Fault Detection Based on Canonical Correlation Analysis with Rank Constrained Optimization

A Data-Driven Fault Detection and Diagnosis by NSGAII-t-SNE and Clustering Methods in the Chemical Process Industry

Data-driven approaches are widely applied for fault detection in industrial process. Recently, a new method for fault detection called principal component pursuit(PCP) is introduced. PCP is not only robust to outliers, but also can accomplish the objectives of model building, fault detection, fault isolation and process reconstruction simultaneously. PCP divides the data matrix into two parts: a fault-free low rank matrix and a sparse matrix with sensor noise and process fault. The statistics presented in this paper fully utilize the information in data matrix. Since the low rank matrix in PCP is similar to principal components matrix in PCA, a T2 statistic is proposed for fault detection in low rank matrix. And this statistic can illustrate that PCP is more sensitive to small variations in variables than PCA. In addition, in sparse matrix, a new monitored statistic performing the online fault detection with PCP-based method is introduced. This statistic uses the mean and the correlation coefficient of variables. Monte Carlo simulation and Tennessee Eastman (TE) benchmark process are provided to illustrate the effectiveness of monitored statistics.

Industrial processes generally have various operation modes, and fault detection for such processes is important. This paper proposes a method that integrates a variational Bayesian Gaussian mixture model with canonical correlation analysis (VBGMM-CCA) for efficient multimode process monitoring. The proposed VBGMM-CCA method maximizes the advantage of VBGMM in automatic mode identification and the superiority of CCA in local fault detection. First, VBGMM is applied to unlabeled historical process data to determine the number of operation modes and cluster the data in each mode. Second, local CCA models that explore input and output relationships are established. Fault detection residuals are generated in each local CCA model, and monitoring statistics are derived. Finally, a Bayesian inference probability index that integrates monitoring results from all local models is developed to increase the monitoring robustness. The effectiveness of the proposed monitoring scheme is verified through experimental studies on a numerical example and the multiphase batch-fed penicillin fermentation process. Note to Practitioners —Process monitoring is important in guaranteeing process safety and improving product quality. Large amounts of unlabeled process data with multiple operation modes generally exist in industrial processes. Labeling these data is difficult or costly. Hence, this paper presents a VBGMM-CCA method for monitoring multimode processes. The key advantage of the proposed method is that it automatically identifies the number of operation modes in historical data and clusters the data. Then, local CCA monitors are established to model the process input and output relationships. During online monitoring, the running-on operation mode is identified through a density function, and the process status is evaluated by the corresponding CCA monitor. A probabilistic monitoring index is also developed to increase the robustness of the monitoring. In comparison with the results of conventional methods, the monitoring results of the proposed approach are more reliable and informative because the process status and the type of the detected fault are presented.

Principal component analysis (PCA) is a linear data analysis technique widely used for fault detection and isolation, data modeling, and noise filtration. PCA may be combined with statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique in order to detect faults. GLR functions by using the concept of maximum likelihood estimation (MLE) in order to maximize the detection rate for a fixed false alarm rate. The benchmark Tennessee Eastman Process (TEP) is used to examine the performance of the different techniques, and the results show that for processes that experience both shifts in the mean and/or variance, the best performance is achieved by independently monitoring the mean and variance using two separate GLR charts, rather than simultaneously monitoring them using a single chart. Moreover, single-valued data can be aggregated into interval form in order to provide a more robust model with improved fault detection performance using PCA and GLR. The TEP example is used once more in order to demonstrate the effectiveness of using of interval-valued data over single-valued data.

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

In order to improve the performance of canonical correlation analysis (CCA) based methods for process monitoring, this brief proposes a novel process monitoring approach using the structured joint sparse canonical correlation analysis (SJSCCA). Technically, the graph Laplacian could incorporate structured variable correlation information and the joint sparsity could discard useless variables. The developed two-stage alternating direction method of multipliers is shown to be very efficient because each derived subproblem has a closed-form solution or can be solved by fast solvers. In order to detect abnormal situations, $T^{2}$ test statistic is adopted. The validity of SJSCCA is illustrated by the benchmark Tennessee Eastman process. The achieved results show that the proposed SJSCCA is able to improve the monitoring performance significantly in comparison with the existing state-of-the-art CCA-based methods.

Quality-related fault detection for dynamic process based on quality-driven long short-term memory network and autoencoder

Switching LDS-based approach for process fault detection and classification

In recent years, partial least squares (PLS) has become more and more closely linked with the monitoring of key performance indicators (KPI) in large-scale industrial processes. However, the standard PLS and its modifications still have some problems during process monitoring, because these kinds of methods cannot fully utilize the correlation between variables. Instead of using the traditional KPI-related PLS, this paper uses canonical correlation analysis (CCA) to exploit the best correlation among variables. The proposed method decomposes process data into KPI-related and KPI-unrelated parts, and then constructs statistics for process monitoring. For short, the proposed method is termed as KPI-CCA. Finally, the effectiveness and advantages of KPI-CCA are validated by a numerical example and the Tennessee Eastman (TE) benchmark process simulation.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis

A distributed alternating optimization approach to canonical correlation analysis based fault detection for dynamic systems.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

A fault diagnosis system is developed by integrating principal component analysis (PCA) with fuzzy logic knowledge-based (FLKB) systems. A PCA model is created using normal state data. Then it is used to project normal and faulty data during the training stage. It evaluates the process variables values and their correlation, allowing fast and reliable fault detection. Once detection is performed, a FLKB system is used to evaluate the contributions of each variable to changes in the process, finding the root causes of the abnormal event detected. A simple methodology to automatically extract compact process information is presented. Then, an optimization algorithm is implemented to improve the isolation performance. The methodology is demonstrated in an academic case study and in the Tennessee Eastman process benchmark.

Monitoring large-scale processes is a crucial task to ensure the safety and reliability of the plants. This paper proposes an approach for decentralized fault detection in large-scale processes. The measured variables of the plant are divided into multiple and possibly overlapping blocks using different techniques based on data. Local monitoring methods are applied in each block using DPCA (Dynamic Principal Component Analysis) model. The local results are then fused by the Bayesian inference strategy. This paper also compares different techniques to decompose the plant looking for the best strategy from the point of view of the fault detection results. The proposed method was applied to the widely used benchmark Tennessee Eastman Process, showing its effectiveness when compared with a centralized method and another decentralized technique.