The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.