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Purpose: In the winding process of chemical fiber production, the winding machine was responsible for winding the post-treated primary fiber. The winding machine clamp head was used as the direct winding part, and its running state would directly affect the quality and production efficiency of the chemical fiber cake. With the increasing complexity and precision of chemical fiber equipment, the traditional method of fault diagnosis relying on manual experience has been unable to meet the needs of intelligent production. The winding equipment also has some problems, such as few fault samples, strong background noise interference and large speed fluctuation. Therefore, the study of a better intelligent fault diagnosis method is of great significance to improve the winding reliability of the clamp head and ensure the quality and production efficiency of the chemical fiber cake. Methods: In this paper, a fault diagnosis method based on Empirical Mode Decomposition Order Spectrum of vibration signal and Dual-Channel Convolutional Neural Network (EMDOS-DCCNN) was proposed and the corresponding fault diagnosis model was designed. The trigger interval sampling scheme was designed according to the characteristics of the changing working conditions of the winder. The vibration data at the base of the winder's clamp head was collected by data acquisition equipment from Zhongli Winder in a chemical fiber production plant in Zhejiang Province as the real measured data for rolling bearing diagnosis. The strong background noise and the influence of the variable speed of the winder were removed by Empirical Mode Decomposition (EMD) reconstruction of the original signal and order spectrum analysis. The dual-channel convolutional neural network model was used for fault diagnosis, and the dual-channel data was fused to further improve the fault diagnosis accuracy. Order analysis and validation set confusion matrix were used to evaluate the diagnostic model proposed in this paper. Results and Conclusion: The model was trained with vibration data collected at three rotational speeds and verified on data sets collected at other rotational speeds. The results showed that the fault diagnosis model of rolling bearings based on EMDOS-DCCNN could effectively remove noise, and a single diagnosis model could be used to diagnose rolling bearings at different rotational speeds. In the model evaluation experiment, the accuracy of the dual-channel CNN model was stable at 98.6%, which was 3.1% higher than that of the single-channel model. It had high fault identification accuracy and adaptability at different speeds, and the model was suitable for variable working conditions and had better robustness. [ABSTRACT FROM AUTHOR]

Accurate prediction of cycle time (CT) plays an important role in the promises of a good delivery-time for semiconductor manufacturers. However, the CTs of wafer lots are difficult to forecast since they are influent by a lot factors (e.g., workload for every machine). The identification of key factors (called CT-related) can not only improve the prediction performance but also facilitate the control of CT, which is of particular significance. This paper focuses on gathering subtle variables into the candidate variables set along with the further analysis which is required in the correlation analysis between various candidate factors and CTs of wafer lots. The candidates set is first constructed to collect all factors that may affect wafer lots’ CT. Then, a regression-based model is proposed to select CT-related variables, which consists of three parts: 1) discretization; 2) adaptive logistic regression-based correlation analysis; and 3) CT-related factor selection. Subsequently, a parallel computation model is implemented to forecast the CTs of wafer lots. In the numerical experiments, 108 CT-related factors stood out from 774 candidates and replaced six global factors (used as reference) to predict CTs of wafer lots. The results indicated that the proposed approach had higher accuracy than linear regression and back propagation network in CT forecasting in large scale data analysis. [ABSTRACT FROM PUBLISHER]

At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks. [ABSTRACT FROM AUTHOR]

Performance prediction plays a key role in product quality control and quality optimization. However, due to the error transmission in the refrigeration compressor assembly process and the imbalance of data collected during the refrigeration compressor assembly process, existing performance prediction methods cannot accurately extract key features of the assembly process resulting in low prediction accuracy. In this paper, a knowledge graph neural network (AKGNN) model with predictive value calibration is proposed for refrigeration compressor performance prediction in two stages. First, a sequence graph attention feature extraction module is designed to extract key features of the assembly process data to cope with the assembly error propagation. Second, to deal with the imbalanced distribution of compressor data, a predictive value calibration (PC) module is designed to calibrate the output of the first stage. Experimental results demonstrate the proposed method outperformed all compared cutting-edge methods in four evaluated indicators. [ABSTRACT FROM AUTHOR]