Bolts constitute a very important subset of mechanical fasteners. In order to tighten bolts, a degree of bolt preload scatter is to be expected. Since the torque control of tightening bolts is the most popular means of controlling the preload, an appropriate tightening torque becomes pivotal. This paper investigates the torque control problem of bolt tightening process. This process is not as simple as it looks because the inherently nonlinear process contains many uncertainties. To conquer the adverse effects of the uncertainties, this paper designs an adaptive-gain second-order sliding mode controller. Theoretically, such design can guarantee that the bolt tightening process has the closed-loop stability in the sense of Lyapunov. From the aspect of practice, the control method is carried out by a platform. Some comparisons illustrate the feasibility and effectiveness of the designed controller.

This paper presents a super-twisting sliding mode control method for the formation maneuvers of multiple robots. In the real world of applications, the robots suffer from many uncertainties and disturbances that trouble the super-twisting sliding mode formation maneuvers very much. Especially, this issue has the adverse effects on the formation performance when the uncertainties and disturbances have an unknown bound. This paper focuses on this issue and utilizes the technique of disturbance observer to meet this challenge. In terms of the leader–follower framework, this paper investigates the integration of the super-twisting sliding mode control method and the disturbance observer technique. This kind of formation design has the guaranteed closed-loop stability in the sense of Lyapunov. Some simulations are implemented through a multi-robot platform. The results demonstrate that the superiority of the formation design regardless of uncertainties and disturbances.

With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithms, a more efficient neural network is proposed by using Convolutional Neural Networks (CNN) to replace Long-Short Term Memory (LSTM). Firstly, multi-sensor degenerate information fusion coding is realized with the convolutional autoencoder (CAE). Then, the temporal convolutional network (TCN) is applied to achieve efficient prediction with the obtained degradation code. It does not depend on the iteration along time, but learning the causality through a mask. Moreover, the data processing is improved to further improve the application efficiency of the algorithm. ExtraTreesClassifier is applied to recognize when the failure first develops. This step can not only assist labelling, but also realize feature filtering combined with tree model interpretation. For multiple operation conditions, new features are clustered by K-means++ to encode historical condition information. Finally, an experiment is carried out to evaluate the effectiveness on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets provided by the National Aeronautics and Space Administration (NASA). The results show that the proposed algorithm can ensure high-precision prediction and effectively improve the efficiency.

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain’s decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions.