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Author: Nur Afifah Hassan Publisher: ISBN: Category : Principal components analysis Languages : en Pages : 62
Book Description
This research is about enhancement of PCA-based fault detection system through utilizing dissimilarity matrix. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional PCA-based MSPC is no longer effective because it only valid for the linear relationships between measured variables. Due in order to solve this problem, the technique of dissimilarity matrix is used in multivariate statistical process control as alternative technique which models the non-linear process and can improve the process monitoring performance. The conventional PCA system was run and the dissimilarity system was developed and lastly the monitoring performance in each technique were compared and analysed to achieve aims of this research. This research is to be done by using Matlab software. The findings of this study are illustrated in the form of Hotelling's T2 and Squared Prediction Errors (SPE) monitoring statistics to be analysed. As a conclusion, the dissimilarity system is comparable to the conventional method. Thus can be the other alternative ways in the process monitoring performance. Finally, it is recommended to use data from other chemical processing systems for more concrete justification of the new technique.
Author: Nur Afifah Hassan Publisher: ISBN: Category : Principal components analysis Languages : en Pages : 62
Book Description
This research is about enhancement of PCA-based fault detection system through utilizing dissimilarity matrix. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional PCA-based MSPC is no longer effective because it only valid for the linear relationships between measured variables. Due in order to solve this problem, the technique of dissimilarity matrix is used in multivariate statistical process control as alternative technique which models the non-linear process and can improve the process monitoring performance. The conventional PCA system was run and the dissimilarity system was developed and lastly the monitoring performance in each technique were compared and analysed to achieve aims of this research. This research is to be done by using Matlab software. The findings of this study are illustrated in the form of Hotelling's T2 and Squared Prediction Errors (SPE) monitoring statistics to be analysed. As a conclusion, the dissimilarity system is comparable to the conventional method. Thus can be the other alternative ways in the process monitoring performance. Finally, it is recommended to use data from other chemical processing systems for more concrete justification of the new technique.
Author: Siti Nur Liyana Ahamd Publisher: ISBN: Category : Multivariate analysis Languages : en Pages : 50
Book Description
Nowadays, the production based on chemical process was rapidly expanding either domestically or internationally. To produce the maximum amount of consistently high quality products as per requested and specified by the customers, the whole process must be considering included fault detection. This is to ensure that product quality is achieved and at the same time to ensure that the quality variables are operated under the normal operation. There were several methods that commonly used to detect the fault in process monitoring such as using SPC or MSPC. However because of the MSPC can operated with multivariable continuous processes with collinearities among process variables, this technique was used widely in industry. In MSPC have a few methods that were proposed to improve the fault detection such as PCA, PARAFAC, multidimensional scaling technique, partial least squares, KPCA, NLPCA, MPCA and others. Here, in this thesis was to proposed new technique which was by implementing PCA-based fault detection system based on selected imported variables for continuous-based process. This technique was selected depends on the highest number of magnitude of correlation of variables using Matlab Software. The result in this thesis was the fault can be detected using only selected important variables in the process.
Author: Mohamad Yusup Abd Wahab Publisher: ISBN: Category : Multivariate analysis Languages : en Pages : 53
Book Description
Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis. Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. The correlation coefficient is determined using multivariate analysis techniques, namely Principal Component Analysis (PCA). In order to overcome these problems, the objective of this research is to develop new approaches, which can improve the performance of the present conventional MSPC methods. The new approaches have been developed, the Outline Analysis Approach for examining the distribution of Principal Component Analysis (PCA) scores, the Correlation Coefficient Approach for detecting changes in the correlation structure within the variables. This research proposed PCA Outline Analysis Control Chart for fault detection. The result from the conventional method and ne approach were compared based on their accuracy and sensitivity. Based on the results of the study, the new approaches generally performed better compared to the conventional approaches, particularly the PCA Outline Analysis Control Chart.
Author: Nurul Fadhilah Roslan Publisher: ISBN: Category : Multivariate analysis Languages : en Pages : 61
Book Description
Multivariate statistical techniques are used to develop detection methodology for abnormal process behavior and diagnosis of disturbance which causing poor process performance (Raich and Cinar, 2004). Hence, this study is about the development of principal component analysis (PCA) -based fault detection system based on various modes of normal operating condition (NOC) models for continuous-based process. Detecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product quality variations (Raich and Cinar,2004). The scope of the proposed study is to run traditionally multivariate statistical process monitoring (MSPM) by defining mode difference in variance for continuous-based process. The methodology use to identify and detection of fault which undergo two phase which phase I is off-line monitoring while phase II is on-line monitoring. As a result, it will be analyze and compared of the implementing traditional PCA of Single NOC modes and Multiple NOC modes. Particularly, this study is critically concerned more on the performance during the fault detection operations comprising both off-line and on-line applications, hence it will analyze until fault detection and comparing between two modes of NOC data.
