To address the problems of low diagnostic accuracy and slow diagnostic speed of the convolutional neural network (CNN) fault diagnosis method in rolling bearing diagnosis, a new rolling bearing fault diagnosis method based on fast Fourier transform (FFT) image coding and lightweight-CNN (L-CNN) is proposed. The method is mainly divided into three stages: firstly, the original signal is reconstructed by noise reduction using a joint noise reduction method of complete ensemble empirical mode decomposition with adaptive noise, permutation entropy, and wavelet threshold denoise; then, the frequency spectra and phase spectra feature fusion data of the noise-reduced and reconstructed bearing vibration signals are obtained by FFT, the feature fusion data are encoded into a heat map, and the image coding data-set is fed into an improved L-CNN for fault diagnosis. Experiments were carried out using the Guangdong University of Petrochemical Technology bearing fault data-set and the Case Western Reserve University bearing fault data-set with diagnostic accuracies of 98.75% and 99%, respectively. The results demonstrate that the method can effectively classify bearing fault vibration signals with the advantages of a fast diagnosis, high accuracy, and good generalization ability.

Electrical discharge machining (EDM) is a non-traditional process, which can cut materials with a high melting point, high hardness, high strength, and low brittleness. However, the kerosene (dielectric of EDM) produces aerosols and toxic gases at high temperatures, which seriously affect the health of operators and air quality. This means that it is not conducive to the green manufacturing and sustainable development of EDM. In this study, thereafter, sunflower seed oil (SSO) and kerosene were used as dielectrics of EDM for machine SKD11, and the machining performance of the two dielectrics under different current, duty ratio, pulse duration and electrodes were comparatively analyzed, such as material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV) and exhaust emissions characteristics (EEC). This investigation found that the minimum value of EEV in SSO was 0.3879 kJ/mm³, which was about 25% lower than the minimum value of 0.4849 kJ/mm³ in kerosene. The emission rate of Cu electrode in SSO was 62.017 µg/min, which was lower than that in 78.857 µg/min, decreasing by about 21.36%, in kerosene. In addition, a super depth of field optical micro-scope was subsequently used in the experiments to observe the diameter of the debris. The results indicated that SSO has a larger proportion of debris of more than 35 µm in diameter. Therefore, SSO can be adopted as a substitute for kerosene dielectric to improve the sustainability of electrical discharge machining and realize green manufacturing.

In the context of continuous technological progress, the application of information technology in the field of education has gradually opened the door to innovation. The integration of information technology and curriculum is the use of information technology to present the curriculum in a new way in the teaching process. Therefore, compared with the traditional teaching mode, the integration of information technology and curriculum is more flexible and can show the boring classroom vividly and graphically in front of students, thus motivating students to learn actively and improving classroom efficiency. In recent years, with the continuous development of computer technology, the rise of information technology such as data analysis and the Internet of Things has caused a wave of “Internet+” education. With the promotion of education informatization, the smart classroom has been developed based on modern education technology. As a result, the classroom teaching mode based on modern information technology has become one of the hot spots for research. As a product of the integration of new technologies and teaching, the smart classroom creates a better learning environment for students with features such as intelligence, diversity, and personalization. At the same time, this teaching model gradually cultivates the information literacy of teachers and students while improving learning effectiveness. Also, with the rapid development of modern information technology, integrated teaching has become a popular and common teaching method. In this context, integrated teaching has a great impact on traditional teaching methods. This requires teachers to make corresponding changes in classroom teaching methods, approaches, and strategies in line with the trend of the times. However, although there are many changes, the corresponding teaching theory has not been updated. While modern information technologies such as artificial intelligence and big data have been widely used to track, collect, and analyze human behavior, it has been difficult for teachers to integrate these technologies into their teaching to help them teach effectively. In recent years, a growing number of experts and scholars have joined the discussion on instructional design that incorporates modern information technologies. There is a need to explore how to teach students in depth through big data and other tools, but there is no complete theoretical chain. Therefore, it is necessary to explore the reform and practice of modern Internet-based classroom teaching models. This study begins with an understanding of the current utilization of smart learning platforms in some schools and the teaching needs of IT subjects. After identifying the value of the IT smart classroom teaching model, the current research status of the smart classroom is outlined, and related concepts and theories are outlined and understood. After that, the characteristics of smart classrooms are summarized, and the traditional classroom, flipped classroom, and smart classroom are compared and analyzed.

The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

Traditional hazard and operability analysis (HAZOP) is one of the most widely applied methods for process safety management in process enterprises. Due to its principles based on the conservative and qualitative judgment, it often leads to too conservative risk identification results for the fluorine chemical industry usually with high-risk processes to keep the continuity of production. Most of improved quantitative and semi-quantitative methods are based on the layer of protection analysis (LOPA) to resolve the over-conservative problem of traditional HAZOP with the database of LOPA. However, the improved model, taking LOPA as the main line and HAZOP only as the provider of scenarios and influencing factors, is limited to the fact that LOPA can only analyze complete and independent protection layers (IPLs). Therefore, in order to realize the quantitative or semi-quantitative analysis of disaster causes and consequences, a new semi-quantitative HAZOP method takes HAZOP as the main line to integrate LOPA, F&EI (fire and explosion index) for quantitatively calculating the reduction factors, probability on failure demand (PFD) of general protection layers (GPLs) and PFD of IPLs. With the case comparison of fluorine chemical industry, it is proved that this new method can effectively improve the problem that traditional HAZOP are too conservative in complex scenarios.