It is necessary to perform condition monitoring and fault identification on ship-borne antennae to ensure navigation safety. However, timely fault identification of key parts in a complicated drivetrain of the ship-borne antenna is still a challenging task since fault features are usually modulated and inevitably submerged by heavy noise. Therefore, a new adaptive feature extraction method based on Feature Mode Decomposition (FMD) is proposed in this paper for diagnostic purpose. First, scale transform is applied on the spectrum of the monitoring signal to determine the parameters of FMD, including the number of FIR filters, the location of each filter and the order of the filter bank. Second, a modified feature mode decomposition (MFMD) algorithm is proposed to adaptively extract mono-component modes and combine similar modes for weak feature enhancement. Finally, the combined modes are analyzed for mechanical fault identification based on Hilbert demodulation. Two application cases show that the proposed method owns superior performance during feature extraction, compared with the traditional methods.

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

Unlike common rotating machines, shipborne antennas always work under variable loads and suffer from extreme ocean conditions, which makes monitoring their condition and early fault identification necessary and challenging. However, extracting weak fault characteristics from vibration signals accurately and efficiently is difficult because of multi-modulation phenomenon and heavy noise. Therefore, an adaptive denoising method based on morphological filtering via structuring element optimization is proposed in this paper. The proposed method mainly includes two aspects: an adaptive spectrum segmentation algorithm via scale expression and a criterion based on the characteristic energy ratio for structuring element optimization. Experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method. The proposed method is also applied to identify an early antenna drivetrain fault in a real case, showing its superiority and effectiveness.

To address the urgent need for efficient and accurate perceptual capabilities in unmanned underwater vehicle (UUV)-based information countermeasure systems, this paper proposes a novel lightweight and edge-aware detection framework for forward-looking sonar images. Despite the advances in underwater target detection, existing models are often hindered by high computational costs, suboptimal boundary localization, and the lack of specialized sonar datasets. In this work, we propose Lightweight-YOLOv8, an object detection model tailored for sonar applications, and constructs a dedicated underwater sonar image (USI) dataset to support experimental validation. Specifically, we design a lightweight C2F module integrating inverted residual blocks to achieve a balance between detection accuracy and inference speed. To improve target representation, we propose a multi-scale and multi-receptive field feature fusion strategy. Furthermore, an edge-aware CIoU loss function is introduced by incorporating Canny edge detection to enhance boundary precision in sonar scenes. Experimental results on the USI dataset show that our model achieves a precision of 98.45%, surpassing YOLOv7 and YOLOv8 by 9.73% and 4.99%, respectively. Ablation studies confirm the contribution of each component. The proposed framework offers a promising solution for distributed, real-time acoustic perception in intelligent UUV missions.

Utilizing unsupervised domain adaptation for intelligent fault diagnosis (IFD) has demonstrated significant potential for ensuring the security of machinery systems. Nonetheless, the inherent imbalance attribute of collected data affects the performance of diagnostic model. Especially, for machines working under varied conditions, the acquired unlabeled data frequently exhibits diverse degrees of distributional deviations, thus further undermining the transferable model’s generalization capability. To address this challenge, we introduce a method termed Dynamic Unsupervised Imbalanced Domain Adaptation (DUIDA) for IFD. Employment of class rebalancing and label-dependent margin regularization strategies optimizes the selection of decision boundaries which counteract the distributional deviations introduced by the imbalance. In addition, by integrating a dynamic weighting mechanism, encompassing both adversarial-based and MMD-based domain adaptation, our model becomes versatile across varied UIDA tasks, assigning higher weights to fundamental faulty features. Finally, our empirical analyses on two faulty bearing datasets substantiate the efficacy and superior performance of the proposed framework across diverse operational scenarios.

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method.