Recently, physics-informed learning-based intelligent bearing fault diagnosis methods have demonstrated great potential in improving diagnostic accuracy and physical consistency. Nevertheless, the practical deployment of conventional physics-informed neural networks typically relies on explicit partial differential equations (PDEs), which are generally difficult to acquire in real industrial scenarios. To address this limitation, a PDE-free physics-informed bearing fault diagnosis framework is proposed based on deterministic learning and statistical constraints. In the proposed framework, deterministic learning is adopted to extract nonlinear dynamic trajectories from multi-directional bearing vibration signals. The residuals between sample dynamic trajectories and class-specific standard trajectories are further converted into probabilistic statistical soft constraints. Accordingly, the system evolution mechanism embedded with physical dynamic characteristics is incorporated into the model training process, eliminating the need for explicit PDE constraints. In addition, a composite loss function is constructed by integrating the supervised classification loss with dynamic trajectory-based statistical constraint terms. This enables the network optimization process to be jointly guided by classification error and physical dynamic consistency. Finally, the effectiveness of the proposed method is validated through simulation and experimental studies. The results reveal that the average discernibility of dynamic trajectories reaches 0.3614, representing respective improvements of 122% and 68.6% over time-domain and frequency-domain signals. The proposed method achieves an effective balance among diagnostic accuracy, inference speed, and model complexity, with its average parameter volume reduced by approximately 93.9% relative to the comparative methods. These results demonstrate that the proposed method is applicable to bearing fault diagnosis scenarios where explicit PDE information is unavailable.
Peng Zhang, Qian Wang, Fukai Zhang et al.· IEEE Transactions on Industr...· 0 citations
Lower limb exoskeletons and mobile robots hold great potential in improving motor function rehabilitation for patients with limb dysfunction. However, their widespread application is limited by the substantial time and effort investment required from professional rehabilitation therapists. A significant challenge in achieving autonomous and intelligent rehabilitation lies in addressing the coordinated control between the exoskeleton and the robotic walker. This paper proposes a novel collaborative learning control strategy based on deterministic learning, which aims to achieve high-performance coordinated control through precise closed-loop dynamics modeling of the exoskeleton-walker system. First, radial basis function neural networks (RBFNNs) are employed to approximate the system dynamics during the coordinated control process. Utilizing deterministic learning theory, it is rigorously demonstrated that, under persistent excitation conditions, the unknown dynamics of the system can be accurately approximated and stored as constant neural networks. Subsequently, an experience-based collaborative learning controller is designed, enabling autonomous coordinated control of the human-robot system and offering a viable approach for its broader application. The effectiveness and superior performance of the proposed control strategy are validated through experiments conducted on the CoppeliaSim robotic platform. Note to Practitioners—This work is intended for researchers and engineers working on rehabilitation robotics, particularly those focusing on lower limb exoskeletons and mobile robotic walkers. One of the main barriers to the practical deployment of these systems is the reliance on continuous assistance from rehabilitation professionals during use. To address this, we propose a deterministic learning-based collaborative control strategy that enables accurate modeling and reuse of system dynamics, ultimately achieving autonomous coordination between the exoskeleton and robotic walker. This reduces reliance on manual intervention and opens up the possibility for long-term, high-performance rehabilitation training in clinical and home environments. Practitioners can leverage this approach to develop smarter, more adaptive rehabilitation systems that respond to patient needs with greater precision and autonomy.
Weitian He, Xinhao Zhang, Qinchen Yang et al.· IEEE Transactions on Automat...· 0 citations