Fault diagnosis that identifies the root of the abnormal status is of great importance to eliminate faults in the complex chemical processes. Many data-driven fault diagnosis models ignore different faults that occur with varied frequencies in chemical plants, and they need a complete retraining process with the arrival of new fault modes. In this article, a novel incremental imbalance modified convolutional neural network is proposed to solve the aforementioned issues. The proposed method employs an imbalance modified method to extract the valuable information from the imbalance data, and generate new samples. After that, a local hyperplane-based dynamic Relief is designed to reduce the dimension of the chemical data and simplify the complex learning process. Finally, for the arrival of new fault modes, the proposed method is prompted in an incremental hierarchical way. Unlike the traditional models that are trained on static data, the proposed method inherits the existing knowledge and updates itself to include new coming fault classes. The proposed method is utilized in a simulated process and a real industrial process. Experimental results illustrate that the proposed method is better than the existing methods and has significant robustness and reliability in chemical fault diagnosis.

Although sentiment analysis on microblog posts has been studied in depth, sentiment analysis of posts is still challenging because of the limited contextual information that they normally contain. In microblog environments, emoticons are frequently used and they have clear emotional meanings. They are important emotional signals for microblog sentimental analysis. Existing studies typically use emoticons as noisy sentiment labels or similar sentiment indicators to effectively train classifier but overlook their emotional potentiality.We address this issue by constructing an emotional space as a feature representation matrix and projecting emoticons and words into the emotional space based on the semantic composition. To improve the performance of sentimental analysis, we propose a new emotion-semantic enhanced convolutional neural network (ECNN) model. ECNN can use emoticon embedding as an emotional space projection operator. By projecting emoticons and words into an emoticon space, it can help identify subjectivity, polarity and emotion in microblog environments. It is more capable of capturing emotion semantic than other models, so it can improve the sentiment analysis performance. The experimental results show that this model consistently outperforms other models on the dataset of several sentiment tasks. This paper provides insights on the design of ECNN for sentimental analysis in other natural language processing tasks.

Fault diagnosis is of great importance to enhance the reliability and security of the complex chemical processes. However, many fault diagnosis methods cannot extract correlation information among attributes in the feature extraction procedure, resulting in the degradation of diagnosis performance. In this paper, a novel framework is proposed for fault diagnosis of the chemical processes. First, in the framework, a enhanced kernel principal component analysis (eKPCA) is proposed to obtain the key features of the fault based on the Hotelling’s T2 and squared prediction error (SPE) statistics. Second, to learn the correlation information of the features, an enhanced Naive Bayesian model (eNBM) is constructed with multi-variate Gaussian kernel function. Third, dragonfly algorithm (DA) is employed to find the optimal smoothing parameter to improve the performance of eNBM. The framework is applied to make fault diagnosis of Tennessee Eastman process (TE). Experimental results show that the proposed framework is more effective compared to the already existed traditional approaches such as deep learning.