The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility.

To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generative adversarial network (CTGAN) and AlexNet was proposed. The self-attention mechanism is introduced into the generator of CTGAN to maintain the correlation between the indicators of the input data, and a large amounts of high-quality data are generated according to the small number of fault samples. The generated data are input into the AlexNet model for fault diagnosis. The experimental results demonstrate that compared with the SMOTE and CTGAN methods, the dataset generated by the self-attention-conditional tabular generative adversarial network (SA-CTGAN) model has better data relevance. The accuracy of fault diagnosis by the proposed method reaches 94.81%, which is improved by about 11% compared with the model trained on the original data.

Conceptual design is a key link in the process of complex product design, and it is very important to select the appropriate design scheme; however, there are many types and inaccuracies of the evaluation data, and there is a problem of mutual influence between the evaluation criteria, which leads to unreliable decision making of the optimal solution. In order to solve this problem, a decision-making method based on intuitionistic fuzzy sets (IFS) and prospect theory is proposed. This method can be used for symmetric and asymmetric evaluation data. The evaluation data are classified according to different expression types and unified using intuitionistic fuzzy numbers. The intuitionistic fuzzy prospect value of decision information is calculated using prospect theory, and the prospect transformation of decision information is completed. At the same time, the Gray Relational Analysis (GRA) method and the Criteria Importance Though Intercriteria Correlation (CRITIC) method are used to calculate the subjective and objective weights of the technical and economic evaluation indexes of the product, and the combination weights are given; then, based on the evidence theory, the basic probability distribution of the evidence chain of all conceptual design schemes is synthesized, and the comprehensive prospect evaluation results of the schemes are obtained to complete the optimization of the conceptual design schemes. Finally, the effectiveness of the proposed method is verified by the conceptual design of the chip removal system of the deep hole machining machine tool. This work provides a promising method for decision makers to optimize the design scheme and provides insights into multi-objective decision-making problems.

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.