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The objective was to investigate the utility of cottonseed oil (CSO) as a raw material for the synthesis of CSO water-based alkyd resin. The synthesis involved the polymerization of CSO, trimethylolpropane, phthalic anhydride (PA) and trimellitic anhydride (TMA). The prepared resin coating material was subsequently applied to the surface of steel structure material.

This study aimed to synthesize water-based alkyd resins using CSO. Therefore, the alkyd resin was introduced with TMA containing carboxyl groups and neutralized with triethylamine (TEA) to form a water-soluble salt. Then, the esterification kinetics of CSO water-based alkyd resin were investigated, and finally, the basic properties of CSO water-based alkyd resin coating were evaluated.

It was demonstrated that CSO water-based alkyd resin exhibited excellent water solubility and that the esterification kinetic of the synthesis reaction could be described by a second-order reaction. The coating properties of the material were investigated and found to have good basic properties, with 40% resin addition having the best corrosion resistance. Consequently, it could be effectively applied to the surface of steel structural materials.

This study not only met the requirement of environmentally friendly development but also expanded the application of CSO through the synthesis of CSO water-based alkyd resin via alcoholysis. Compared to fatty acid process, the alcoholysis reduced the need for fatty acid pre-extraction, simplifying the alkyd resin synthesis process. Thus, economic costs are effectively reduced.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.