Author: Abdalhamid Ahmad Rahoma Publisher: ISBN: Category : Languages : en Pages :
Book Description
Data based methods are widely used in process industries for fault detection and diagnosis. Among the data-based methods multivariate statistical methods, for example, Principal Component Analysis (PCA), Projection to Latent Squares (PLS), and Independent Component Analysis (ICA) are most widely used methods. These methods in general are successful in detecting process fault, however, diagnosis of the root cause is always not very accurate. The primary goal of the thesis is to improve the fault diagnosis ability of PCA based methods. In PCA, each Principal Component (PC) is a linear combination of all the variables, therefore makes it difficult to identify the root cause from the violation of a PC. Sparse Principal Component Analysis (SPCA) is one version of PCA that gets a sparse description of the PCA loading matrix making it more suitable for fault diagnosis. The present research aims to devise novel strategies to find the sparse description of loading matrix, more aligned with process fault detection and diagnosis. The thesis also looks into improving the fault diagnosis of PCA using clustering methods. The entire thesis can be divided into three major tasks. First, a novel fault detection and diagnosis method is proposed based on the Sparse Principal Component Analysis (SPCA) approach. This approach incorporates a new criterion based on the Fault Detection Rates (FDRs) and False Alarm Rates (FARs) into Zou et al.'s (2006) SPCA algorithms. The objective here is to find appropriate the (Number of Non-Zero Loadings) NNZLs for SPCs that can result in low FARs and high FDRs. A comparison between PCA and four SPCA-based methods for FDD using a continuous stirred tank heater (CSTH) as a benchmark system is also carried out. The results indicate that shortcomings of the PCA can be overcome using the Sparse Principal Component Analysis (SPCA) that uses the novel NNZL criterion. The FDR-FAR SPCA approach gives the highest FDRs for the SPE statistic (93.8%). The second task focuses on developing statistical methods to decide on the non-zero elements of the loading elements of SPCA. Rather than using heuristics, the proposed methods use the distribution of the loading elements to decide if an element should be set to zero. Two SPCA algorithms are proposed to find the NNZL and its position of each PC. The first algorithm is based on bootstrapping of the data, and the second approach is based Iterative Principal Component Analysis (IPCA). The proposed methods are implemented on a CSTH process to test the performance with PCA- and other SPCA-based methods for fault detection and diagnosis. The results reveal that the approaches have superior performance in fault detection, as well as diagnosis of the root cause of fault. Both the Bootstrap-SPCA and Sparse-IPCA methods give the highest FDRs for fault 1 for the SPE statistic (99.3% and 95.76%, respectively) As the third task, this research combines the clustering (k-means) algorithm and PCA algorithm to improve the detection and diagnosis of the fault. PCA has the advantage of detecting the fault without the need for data labelling, while clustering is able to distinguish data from different fault groups into separate clusters. By combining these two algorithms we are able to have better detection and diagnosis of fault and eliminate the need for data labelling. The performance of the proposed method is demonstrated in simulated and large-scale industrial case studies.
Author: L.H. Chiang Publisher: Springer Science & Business Media ISBN: 1447103475 Category : Technology & Engineering Languages : en Pages : 281
Book Description
Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.
Author: Steven X. Ding Publisher: Springer Science & Business Media ISBN: 1447164105 Category : Technology & Engineering Languages : en Pages : 306
Book Description
Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background.
Author: Rolf Isermann Publisher: Springer Science & Business Media ISBN: 3540303685 Category : Technology & Engineering Languages : en Pages : 478
Book Description
With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.
Author: Rolf Isermann Publisher: Springer Science & Business Media ISBN: 3642127673 Category : Technology & Engineering Languages : en Pages : 358
Book Description
Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book “Fault-Diagnosis Systems” published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.
Author: Nur Afifah Hassan
Author: Nur Afifah Hassan
Author: Siti Nur Liyana Ahamd
Author: Mohamad Yusup Abd Wahab
Author: Nurul Fadhilah Roslan
Author: Abdalhamid Ahmad Rahoma
Author: Christopher S. Arnold
Author: L.H. Chiang
Author: Steven X. Ding
Author: Rolf Isermann
Author: Rolf Isermann