Gangjin Huang

Accurate tool wear monitoring is crucial for the efficient and stable operation of smart manufacturing systems. However, multi-source sensor signals in complex cutting environments often couple high-frequency transient impacts with low-frequency long-term degradation trends. Traditional deep learning models, such as Long Short-Term Memory (LSTM) networks, frequently encounter challenges regarding long-range historical information decay and localized perceptual imbalance when processing such multi-scale features. To address these bottlenecks, this paper proposes a Bidirectional Long-term Attentive State Space Model (Bi-LASM). This model integrates bidirectional recurrent gating mechanisms with Selective State Space Models. Specifically, an adaptive multi-source signal fusion mechanism is designed to extract condition-sensitive channel features. Subsequently, an input-dependent dual-scale linear state space branch is constructed to decouple and parallelly fit the long-term smooth degradation patterns and short-term local mutation characteristics of the tool. Furthermore, a dynamic gating mechanism is introduced to achieve step-level adaptive weight allocation between long-term and short-term memory. Comprehensive experiments on an actual impeller milling dataset integrating force, vibration, sound pressure, and current sensors demonstrate that Bi-LASM achieves an overall recognition accuracy of 98.12%. This performance yields a 1.87% improvement over the pure Selective SSM baseline. Furthermore, the proposed model consistently outperforms mainstream architectures, including the Transformer and Bi-LSTM. The proposed method exhibits stable performance under complex working conditions and provides explicit physical interpretability for tool state evolution.

To address the detection challenges in defective images of insulators in transmission lines, including tiny object size, significant scale variations, a wide variety of defects, and complex background interference. In this study, an improved insulator defect detection algorithm is proposed, based on the YOLOv8s framework and combining feature enhancement and deformable convolution techniques. Firstly, to address the image feature distortion problem caused by aerial photography, a deformable convolutional feature extraction module (DCFEM) is introduced, which is designed to enhance the model’s ability to adapt to the local geometric deformation, so as to effectively recover the distorted feature information in the image. Moreover, to enhance the detection ability of the model for small objects, a small object feature enhancement module is designed, which adopts an efficient multi-scale attention mechanism, and aims to enhance the feature extraction ability of small objects, improve the sensitivity to small-size defects, and improve the detection accuracy. Eventually, to optimize the computational efficiency of the model, the average pooling-sparse convolution-batch normalization (BN) module is proposed. This module combines average pooling, sparse convolution and BN techniques to achieve a lightweight model while maintaining a high level of feature extraction capability. Experimental results on the China power line insulator dataset show that the improved model achieves a 4.3 percentage point improvement in the mAP metric compared to YOLOv8s, and the number of parameters in the model is reduced by 10%. The proposed scheme not only improves the accuracy and efficiency of defect detection, but also reduces the demand for computational resources, thus providing a more reliable and efficient solution for insulator defect detection in practical applications.

Detecting faults in bearings is essential for the maintenance and operation of rotating machinery. However, achieving high accuracy and noise immunity is challenging due to the involvement of intricate and noisy signals. To address this issue, this paper introduces a multi-scale separable gated convolutional neural network (GCK-MSSC). In the GCK-MSSC model, the gate convolutional kernel replaces the conventional convolutional kernel. It is designed to dynamically adjust the convolution kernel’s weights based on the input features. Additionally, the one-dimensional global attention mechanism is incorporated, enhancing the model’s global awareness within the MSSC framework. The experimental results on two public bearing datasets confirm the performance of the proposed method. It demonstrates improved performance over current leading-edge methods, especially in terms of accuracy, and proves to be significantly robust against various levels of noise. Specifically, it achieves accuracies of 99.45% and 99.78% on the two datasets. Furthermore, even after the addition of noise with a signal-to-noise ratio of 0, it still maintains an accuracy as high as 85.65% (on the Politecnico di Torino dataset).

Rotating machinery is a widely-used component of factories and enterprises. Unquestionably, rolling bearing is the backbone of rotating machinery and its performance degradation assessment (PDA) is a vital part of condition-based intelligent health management. Therefore, a PDA method based on one-dimensional sparse representation self-learning dictionary is presented to assess the degradation trend of rolling-element bearings. We proposed a novel feature extraction method named one-dimensional K-singular value decomposition to extract the sensitive feature from run-to-failure data. Then, an improved support vector data description is adopted to establish the PDA model, and a novel degradation indicator is obtained. Subsequently, the proposed method adaptively tracks the development of deterioration and identifies the occurrence of an incipient fault. Experimental results on the simulation signal and XJTU-SY bearing datasets verify the effectiveness and outperform the conventional methods.

Intelligent tool wear recognition techniques have great significances to guide the cutting process in automated manufacturing systems. Traditional methods heavily rely on human experience and fail to consider the impact on processing. In this article, a novel deep kernel autoencoder (DKAE) feature learning method optimized by the gray wolf optimizer (GWO) is proposed for tool wear state intelligent recognition. The Gaussian kernel function is used to construct the loss function of the neural networks to enhance the feature learning ability. In addition, the current sensors are used to collect the multichannel spindle motor current signals in three directions of X-, Y-, and Z-axes. Compressed sensing technology is adopted to fuse and reduce the dimension of massive multichannel current signals into a single sample signal. A series of experiments with numerical control machines in the real manufacturing process of the impeller is run to test the superiority of tool wear recognition with this method. The results indicate that this work can be applied for real-time tool wear monitoring and greatly improved recognition accuracy.

Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.

Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.