FAULT DIAGNOSIS USING SYSTEM IDENTIFICATION FOR CHEMICAL PROCESS PLANT

Fault detection and diagnosis have gained an importance in the automation process industries over the past decade. This is due to several reasons; one of them being that sufficient amount of data is available from the process plants. The goal of this project is to develop such fault diagnosis systems, which use the input-output data of the realm process plant to detect, isolate, and reconstruct faults. The first part of this project focused on developing a different prediction models to the real system. Moreover, a linearized model using Taylor Series Expansion approach and ARX (Autoregressive with external input) model of the real system have been designed. In addition, the most accurate identification model which describes the dynamic behavior of the monitored system has been selected. Furthermore, a technique Statistical Process Control (SPC) used in fault diagnosis. This method depends on central limit theorem and used to detect faults by the analysis of the mismatch between the ARX model estimation and the process plant output. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor) and the results and conclusion have been reported and showed excellent estimation of ARX model and good fault diagnosis performance of SPC

Events Recognition System for Water Treatment Works

The supply of drinking water in sufficient quantity and required quality is a challenging task for water companies. Tackling this task successfully depends largely on ensuring a continuous high quality level of water treatment at Water Treatment Works (WTW). Therefore, processes at WTWs are highly automated and controlled. A reliable and rapid detection of faulty sensor data and failure events at WTWs processes is of prime importance for its efficient and effective operation. Therefore, the vast majority of WTWs operated in the UK make use of event detection systems that automatically generate alarms after the detection of abnormal behaviour on observed signals to ensure an early detection of WTW’s process failures. Event detection systems usually deployed at WTWs apply thresholds to the monitored signals for the recognition of WTW’s faulty processes. The research work described in this thesis investigates new methods for near real-time event detection at WTWs by the implementation of statistical process control and machine learning techniques applied for an automated near real-time recognition of failure events at WTWs processes. The resulting novel Hybrid CUSUM Event Recognition System (HC-ERS) makes use of new online sensor data validation and pre-processing techniques and utilises two distinct detection methodologies: first for fault detection on individual signals and second for the recognition of faulty processes and events at WTWs. The fault detection methodology automatically detects abnormal behaviour of observed water quality parameters in near real-time using the data of the corresponding sensors that is online validated and pre-processed. The methodology utilises CUSUM control charts to predict the presence of faults by tracking the variation of each signal individually to identify abnormal shifts in its mean. The basic CUSUM methodology was refined by investigating optimised interdependent parameters for each signal individually. The combined predictions of CUSUM fault detection on individual signals serves the basis for application of the second event detection methodology. The second event detection methodology automatically identifies faults at WTW’s processes respectively failure events at WTWs in near real-time, utilising the faults detected by CUSUM fault detection on individual signals beforehand. The method applies Random Forest classifiers to predict the presence of an event at WTW’s processes. All methods have been developed to be generic and generalising well across different drinking water treatment processes at WTWs. HC-ERS has proved to be effective in the detection of failure events at WTWs demonstrated by the application on real data of water quality signals with historical events from a UK’s WTWs. The methodology achieved a peak F1 value of 0.84 and generates 0.3 false alarms per week. These results demonstrate the ability of method to automatically and reliably detect failure events at WTW’s processes in near real-time and also show promise for practical application of the HC-ERS in industry. The combination of both methodologies presents a unique contribution to the field of near real-time event detection at WTW

Seleção de variáveis aplicada ao controle estatístico multivariado de processos em bateladas

