Continuous Stirred Tank Heater Process Research Articles

Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

Prior knowledge-augmented unsupervised shapelet learning for unknown abnormal working condition discovery in industrial process

Unknown abnormal working condition discovery is the key of refinement industrial production. Clustering industrial time series is an effective way to discover unknown working condition types. However, it is challenge for existing time series cluster methods to discover unknown abnormal working condition from industrial time series. In this study, a novel prior knowledge-augmented unsupervised shapelet learning method is proposed to discover abnormal and meaningful working condition through interpretable subsequences. A prior feature extracting module is proposed to change prior knowledge into a recognizable form for the data model. The prior knowledge contains abnormal working condition information. The knowledge-augmented clustering module can learn informative shapelets which stand for abnormal working condition by combining prior features with data features. Furthermore, the preference of prior knowledge and data are self-adjusted in the learning phase. Numerical test results on the real-world aluminum electrolysis process, simulated Tennessee Eastman process, and continuous stirred tank heater process verify the superior performances of the proposed method. The proposed method provides a new perspective for the fusion of prior knowledge and data model. It also provides a new way to solve the problem of abnormal unknown working condition discovery in industrial process.

Manifold-constrained trace ratio optimization for nonstationary process performance monitoring

Data-driven control performance monitoring (CPM) is becoming increasingly important in assessing and diagnosing changes in intricate industrial processes that are challenging to investigate with precise prior knowledge. The existing data-driven CPM methods mostly set covariance or other autocorrelation indexes intended to reflect the global deviation from benchmarks and subsequently diagnose the loops or variables responsible for these changes. However, as these methods assume stationary sequences, only steady-state fluctuation is assessed and diagnosed. This limits the application to industrial processes where transient processes are negligible. A comprehensive performance index integrating the transient performance and the steady-state performance is defined in this paper. The manifold constraint is investigated to mine the transient features so that it can be merged into the variance-based assessment framework. Moreover, to address the issue of information redundancy in non-orthogonal directions, which is present in most existing methods, we proposed a trace ratio-driven framework for enhancing the accuracy of assessment and diagnosis. A numerical example and a simulated industrial cascaded continuous stirred tank heater process are used to test the assessment results and demonstrate the effectiveness of the proposed diagnosis strategy.

Semi-supervised Discriminative Projective Dictionary Pair Learning and Its Application to Industrial Process

Industrial process data have the characteristics of less label, multimode, high dimension, containing noise, and mixing with outliers, which increase the difficulty of mode identification and anomaly detection in process monitoring using limited labeled data. In this article, to address the effect of these adverse factors, a semi-supervised discriminative projective dictionary pair learning (SSDP-DPL) is proposed for industrial process monitoring. First, a semi-supervised dictionary pair learning framework with a class estimation regularization term is developed to address the problem of lacking labeled data. Based on unlabeled data and their reconstruction errors, the class estimation regularization term is designed to obtain a discriminative extended synthetical dictionary, mining the hidden discriminative information in unlabeled data and reducing the impact of incorrect class estimation. Second, a sparse constraint is added to the analytical dictionary to enhance the robustness of projective dictionary pair. Then, a class discriminative function term is designed to improve the discrimination of dictionary pair, which ensures the intraclass compactness and interclass separation of coding space. Finally, the synthetical dictionary, the analytical dictionary, and the control threshold are obtained by iterating the dictionary pair to a process monitoring model for anomaly detection and mode identification. The effectiveness of the proposed process monitoring method is verified by the continuous stirred tank heater process and Tennessee Eastman benchmark tests. The proposed method is then applied to a real-world aluminum electrolysis industrial process. Experimental results demonstrate the superiority of our SSDP-DPL in contrast to other existing methods.

Online reconstruction and diagnosibility analysis of multiplicative fault models for process-related faults

Multiplicative faults generally refer to the change of process parameters or structures which are well-suited to represent the process-related anomalies. Unlike sensor faults and external disturbances that are added into process observations and independent with process states, process-related faults directly influence process states such that it is more challenge to reconstruct and diagnose. To address the process-related fault diagnosis, an online fault reconstruction method based on the multiplicative fault model is proposed with the commonly used multivariate statistical process monitoring framework. The fault reconstruction strategy based on the multiplicative fault representation is given by minimizing reconstruction errors. The diagnosability of the proposed reconstruction method is guaranteed for the change of a single parameter, also known as a unidimensional fault. Moreover, the reconstruction-based contribution is derived for providing heuristic references when diagnosing multidimensional faults. Experiments on a numerical example and a simulated continuous stirred tank heater process benchmark are carried out to investigate the effectiveness of the proposed method. The results show that this method can accurately diagnose the faulty variable or loop and further reconstruct the faulty samples into normal ones.

Locality‐preserving data modelling and its application in fault classification

AbstractIn this work, a new local fault classification technique named neighbourhood‐preserving discriminant analysis (NPDA) was developed and applied for fault classification in industrial processes. Rather than using traditional global modelling methods, we used the neighbourhood‐preserving embedding (NPE) algorithm, which describes data features stably and reliably. Taking the latent variables obtained by NPE and Fisher discriminant analysis (FDA) as the classification features, a classification model was constructed by NPDA and used to classify various types of faults. To evaluate fault classification, case studies on a continuous stirred tank heater process and a Tennessee Eastman benchmark process were conducted. The effectiveness of the proposed approach was confirmed, as improved classification results were obtained by NPDA.

Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis

This paper considers the problem of detecting and isolating incipient sensor fault in multimode processes. A data-driven multimode process monitoring method called conditionally independent Bayesian learning based recursive transformed component statistical analysis (CIBL-RTCSA) is presented. Considering the strong assumption of conditional independence in naïve Bayes, orthogonal transformation is applied to measured variables to improve the extent of conditional independence in different operating modes. The Bayes-based mode identification is adopted for transformed data, and a multiple RTCSA model with a window-switching scheme is developed for monitoring multimode processes. With the orthogonal transformation, the accuracy of mode identification can be effectively improved compared with naïve Bayes. In addition, the fault detection and isolation performance of the proposed method outperforms traditional monitoring methods. The effectiveness of the proposed method is demonstrated by a numerical example and the simulation on a continuous stirred tank heater process.

Fault diagnosis for processes with feedback control loops by shifted output sampling approach

Data-driven fault diagnosis of closed loop processes has been a challenge in the process control community. The issue of the interaction between the process model and the controller model exists in models directly identified from closed loop data, because for all the measured process outputs, no matter whether they are normal or faulty, they are fed back into the controllers so that the reconstruction-based contribution (RBC) as the fault diagnosis method has a severe fault smearing effect. This article proposes a novel sampling scheme which can significantly eliminate the adverse effect of modeling issues in feedback control. The identifiability condition of model parameters is satisfied in the new sampling framework so that the RBC recovers its efficiency even though the process runs under feedback control. Two benchmarks, a continuous stirred-tank heater process and the Tennessee Eastman challenge problem, are used to test the efficiency of the proposed method.

Fuzzy Observer-Based Fault Detection Design Approach for Nonlinear Processes

This paper is concerned with the analysis and integrated design of a type of observer-based fault detection (FD) system for general nonlinear processes. To this end, the existence and design condition for this type of nonlinear observer-based FD systems is first introduced. In this context, the integrated design scheme is investigated by dealing with the design condition with the aid of Takagi–Sugeno (T–S) fuzzy dynamic modeling technique. To be specific, the universal T–S fuzzy observer-based residual generator is developed via fuzzy Lyapunov functions. Subsequently, an integrated observer-based FD scheme is proposed with an embedded dynamic threshold, which is generated to meet the real-time FD requirements from industrial processes. In the end, a numerical example and case simulation study on a continuous stirred tank heater process are performed to show the application of the proposed method.

Design and Implementation of Lifecycle Management for Industrial Control Applications

This paper presents an integrated methodology to manage the lifecycle of industrial control applications, which covers the adaptive estimation and iterative optimization of process parameters, as well as the needs for fault diagnosis and continuous monitoring. More specifically, the design philosophy behind this scheme starts with the parameterization of all stabilizing controllers in the residual generator-based form, and then proceeds with the formation of the expandable controller architecture with an integrated residual access. To further implement the methodology, control system parameters are classified functionally with different importance and preemptive priorities. In the end, some experimental studies and comparative evaluation are conducted in the laboratory continuous stirred tank heater process to verify the effectiveness of the proposed scheme.

Data‐driven design of two‐degree‐of‐freedom controllers using reinforcement learning techniques

Motivated by the successful application for feedback control, this study extends the study of reinforcement learning techniques to the design of two-degree-of-freedom controllers in the data-driven environment. Based on the residual generator based form of Youla parameterisation, all stabilising controllers are first interpreted in the feedback–feedforward situation with a Kalman filter-based residual generator acting as the core part. For the reference tracking problem, further discussions are conducted from the regulatory perspective and using the Q learning, recursive least squares methods and the policy iteration algorithm. The entire design is carried out as a two-stage process that separately achieves the optimal feedback and feedforward controllers. Finally, the effectiveness of the proposed approach is demonstrated with its application in the laboratory continuous stirred tank heater process.

Dynamic mixture probabilistic PCA classifier modeling and application for fault classification

A dynamic classifier based on the mixture probabilistic principal component analyzer (MPPCA) is proposed for fault classification. Compared with traditional methods, both fault detection and diagnosis are combined into a single classification task. By introducing a state indicator, the conventional MPPCA model is first designed as a standard classifier. Then, the static MPPCA model based classifier is temporally extended to the dynamic form within the hidden Markov model framework. Both static and dynamic MPPCA classifiers are obtained by using the Expectation‐Maximization algorithm. For performance evaluation, case studies of the continuous stirred tank heater process and the Tennessee Eastman process are carried out. Results indicate that the dynamic MPPCA classifier performs better compared with the static MPPCA classifier and the hidden Markov model based classifier. Copyright © 2015 John Wiley & Sons, Ltd.

Fault detection and isolation in hybrid process systems using a combined data‐driven and observer‐design methodology

A combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014