The federated fault diagnosis approach has achieved remarkable results in recent years, which enables multiple clients with similar mechanical devices to collaboratively construct global intelligent diagnostic models while protecting data privacy. However, in practice, the statistical heterogeneity of data collected from different clients, as well as the model heterogeneity due to local model personalization, pose great challenges to federated learning (FL). Meanwhile, using a central server as an information management center to build global models increases additional model parameters and the risk of data privacy leakage. To address these issues, this article proposes a heterogeneous FL framework based on peer-to-peer communication (P2PCHF) for rotating machinery fault diagnosis. To achieve heterogeneous client communication without relying on a central server, the sharing unlabeled dataset is utilized in the collaborative updating phase to achieve peer-to-peer communication between clients and to align instance dimensions and clustering dimensions between heterogeneous clients by constructing intercorrelation matrices to achieve feature-level and semantic-level knowledge exchange for better interdomain generalization capabilities. Joint knowledge distillation based on class labels and class relations is introduced in the local update phase to mitigate forgetting effect in the local update phase of private models and effectively balance multidomain category knowledge. It is verified in three cases that the proposed P2PCHF can effectively address model heterogeneity and data statistics heterogeneity among clients, and enable locally-privatized models to gain interdomain generalization capability. The code framework is available athttps://github.com/JC952/P2PCHF.

Federated learning (FL) enables the collaborative training of intelligent diagnostic models across multiple clients while preserving data privacy. However, the complex and dynamic working conditions often cause federated diagnostic models to suffer from performance degradation when applied to unseen clients, exposing deficiencies in model generalization. Domain generalization fault diagnosis methods aim to enhance model adaptability under unknown working conditions, but most existing algorithms rely on shared data, making them unsuitable for direct application in FL environments. To address this challenge, we propose a federated domain generalization fault diagnosis method based on Cross-Client Style Integration and Dual Alignment representation (CCSIDA). This method significantly enhances model generalization in FL settings while ensuring data privacy. Specifically, CCSIDA integrates three key strategies: 1) cross-client style data enhancement; 2) domain-sensitive feature suppression (DSFS); and 3) predictive alignment. For cross-client style integration, we design a stochastic mixed-style sample generation module to overcome the limitations of conventional domain generalization data enhancement methods in FL. This module generates diverse samples by constructing a shared style repository, enabling interclient style information transfer. For cross-client dual alignment representation, we introduce a DSFS strategy to reduce domain discrepancies between original and enhanced samples. Additionally, Jensen-Shannon divergence is employed to ensure prediction consistency between these samples. Finally, extensive experiments on five benchmark datasets validate the effectiveness of the proposed method.

Exploitation and Exploration in Reinforcement Learning (RL) are often in conflict, and a persistent problem in RL is finding effective methods for state space exploration. For the deterministic strategy algorithm, the exploration strategy directly affects the search process of the agent in the state space, which in turn affects the training efficiency. Model-based RL algorithms consider the environment model to be completely black-box, but when RL is applied to real-world problems, a portion of many environment models is often known. In this paper, we utilize a portion of known environment models in our exploration strategy to achieve more efficient exploration and thus accelerate the training of the agent. This is also closer to the actual situation, and often part of the environmental model we can acquire knowledge by way of a priori knowledge or learning the model. Our exploration strategy is applicable in a continuous action space environment. We propose three exploration methods that utilize partial or full environment models to achieve more efficient exploration. We use two GYM continuous action space environments and a 7-joints robotic arm environment to validate our exploration methods.

Underwater wireless sensor networks (UWSNs) have been developed for a set of underwater applications, including the resource exploration, pollution monitoring, tactical surveillance, and so on. However, the complexity and diversity of the underwater environment differentiate it significantly from the terrestrial environment. In particular, the coverage requirements (i.e., coverage degrees and coverage probabilities) at different regions probably differ underwater. Nevertheless, little effort has been made so far on the topology control of UWSNs given the diverse coverage requirements. To this end, this article proposes two algorithms for the diverse coverage problem in UWSNs: (1) Traversal Algorithm for Diverse Coverage (TADC), which adjusts the sensing radii of nodes successively, that is, at each round only one node alters its sensing radius, and (2) Radius Increment Algorithm for Diverse Coverage (RIADC), which sets the sensing radii of nodes incrementally, that is, at each round multiple nodes may increase their sensing radii simultaneously. The performances of TADC and RIADC are analyzed through mathematical analysis and simulations. The results reveal that both TADC and RIADC can achieve the diverse coverage while minimizing the energy consumption. Moreover, TADC and RIADC perform nicely in obtaining optimal sensing radii and reducing message complexity, respectively. Such merits further indicate that TADC and RIADC are suitable for small-scale and large-scale UWSNs, respectively.

In order to achieve the high motion precision of the macro-micro parallel manipulator system for a large radio telescope, its real-time motion planning is dealt with in this paper. To uniquely determine the spatial position and orientation of the cable driven parallel manipulator, the real-time optimization is conducted according to the principle of uniform tension in the six driving cables. Synchronized points and the “judge and wait” mechanism ensure the continuity and synchrony of the trajectory tracking of the two parallel manipulators. The preadjustment of the micro parallel manipulator reduces the drastic dynamical coupling as a result of the fast movement of the micro manipulator. Besides, over-workspace avoidance procedure is introduced to restrict the micro parallel manipulator not to exceed it workspace. Experimental results validate the high precision of the macro-micro parallel manipulator system with supervisory controller and the motion prediction algorithm.