Nonlinear Process Fault Research Articles

Multirate Nonlinear Process Fault Detection Based on Multiscale Hierarchical Variational Autoencoder

Multirate Nonlinear Process Fault Detection Based on Multiscale Hierarchical Variational Autoencoder

Reconstruction-based stacked sparse auto-encoder for nonlinear industrial process fault diagnosis

The reconstruction-based (RB) approach can effectively suppress the misdiagnosis problem due to the smearing effect in fault isolation. However, the current exploration of the RB approach for large-scale nonlinear systems is still limited. Therefore, this paper proposes a reliable and effective fault diagnosis method based on a reconstruction-based stacked sparse autoencoder (RBSSAE) for high-dimensional industrial systems. In RBSSAE, a reconstruction-based index achieved by the Steffensen iterative method is developed to check whether the given variable(s) are responsible for the faults efficiently. However, the number of possible faulty variable combinations grows exponentially with the system dimension or actual abnormal variables, causing an unbearable computational burden. Hence, the proposed RBSSAE utilizes a sequential floating forward selection approach to rapidly isolate the most decisive variable combination, meeting a requirement of online fault diagnosis. Finally, the effectiveness of the RBSSAE is verified on a numerical example and a real industrial case.

MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes

Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient faults in nonlinear and dynamic systems. Meanwhile, fault identification experiments were conducted, validating the high accuracy of the fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

Novel adaptive fault detection method based on kernel entropy component analysis integrating moving window of dissimilarity for nonlinear dynamic processes

Fault detection of nonlinear dynamic processes can ensure the safety of industrial production processes. Industrial process data are mostly autocorrelated along with strong nonlinear characteristics. And these significant characteristics interact with each other and limit the fault detection performance of traditional methods. Therefore, this paper presents a novel adaptive fault detection method for nonlinear dynamic processes based on kernel entropy component analysis (KECA) integrating the moving window of dissimilarity (DMW) (KECA-DMW). The KECA is used to map the raw data and capture the nonlinear features of the data, which combine with moving window techniques to build the fault detection model. In the process of updating the data in the moving window, the data information of the historical window is combined with that of the current window to obtain a more comprehensive judgment of the current moment. Then a dynamic update fusion method with adaptive weight allocation based on the dissimilarity index is proposed by analyzing the data characteristics of window information at different moments through the dissimilarity. Finally, three example studies with a numerical example, a closed-loop continuously stirred tank reactor and a Tennessee-Eastman process are used to validate the effectiveness of the proposed method. Compared with other nonlinear dynamic process fault detection methods, the results verify the effectiveness of the proposed method in the process monitoring performance of nonlinear dynamic processes in terms of false alarm rate and fault detection rate, where the false alarm rates of the proposed method are only 2%, 1.83%, and 4.33%, while the fault detection rates are 97.4%, 96.83%, and 86.25%, respectively.

Improved dynamic kernelPCAbased on local preserving projections and its application for electric submersible pump fault diagnosis

AbstractDynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as theT2and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.

Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

Improved key performance indicator-partial least squares method for nonlinear process fault detection based on just-in-time learning

Key performance indicator-partial least squares (KPI-PLS) achieves linear indicator fault detection through the appropriate decomposition of variables into related and unrelated parts. This approach uses the projection matrix obtained by general singular value decomposition to decompose the residual space of PLS and thus achieves a more reasonable division of data space. However, KPI-PLS has limitations in handling nonlinear issues. Such problems can be effectively addressed by a local model, just-in-time learning (JITL). Therefore, in this study, an improved KPI-PLS method combined with JITL (JITL-KPI-PLS) is proposed to achieve nonlinear fault detection. The local model is built using hierarchical clustering to improve computational efficiency of JITL for searching relevant samples. By updating the model and control limit in real time, the current status of online samples can be better tracked. Finally, better fault detection performance of the proposed method is confirmed on the Tennessee Eastman benchmark and the Zhoushan thermal power plant.

