This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.

In real engineering scenarios, the complex and variable operating conditions of mechanical equipment lead to distributional differences between the collected fault data and the training data. This distribution difference can lead to the failure of deep learning-based diagnostic models. Extracting generalized diagnostic knowledge from the source domain in scenarios where the target domain is not visible is the key to solving this problem. To this end, in this paper, we propose a domain generalization network for diagnosing bearing faults under unknown operating conditions, i.e., Feature Decoupled Integrated Domain Generalization Network (FDIDG). First, we propose a “feature decoupling” algorithm to uncover generalized representations of fault features from multiple source domains. The algorithm aims to explore the generalized representations of fault features by shrinking the distribution of data from multiple source domains and further generalize the fault features to unknown domains to reduce the coupling between fault features and operating conditions. Second, the diagnostic accuracy of the model under unknown operating conditions is further improved by adopting a multi-expert integration strategy in the decision-making stage and utilizing domain-private features to reduce the negative impact of edge samples on diagnosis. We conducted several sets of cross-domain experiments on both public and private datasets, and the results show that FDIDG has excellent generalization capabilities.

Efficient target localization is critical to many marine applications in the Internet of Underwater Things (IoUT). Doppler effect becomes more predominant in the underwater environment when the relative speed of the moving object, i.e., the observer, to the signal propagation speed in water is much larger than that in the air. This along with the observer-target geometry will bring in a notable impact on the localization performance. In this article, we derive the Cramér–Rao lower bound (CRLB) and formulate the A-optimality criterion-based observer trajectory optimization problem to improve the localization performance based on time delay and Doppler shift measurements. We show that in the worst case scenario, there will be a conflict between the nonaccessible zone constraint and the observer dynamics constraint, which will lead to an erroneous result. To address this problem, we propose a warning zone-based augmented Lagrange multiplier method (ALMM) where the nonaccessible zone constraint is relaxed to resolve the conflict and ensure the nonaccessible requirement of the targeted zone is maintained. Performance evaluations are conducted through extensive simulations for different scenarios, and the results are compared to other methods with or without trajectory optimization. We demonstrate that trajectory optimization using time delays and Doppler shifts can greatly improve the target localization accuracy in underwater networks.

This letter proposes an adaptive modulation and coding (AMC) scheme based on deep learning for underwater acoustic (UWA) communications. To achieve good communication performance in fast time-varying UWA channels, the proposed AMC scheme is implemented on the orthogonal time-frequency space (OTFS) modulation system. We design an end-to-end deep convolutional neural network (CNN) to capture the channel features and determine the optimal modulation and coding scheme. Additionally, we utilize a meta-learning algorithm to address environment mismatch in real-world UWA applications. This algorithm effectively adapts the CNN model from a given UWA environment to a new UWA environment with only a small amount of data. The performance of the proposed scheme is verified through real-world measured channels. Simulation results demonstrate that the proposed method outperforms existing machine learning-based AMC and fixed modulation and coding schemes in various UWA scenarios, offering better communication throughput and stronger learning capabilities.