Effective fault-diagnosis strategies have been the focus of research on multi-agent systems (MASs). In this paper, the belief rule base (BRB)-based distributed fault-diagnosis problem for MASs is investigated, and a topology-switching strategy is developed to increase the reliability of fault-diagnosis model. Firstly, a BRB-based distributed fault-diagnosis model is constructed for the MAS with multiple faults, then expert knowledge is used to judge whether the agent is faulty. Then, considering that the system may be influenced by the fault or some other factors and thus leading to a decrease in the accuracy of the fault-diagnosis results, a topology-switching strategy based on the average distance of the output diagnosis accuracy is proposed to update the topology of the agent so that the fault-diagnosis results can be more reliable. Note that the topology-switching threshold is designed based on the average distance between the accuracy of the fault diagnosis of each agent. The method proposed in this paper can solve the problem when the fault-diagnosis accuracy of the model is affected by some common factors and thus decreases, and can improve the reliability of the fault-diagnosis model very well. Finally, the effectiveness of the BRB-based distributed fault-diagnosis model and the proposed topology-switching strategy to improve the fault-diagnosis accuracy is verified by simulation examples.

Assessing the health of complex systems is crucial for reducing failure risks and ensuring long-term stability and reliability. The Belief Rule Base (BRB) method effectively evaluates systems by combining observational data with expert knowledge. However, it faces challenges such as rule explosion, unreliable attributes, and uncertainty in expert knowledge. This paper proposes the Approximate Belief Rule Base with Attribute Reliability (ABRB-r) to address these issues. The ABRB-r model simplifies rule combinations by using a linear structure and considers attribute correlations to minimize interdependence effects. It incorporates attribute reliability into the inference process to enhance rule matching and dynamically adjusts activation weights to reflect expert knowledge uncertainty. A case study on diesel engines demonstrates the method’s effectiveness, significantly improving the accuracy and reliability of health state assessments.

Optimal maintenance decision for a sensor network aims to intelligently determine the optimal repair time. The accuracy of the optimal maintenance decision method directly affects the reliability and safety of the sensor network. This paper develops a new optimal maintenance decision method based on belief rule base considering attribute correlation (BRB-c), which is designed to address three challenges: the lack of observation data, complex system mechanisms, and characteristic correlation. This method consists of two sections: the health state assessment model and the health state prediction model. Firstly, the former is accomplished through a BRB-c-based health assessment model that considers characteristic correlation. Subsequently, based on the current health state, a Wiener process is used to predict the health state of the sensor network. After predicting the health state, experts are then required to establish the minimum threshold, which in turn determines the optimal maintenance time. To demonstrate the proposed method is effective, a case study for the wireless sensor network (WSN) of oil storage tank was conducted. The experimental data were collected from an actual storage tank sensor network in Hainan Province, China. The experimental results validate the accuracy of the developed optimal maintenance decision model, confirming its capability to efficiently predict the optimal maintenance time.

Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The dilated convolution-based (DCB) module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each branch. Subsequently, the attention operation-based (AOB) module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of noise. Finally, predictions are made using the heatmap representation. The model can recognize images with diverse scales and more complex semantic information. Experimental results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets, validating the effectiveness of our proposed approach.