Principal Polynomial Nonlinear Process Fault Detection Based on Neighborhood Preserving Embedding

Principal Polynomial Nonlinear Process Fault Detection Based on Neighborhood Preserving Embedding

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

We suggest in this article a dynamic reduced algorithm in order to enhance the monitoring abilities of nonlinear processes. Dynamic fault detection using data-driven methods is among the key technologies, which shows its ability to improve the performance of dynamic systems. Among the data-driven techniques, we find the kernel partial least squares (KPLS) which is presented as an interesting method for fault detection and monitoring in industrial systems. The dynamic reduced KPLS method is proposed for the fault detection procedure in order to use the advantages of the reduced KPLS models in online mode. Furthermore, the suggested method is developed to monitor the time-varying dynamic system and also update the model of reduced reference. The reduced model is used to minimize the computational cost and time and also to choose a reduced set of kernel functions. Indeed, the dynamic reduced KPLS allows adaptation of the reduced model, observation by observation, without the risk of losing or deleting important information. For each observation, the update of the model is available if and only if a further normal observation that contains new pertinent information is present. The general principle is to take only the normal and the important new observation in the feature space. Then the reduced set is built for the fault detection in the online phase based on a quadratic prediction error chart. Thereafter, the Tennessee Eastman process and air quality are used to precise the performances of the suggested methods. The simulation results of the dynamic reduced KPLS method are compared with the standard one.

Autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis

Industrial processes usually exhibit great nonlinearity generated from the effects of complex mechanisms, system integrations and multiple working conditions. Although a variety of dictionary learning algorithms have been proposed in recent years for industrial process fault diagnosis, most of them only model the process data via a linear combination of a few dictionary atoms, which cannot effectively characterize the nonlinear relationships among variables and may lead to limited diagnosis performance. Recent improvements in multilayer neural networks, especially the autoencoders, offer opportunities to tackle the nonlinear problem. However, the overall limited availability of fault samples poses great challenges in achieving satisfactory performance. To address the mentioned issues simultaneously, the present study proposes an Autoencoder Embedded Dictionary Learning approach (AEDL) for nonlinear industrial process fault diagnosis. First, an autoencoder is employed to learn a nonlinear mapping that maps the linearly inseparable industrial process data to a high-dimensional space, where a desired dictionary is learned according to the basic dictionary learning algorithm. Next, two supervised graphs, leveraging the priors of industrial process data, are introduced into the learning process to make the proposed approach robust to training samples. After obtaining the dictionary, the coding coefficients of the process data over the dictionary can be used for fault diagnosis via a simple classifier. As revealed from the encouraging experimental results on the Tennessee Eastman process, the developed approach outperforms several dictionary learning approaches and some other nonlinear fault diagnosis methods.

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry.

This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.

Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes

Single traditional multivariate statistical monitoring methods, such as principal component analysis (PCA) and canonical variate analysis (CVA), are less effective in nonlinear dynamic processes. Monitoring approaches based on radial basis kernel function have been intensively applied. However, an infinite dimension nonlinear mapping is redundant and inefficient. To improve the efficiency of traditional methods and consider the nonlinearity and dynamics simultaneously, this paper proposes canonical variate nonlinear principal component analysis (CV-NPCA) based on explicit polynomial mapping and combined statistic for detecting and identifying faults in nonlinear dynamic processes. There are two main contributions of the proposed method. First, explicit second-order polynomial mapping is introduced to combine CVA with PCA to simultaneously decrease the adverse effects of nonlinearity and dynamics. Second, the $Q_{c}$ statistic combining residual vectors with residual components is proposed, and a two-dimensional (2D) contribution plot and the variable with the largest contribution based on the $Q_{c}$ statistic are given for fault identification in the simulation study. Compared with the results of PCA, CVA, kernel principal component analysis (KPCA), nonlinear dynamic principal component analysis (NDPCA) and kernel entropy component analysis (KECA), the proposed method not only has relatively higher fault detection rates and identification rates but also has lower false alarm rates in the numerical simulation process and the benchmark Tennessee Eastman process.

Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis

AbstractThe batch process generally covers high nonlinearity and two‐directional dynamics: time‐wise dynamics, which correspond to inherently time‐varying dynamics resulting from the slowly varying underlying driving forces within each batch duration; and batch‐wise dynamics, which are associated with different operating modes among different batches. However, most existing dynamic nonlinear monitoring methods cannot extract the slowly varying underlying driving forces of the nonlinear batch process and rarely tackle the batch‐wise dynamic characteristics among batch runs. In order to address these issues, a new monitoring scheme based on two‐directional dynamic kernel slow feature analysis (TDKSFA) is developed by combining kernel SFA with a global modelling strategy. In the TDKSFA method, kernel SFA is integrated with the ARMAX time series model to mine the nonlinear and time‐wise dynamic properties within a batch run due to its capability of extracting the slowly varying underlying driving forces. Furthermore, the global modelling strategy is presented to handle the batch‐wise dynamics among batches by calculating the total average kernel matrix of all training batches. After the slow features are extracted, Hotelling's T2 and SPE statistics are built to detect faults. To solve the issue of fault variable nonlinear identification, a novel nonlinear contribution plot inspired by the pseudo‐sample variable projection trajectories in the TDKSFA model is further proposed to identify fault variables. Finally, the feasibility and effectiveness of the TDKSFA‐based fault diagnosis strategy is demonstrated through a numerical system and the penicillin fermentation process.

