Data-driven fault diagnosis techniques have been widely used in industrial processes. However, facing a large amount of high-dimensional, nonlinear, and strongly coupled process data, traditional data-driven methods achieve low diagnostic accuracy due to ignoring the structural features inside data. To overcome this problem, this article proposes an improved locality preserving projections based on the heat-kernel and cosine weight matrix named Heat-Kernel and Cosine Weights Locality Preserving Projections (HC-LPP). In HC-LPP, a novel weight matrix construction strategy is employed, where a heat-kernel function is combined with a cosine function to optimize the weight matrix between data in terms of shortening distance and angle correlation, respectively. With the new weight matrix, the proposed HC-LPP considers both the distance and the correlation of samples (the shorter the distance is, the closer the neighbors are; the smaller the angle is, the more similar the neighbors are). The dimensionality reduction process of HC-LPP can well maintain the spatial geometric structure of data. Finally, the proposed HC-LPP integrated with the AdaBoost. M2 classifier is applied to the Tennessee Eastman process and the PROcess NeTwork Optimization process for fault diagnosis performance verification. Simulation results show, the proposed HC-LPP achieves better performance in diagnostic accuracy compared with other related methods.

It has become a big challenge and a hot topic of research to capture the most relevant features from high-dimensional process data for enhancing fault diagnosis. To effectively extract discriminative features from high-dimensional data, a novel dimensionality reduction (DR) approach named neighborhood and locality projections with the farthest and nearest distance (FNDNLP) is first proposed for industrial process fault feature acquisition and diagnosis. By constructing intra-class weights and inter-class weights, FNDNLP takes both the intra-class distance and the inter-class distance into consideration in its objective function, improving the diagnostic ability of extracted features through maximizing the inter-class distance and minimizing the intra-class distance. In addition, bootstrap based FNDNLP (BFNDNLP) is further proposed to handle the matrix decomposition problem in FNDNLP. To find the proper order through DR, the Akaike information criterion (AIC) is adopted. Finally, the Naïve Bayes (NB) based classifier is utilized to achieve acceptable fault diagnosis. The simulation results from two complex industrial cases indicate that the proposed methodology can achieve higher diagnosis accuracy than other related methods. What's more, the DR features are further analyzed to show the effectiveness and benefits of the proposed BFNDNLP extraction approach. The main source code of this paper can be obtained on GitHub soon.

In order to ensure safe operations of industrial processes, it is especially important to find fault types accurately and quickly based on historical data and then deal with faults in time. Unfortunately, due to the tricky characteristics of industrial process data such as massive number, high dimensionality, and nonlinearity, it turns out that timely and accurate diagnosis of faults becomes of great difficulty in industrial processes. To address this problem, novel effective fault diagnosis using an improved global and local dimensionality reduction (DR) method named discrimination locality preserving projections integrated with sparse autoencoder (SAEDLPP) is proposed in this article. In SAEDLPP, the global DR information of data is first obtained by sparse autoencoder (SAE); next, the DR data obtained through SAE are passed through discrimination locality preserving projections (DLPP) to obtain local DR information, preserving not only the global information but the local information of the extracted features. Finally, fault diagnosis is achieved by separating the extracted features by SAEDLPP using an AdaBoost classifier to recognize fault types. Simulations are conducted on the Tennessee Eastman process (TEP) and the results indicate that the provided SAEDLPP-based fault diagnosis methodology can achieve much higher accuracy in fault diagnosis than other associated methods.

In the field of the fault diagnosis of industrial processes, there are many problems in process data, such as missing critical fault data, high repeatability of normal state data, and poor representation of faults data, which may reduce the accuracy of fault diagnosis. In this paper, a novel K-medoids based synthetic minority over-sampling technique that combines locality preserving projections (KMS-LPP), is proposed for fault diagnosis. First, the synthetic minority sampling technology (SMOTE) is designed based on K-medoid to generate minority fault samples to address the imbalanced problem of data. Second, to extract the key fault-relevant features and reserve the local structure information at the same time, the manifold learning (ML) approach locality preserving projections (LPP) is performed to reduce the dimensionality of data. Finally, the Adaboost. M2, as an ensemble classifier, is conducted for fault classification. Simulations of the Tennessee Eastman process (TEP) are performed for validation of the performance of the presented KMS-LPP method. The obtained results show that KMS-LPP has enhanced the performance of fault diagnosis due to its higher accuracy compared with traditional oversampling and feature extraction methods, which indicates the effectiveness of KMS-LPP.

As the production demand and external environment change, the same production process may have multiple stable working conditions, i.e., multimode process. The traditional process monitoring methods cannot be directly applied to industrial data with multipeak distribution. In order to address the multimode process monitoring problem, a cross-modality shared-specific network (CMSS-Net) is proposed in this article. First, to address the problem of unavailability of mode indicator variable, CMSS-Net adds a loss term based on discriminative idea to the loss function, which improves the mode recognition ability by maximizing the interclass distance and minimizing the intraclass distance. The multimode process is then monitored. Since different modes originate from the same production process, there exists some common information among modes. CMSS-Net extracts the shared information by minimizing the difference in the distribution of features among modes. At the same time, the gating mechanism is used to fuse the shared information between modes and the unique features of the modes into a multivariate feature fusion. This improves the performance of the model due to the information being enriched across the modes, while increasing the transparency of the model's decision-making process. Finally, the proposed method is developed as soft sensors for Tennessee Eastman process and power plant gas turbine emission process. It is compared with some popular methods. The experimental results demonstrate the effectiveness and superiority of CMSS-Net when applied to the multimode process.