Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reliability of industrial equipment. Remaining useful life (RUL) prediction plays a pivotal role in this process. Accurate prediction results can effectively provide information about the condition of the equipment on which intelligent maintenance can be based, with many methods applied to this task. However, the current problems of inadequate feature extraction and poor correlation between prediction results and data still affect the prediction accuracy. To overcome these obstacles, we constructed a new fusion model that extracts data features based on a broad learning system (BLS) and embeds long short-term memory (LSTM) to process time-series information, named as the B-LSTM. First, the LSTM controls the transmission of information from the data to the gate mechanism, and the retained information generates the mapped features and forms the feature nodes. Then, the random feature nodes are supplemented by an activation function that generates enhancement nodes with greater expressive power, increasing the nonlinear factor in the network, and eventually the feature nodes and enhancement nodes are jointly connected to the output layer. The B-LSTM was experimentally used with the C-MAPSS dataset and the results of comparison with several mainstream methods showed that the new model achieved significant improvements.

This goal of this paper is to provide a framework by which China should accelerate the development and production of new energy vehicles, which should effectively address current energy and environmental pressures, while promoting the sustainable development of the automotive industry, which is an urgent task. In addition, this paper provides guidelines that seek to transform China’s auto industry while developing a new economic growth point to gain an international competitive advantage with strategic initiatives. This study aims to provide an ANP-SWOT (Analytic Network Process and Strength-Weakness-Opportunity-Threat analysis) approach for an interdependency analysis and to prioritize the new energy automobile industry in China. Firstly, a SWOT model is used to analyze the internal and external factors surrounding the development of the new energy automobile industry in China. Secondly, four types of development strategies are proposed by means of the SWOT matrix according to the conclusions of the factor analysis. Finally, the ANP network structure is designed to measure the effects of influential sub-factors, and then to define a strategic plan for China’s new energy automobile industry. The results of this study show that the optimal short-term development strategy for China’s new energy automotive industry is to increase the construction of new energy vehicle-related facilities, while the best long-term development strategy is to use local advantages and resources, through cost control measures which increase competition within the local new energy automotive industry.

Background: Type 2 Diabetes Mellitus (T2DM) is a chronic disease closely related to personal life style. Therefore, achieving effective self-management is one of the most important ways to control it. There is evidence that social support can help to improve the self-management ability of patients with T2DM, but which social support is more effective has been rarely explored. The purpose of this study is to construct an integrated model to analyze which social support has more significant impact on self-management of T2DM, and provide reasonable suggestions to health care providers on how to effectively play the role of social support. Methods: We established a social support indicator evaluation system and proposed an integrated model that combines ANP (Analytical Network Process) and CRITIC (CRiteria Importance through Intercriteria Correlation) methods to evaluate the impact of social support on T2DM self-management from both subjective and objective perspectives. The weights calculated by the model will serve as the basis for us to judge the importance of different social support indicators. Results: Informational support (weighting 49.26%) is the most important criteria, followed by tangible support (weighting 39.24%) and emotional support (weighting 11.51%). Among 11 sub-criteria, guidance (weighting 23.05%) and feedback (weighting 14.68%) are two most relevant with T2DM self-management. This result provides ideas and evidence for health care providers on how to offer more effective social support. Conclusion: To our knowledge, this is the first study in which Multi-Criteria Decision Making (MCDM) tools, specifically ANP and CRITIC, are used to evaluate the impact of social support on improving self-management of type 2 diabetes. The study suggests that incorporating two sub-indicators of guidance and feedback into the diabetes care programs may have great potential to improve T2DM self-management and further control patient blood glucose and reduce complications.

Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (two-dimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.

In actual industrial application scenarios, noise pollution makes it difficult to extract fault features accurately via conventional methods. A novel method for rolling bearing fault diagnosis combining a convolutional neural network (CNN), an attention mechanism for a squeeze-and-excitation network (SENet) module and a broad learning system (BLS) is proposed (SECNN–BLS). The one-dimensional bearing vibration signal is processed by using multiple short-time Fourier transforms (STFT); the two-dimensional image in the time-frequency domain is used as the model input. In the CNN fault feature extraction process, SENet is introduced to replace the CNN convolution layer, and global information is obtained through the squeeze operation. Excitation operation captures the importance of image channels, assigns weights adaptively to improve the attention on important information and eliminates the interference of irrelevant features without increasing the spatial and temporal complexity. The weighted feature representation is then transferred to the BLS input. The BLS has the characteristics of a flat structure and ridge regression to quickly solve the weights; as a fault classifier, it can save more computing resources and improve the accuracy of the fault classification. The SECNN–BLS fault diagnosis has achieved more than 98% accuracy on the Society for Machinery Failure Prevention Technology (MFPT) dataset. We also demonstrate the excellent performance of SECNN–BLS in a noisy environment.

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.