Data‐driven nonlinear chemical process fault diagnosis based on hierarchical representation learning

AbstractRepresentation extraction is crucial in data‐driven process monitoring, and deep neural network (DNN) is an efficient tool for extracting representations from considerable process data. This study proposes a hierarchical representation learning (HRL) method that integrates the deep belief neural (DBN) network and support vector data description (SVDD) for efficient nonlinear chemical process fault diagnosis. First, hierarchical representations containing meaningful process information are generated through a DBN network by utilizing generally massive normal operating process data. Second, an SVDD‐based decision‐making system is constructed using generally small‐sized faulty data. Three experimental studies are then conducted. A comparison of results with those of several state‐of‐the‐art methods reveal the suitability of the HRL method for process monitoring due to its two main advantages. First, DNN has a superior representative ability and generates representations with richer process information than conventional data‐driven methods. Second, the HRL method utilizes available process data and is suitable for practical conditions in which considerable normal operating data but limited small‐sized faulty data are available.

Locally linear back-propagation based contribution for nonlinear process fault diagnosis

This paper proposes a novel locally linear backpropagation based contribution (LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder (AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution (RBC), the propagation of fault information is described by using back-propagation (BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well, and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Research on TE process fault diagnosis method based on DBN and dropout

AbstractIn recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN‐dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the real‐time monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach.

Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

Kernel principal component analysis (KPCA) has been widely applied to the nonlinear process fault diagnosis field. However, it often does not perform well in the case of incipient faults because of...

Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection

It is vital for fault detection technology to extract features of industrial process data effectively. Local kernel principal component analysis (LKPCA) has proved its good performance in preserving global and local structural characteristics. However, it ignored useful high-order statistics of data, so multi-block statistics local kernel principal component analysis (MSLKPCA) algorithm integrating statistics pattern analysis (SPA) into LKPCA is proposed. The correlation coefficient matrix is first calculated and K-means clustering is adopted to divide the original variables into several blocks. Then the weighted SPA, which gives different weights to different samples in each window according to their distributions, is adopted to build statistic spaces containing both low-order and high-order statistics. After that, LKCPA is performed in each statistic space to realize feature extraction. To reduce the noise effect amplified by SPA, PCA is adopted in the residual space to remove noise. Bayesian strategy is used to fuse the results of each block and two monitoring statistics ET and ER are proposed to monitor the feature space and the residual space respectively. The Tennessee–Eastman (TE) process simulation shows the effectiveness and superiority of the proposed algorithm for process monitoring.

Accelerated Kernel Canonical Correlation Analysis with Fault Relevance for Nonlinear Process Fault Isolation

In the field of multivariate statistical process monitoring (MSPM), fault isolation has attracted increasing attention, due to its importance in ensuring process reliability and product quality. Ho...

A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant

Probabilistic principal component analysis (PPCA) based approaches have been widely used in the field of process monitoring. However, the traditional PPCA approach is still limited to linear dimensionality reduction. Although the nonlinear projection model of PPCA can be obtained by Gaussian process mapping, the model still lacks robustness and is susceptible to process noise. Therefore, this paper proposes a new nonlinear process monitoring and fault diagnosis approach based on the Bayesian Gaussian latent variable model (Bay-GPLVM). Bay-GPLVM can obtain the posterior distribution rather than point estimation for latent variables, so the model is more robust. Two monitoring statistics corresponding to latent space and residual space are constructed for PM-FD purpose. Further, the cause of fault is analyzed by calculating the gradient value of the variable at the fault point. Compared with several PPCA-based monitoring approaches in theory and practical application, the Bay-GPLVM-based process monitoring approach can better deal with nonlinear processes and show high efficiency in process monitoring.