## 1 Introduction  
Due to the rapid growth of mechanical automation and artificial intelligence, chemical control systems no longer rely solely on manual operations to manage complicated chemical conditions. However, the products of the chemical process are frequently poisonous, combustible, and explosive due to its tedious procedures and changing environment. Once a breakdown has occurred, the risk is significantly greater than in other businesses, and it is simple to cause massive deaths, environmental degradation, and economic losses [1, 2]. In the past few years, academic and business circles around the world have become more interested in chemical process defect detection and diagnosis, which is one of the most important parts of modern chemical systems [3]. Failure in a chemical process is characterized by the deviation of one or more process variables from their normal state. The fault detection and diagnosis technology monitors the entire system operation process and determines the fault type based on state variables with different deviations [4, 5]. Currently, defect diagnosis methodologies can be loosely categorized as knowledge-driven, model-driven, and data-driven [6]. However, the system's size continues to increase, and the tightness of the correlation between feature variables increases. Knowledge-driven and model-driven diagnostic procedures can no longer meet modern industrial systems' speed and accuracy requirements for massive data processing. The data-driven method is based on process data, makes a decision-making model, mimics how the factory actually works, can find and diagnose problems well, and is becoming the standard method. In the realm of industrial process fault diagnosis, multivariate statistical approaches, such as independent component analysis (ICA), principal component analysis (PCA), and fisher discriminant analysis (FDA), have been frequently utilized [7; 8; 9; 10]. As the number of data dimensions increases, however, the complexity of these statistics-based procedures increases exponentially, resulting in a dimensional catastrophe. The gradual application of shallow learning techniques such as support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) converts defect detection and diagnosis problems into classification problems [11; 12; 13]. However, they rely on extensive training and fault samples, which are difficult for chemical diagnostic models. The problem of troubleshooting chemical processes has been of interest to research scholars. [PERSON] et al. [14] discovered hidden knowledge in industrial datasets by revealing explainable patterns associated with underlying physical phenomena through logical analysis of data (LAD). These patterns are then combined to build a decision model for diagnosing faults during process operations and explaining the potential causes of these faults. [PERSON] et al. [15] used bidirectional recurrent neural network (BiRNN) to construct fault detection and diagnosis (FDD) models with complex RNN units and demonstrated the effectiveness of implementing BiRNN in chemical process fault diagnosis. [PERSON] et al. [16] proposed an extended deep belief network (EDBN) to use the valuable information in the raw data fully. The raw data are also combined with hidden features as the input to each extended restricted Boltzmann machine (ERBM). [PERSON] et al. [17] used long short-term memory (LSTM) and convolutional neural network (CNN) to extract features separately and then fused the extracted features. The features are further compressed and extracted by using them as the input of a multilayer perceptron so that the final extracted features of the network have both spatial and temporal features, thus improving the diagnostic performance of the network. In addition, artificial neural networks have promising applications in fault diagnosis, and many different types of artificial neural networks are available for classification tasks.