In wireless sensor networks, node placement plays a significant role in terms of meeting design goals, such as cost effectiveness, connectivity, lifetime, and data latency. In tunnel environments, the energy consumption of sensor nodes and relay nodes is imbalanced because of the long and narrow shape of tunnels. In this article, to reduce the use of extra batteries and the number of forwarding times for a message, we propose a new approach to assist in the placement of relay nodes for two-tier wireless sensor networks in a tunnel environment. The approach proposed is based on the principle that working relay nodes can change approximately at the same time during network operation. It seems the relay nodes move to a new place when they are instead by dormant relay nodes. The approach provides a novel method that has been proved to find optimal places to deploy relay nodes in tunnel environment. Moreover, the approach balances the energy consumption of entire sensor nodes and prolongs the lifetime of entire networks significantly, which is shown in simulations.

The anomaly identification of vibration monitoring signals for rotating machinery supports fault diagnosis. The abnormal representation of the monitoring signals for rotating machinery faults is a long signal with periodic characteristics and multipoint correlations. Deep learning methods can effectively distinguish various types of faults by learning the interpoint representations or the correlation patterns among sampling points in the monitoring data. However, under the influence of noise, the fault features are disrupted, resulting in a deterioration of the fault identification effect. We noticed that the amplitude of the signal is superimposed with noise, which affects the probability distribution of the amplitude of the original signal. To solve this problem, we proposed a novel deep learning model named variational spectrum-attention transformer (VSAT) with a spectrum attention mechanism. It uses frequency-domain projection to transform the signal amplitude distribution into point distributions at multiple frequencies. Then, an exponential function is employed to increase the distances between feature points. An attention branch for learning the statistical distribution patterns of amplitudes at different frequencies is constructed. It uses variational method to learn the distribution parameters of the frequency-domain amplitudes of monitored sequential data. An experimental analysis of weight grad-CAM maps demonstrates that, compared to extracting features from frequency-domain data by transformer model, the new method captures different distribution features. The verification and comparison results on 3 bearings and 1 gearbox dataset show that the new model not only accelerates the model convergence, but also improves the accuracy of anomaly classification, and has excellent performance in multiclassification and antinoise performance.