Fault detection and diagnosis using principal component analysis. Application to low pressure lost foam casting process

Process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman Process

The fault detection and diagnosis of continuous process is very important for the production safety and product quality. Owing to its no need to know much about the process mechanism and exact process model ,the data driven method, typically the principal component analysis (PCA) has attracted much attention by chemical researchers for monitoring process. PCA is powerful in fault detection ,however, it has difficulties in diagnosing fault correctly in complex process. In this paper, the multiblock principal component analysis (MBPCA) is applied for fault detection and diagnosis in continuous process, which uses the integral PCA to detect fault and block contribution and variables contribution to diagnose fault. The simulations on the Tennessee Eastman process show that the proposed method can not only detect fault quickly ,but also find the fault location exactly.

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Experiences with industrial applications of projection methods for multivariate statistical process control

In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current (HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data, and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error (SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms, without being affected by the sampling frequency.

Principal Component Analysis (PCA) is a commonly employed technique in industrial systems for process monitoring and fault diagnosis, owing to its capability to efficiently process large datasets. Traditionally, it is applied to single-valued variables, where critical information can be lost in real scenarios with data uncertainties. Interval-valued PCA methods like Symbolic Covariance PCA (SCPCA) and Complete Information PCA (CIPCA) have been developed to enhance fault detection by incorporating data uncertainties in the PCA model. This paper presents a novel adaptation of SCPCA for detecting incertain sensor faults, marking the first correct implementation of SCPCA for fault detection and isolation (FDI). It aims to compare the performance of the new SCPCA with that of CIPCA, evaluating its reliability and accuracy in detecting sensor faults in a greenhouse prototype system.

This paper presents a new knowledge discovery assistance method to improve wastewater treatment plant (WWTP) operation based on multivariate statistical process control (MSPC). The proposed method combines with MSPC by principal component analysis (PCA-MSPC) and monitoring of a pre-defined performance index for efficient and stable plant operation. Fault detection and isolation (FDI) related to the performance index is selectively performed by monitoring the time series data of the performance index wherein the sample points violating the control limit of Q statistic or that of T2 statistic in PCA-MSPC are indicated. Hidden patterns of probable cause variables to deteriorate the performance index are discovered from the FDI by observing the time series data of the isolated variables. Applications of the proposed method to real WWTPs illustrate the effectiveness of the proposed method by showing possible improvement for energy-saving operation and stable plant operation.

Principal component analysis (PCA) as a basic technique of multivariate statistical process control (MSPC) was successfully applied to monitor large-scale plants, where it is used for modeling process data. To detect abnormal situations, PCA utilize several types of monitoring statistics, where the most popular is the squared prediction error (SPE). After detection, the root cause of the fault must be identified. For that purpose, many approaches have been proposed and developed in the literature. The aim of this work is to study the ability of static PCA to monitor the predetermined Tennessee Eastman faults where the static PCA model is constructed using Tennessee Eastman process (TEP) training set.

Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes

Both process monitoring and fault isolation are important and challenging tasks for quality control and improvement in high-dimensional processes. Under a practical assumption that not all variables would shift simultaneously, this paper proposes a variable-selection-based multivariate statistical process control (SPC) procedure for process monitoring and fault diagnosis. A forward-selection algorithm is first utilized to screen out potential out-of-control variables; a multivariate control chart is then set up to monitor suspicious variables. Therefore, detection of faulty conditions and isolation of faulty variables can be achieved in one step. Both simulation studies and a real example have shown the effectiveness of the proposed procedure.

Fault detection and diagnosis in process data using one-class support vector machines

Root causality analysis at early abnormal stage using principal component analysis and multivariate Granger causality

Fault detection in linear systems is a fairly matured area where the well-known principal component analysis (PCA) and its variants are widely used. However, a large class of non-linear systems exist, especially chemical processes, on which such techniques cannot be applied. The present work aims at demonstrating the application of self-organising maps (SOM) for fault detection in non-linear processes. SOM belongs to the class of unsupervised and competitive learning algorithms and it is highly capable of handling nonlinear relationships. Application of SOM to fault detection involves generation of a reference template for the process under fault-free conditions. Online fault detection is performed by generating a new template using a windowing of the data, which is compared with the reference template using a novel metric based on the node weights obtained from SOM to detect possible faults in the process. Simulation studies on two non-linear systems, namely, (1) continuously stirred tank reactor (CSTR) and (2) bioreactor process demonstrate the practicality and utility of the proposed method.

Data-driven techniques have been receiving considerable attention in the industrial process monitoring field due to their major advantages of easy implementation and less requirement for the prior knowledge and process mechanism. Principal component analysis (PCA) method is known as a popular method for monitoring and fault detection in industrial systems but as it is basically a linear method. However, most practical systems are nonlinear. To make the extension to nonlinear systems, kernel PCA (KPCA) method has been proposed for process modeling and monitoring. We present in this paper an online reduced rank optimized KPCA (RR-KPCA) technique for fault detection in order to extend the advantages of the KPCA models to online processes. Following the fault detection, the identification of the variables correlated to the fault occurred is of great importance. For this purpose, it is proposed to extend the approaches of localization by partial PCA and by elimination in the linear case to the nonlinear case, by exploiting the solution of reduction of the dimension of the kernel matrix in the feature space. The partial RR-KPCA and the elimination sensor identification (ESI-RRKPCA) are generated based on the static RR-KPCA and the online RR-KPCA methods. The idea of these approaches is to generate partial RR-KPCA models with reduced sets of variables. In other words, their goal is to generate indices of fault detection sensitive to certain faults and insensitive to others. The proposed fault isolation methods are applied for monitoring an air quality monitoring network (AIRLOR) data.

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.