A presente tese apresenta proposições para o uso da seleção de variáveis no aprimoramento do controle estatístico de processos multivariados (MSPC) em bateladas, a fim de contribuir com a melhoria da qualidade de processos industriais. Dessa forma, os objetivos desta tese são: (i) identificar as limitações encontradas pelos métodos MSPC no monitoramento de processos industriais; (ii) entender como métodos de seleção de variáveis são integrados para promover a melhoria do monitoramento de processos de elevada dimensionalidade; (iii) discutir sobre métodos para alinhamento e sincronização de bateladas aplicados a processos com diferentes durações; (iv) definir o método de alinhamento e sincronização mais adequado para o tratamento de dados de bateladas, visando aprimorar a construção do modelo de monitoramento na Fase I do controle estatístico de processo; (v) propor a seleção de variáveis, com propósito de classificação, prévia à construção das cartas de controle multivariadas (CCM) baseadas na análise de componentes principais (PCA) para monitorar um processo em bateladas; e (vi) validar o desempenho de detecção de falhas da carta de controle multivariada proposta em comparação às cartas tradicionais e baseadas em PCA. O desempenho do método proposto foi avaliado mediante aplicação em um estudo de caso com dados reais de um processo industrial alimentício. Os resultados obtidos demonstraram que a realização de uma seleção de variáveis prévia à construção das CCM contribuiu para reduzir eficientemente o número de variáveis a serem analisadas e superar as limitações encontradas na detecção de falhas quando bancos de elevada dimensionalidade são monitorados. Conclui-se que, ao possibilitar que CCM, amplamente utilizadas no meio industrial, sejam adequadas para banco de dados reais de elevada dimensionalidade, o método proposto agrega inovação à área de monitoramento de processos em bateladas e contribui para a geração de produtos de elevado padrão de qualidade.This dissertation presents propositions for the use of variable selection in the improvement of multivariate statistical process control (MSPC) of batch processes, in order to contribute to the enhacement of industrial processes’ quality. There are six objectives: (i) identify MSPC limitations in industrial processes monitoring; (ii) understand how methods of variable selection are used to improve high dimensional processes monitoring; (iii) discuss about methods for alignment and synchronization of batches with different durations; (iv) define the most adequate alignment and synchronization method for batch data treatment, aiming to improve Phase I of process monitoring; (v) propose variable selection for classification prior to establishing multivariate control charts (MCC) based on principal component analysis (PCA) to monitor a batch process; and (vi) validate fault detection performance of the proposed MCC in comparison with traditional PCA-based and charts. The performance of the proposed method was evaluated in a case study using real data from an industrial food process. Results showed that performing variable selection prior to establishing MCC contributed to efficiently reduce the number of variables and overcome limitations found in fault detection when high dimensional datasets are monitored. We conclude that by improving control charts widely used in industry to accomodate high dimensional datasets the proposed method adds innovation to the area of batch process monitoring and contributes to the generation of high quality standard products

Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Automated fault detection is an important part of a quality control system. It has the potential to increase the overall quality of monitored products and processes. The fault detection of automotive instrument cluster systems in computer- based manufacturing assembly lines is currently limited to simple boundary checking. The analysis of more complex non-linear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. In this paper, a novel approach for automated fault detection and isolation based on deep machine learning techniques is presented. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the proposed approach models the different spatial / temporal patterns found in the data. The approach is also able to successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established fault detection and isolation methods

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Current multivariate control charts for monitoring large scale industrial processes are typically based on latent variable models, such as principal component analysis (PCA) or its dynamic counterpart when variables present auto-correlation (DPCA). In fact, it is usually considered that, under such conditions, DPCA is capable to effectively deal with both the cross- and auto-correlated nature of data. However, it can easily be verified that the resulting monitoring statistics (T2 and Q, also referred by SPE) still present significant auto-correlation. To handle this issue, a set of multivariate statistics based on DPCA and on the generation of decorrelated residuals were developed, that present low auto-correlation levels, and therefore are better positioned to implement SPC in a more consistent and stable way (DPCA-DR). The monitoring performance of these statistics was compared with that from other alternative methodologies for the well-known Tennessee Eastman process benchmark. From this study, we conclude that the proposed statistics had the highest detection rates on 19 out of the 21 faults, and are statistically superior to their PCA and DPCA counterparts. DPCA-DR statistics also presented lower auto-correlation, which simplifies their implementation and improves their reliability

Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization

The goal of this work is to use sensor data for online detection and identification of process anomalies (faults). In pursuit of this goal, we propose Dirichlet process Gaussian mixture (DPGM) models. The proposed DPGM models have two novel outcomes: 1) DP-based statistical process control (SPC) chart for anomaly detection and 2) unsupervised recurrent hierarchical DP clustering model for identification of specific process anomalies. The presented DPGM models are validated using numerical simulation studies as well as wireless vibration signals acquired from an experimental semiconductor chemical mechanical planarization (CMP) test bed. Through these numerically simulated and experimental sensor data, we test the hypotheses that DPGM models have significantly lower detection delays compared with SPC charts in terms of the average run length (ARL1) and higher defect identification accuracies (F-score) than popular clustering techniques, such as mean shift. For instance, the DP-based SPC chart detects pad wear anomaly in CMP within 50 ms, as opposed to over 140 ms with conventional control charts. Likewise, DPGM models are able to classify different anomalies in CMP