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Fault Detection In Wind Turbines Using Pca And Statistical Hypothesis Testing
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Book Synopsis Fault Detection in Wind Turbines Using PCA and Statistical Hypothesis Testing by : Josep Ma Serrahima de Cambra
Download or read book Fault Detection in Wind Turbines Using PCA and Statistical Hypothesis Testing written by Josep Ma Serrahima de Cambra and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: L'augment de la mida dels aerogeneradors per la generació d'electricitat i la seva construcció en llocs remots per maximitzar la producció suposa un augment en costos de manteniment i operació. Per tal de reduir aquests costos, eliminar manteniments programats i millorar la seguretat, apareix la necessitat de sistemes de control a distància. Structural health monitoring és el procés d'implantació d'una estratègia de detecció de fallades a l'estructura. Aplicat als aerogeneradors, fins i tot en condicions de vent canviants és necessària la detecció de dany. La primera part del projecte millora una metodologia prèviament aplicada als aerogeneradors (inferència estadística simple) reduint el temps de detecció; el segon mètode aplicat utilitza la inferència múltiple per detectar dany. Ambdós mètodes són provats per 24 mostres d'aerogeneradors en diferents condicions (sanes i danyades), i els resultats són encoratjadors: utilitzant la inferència simple el temps de detecció és reduït fins obtenir una detecció gairebé instantània; alhora, aquest projecte serveix com a prova pilot amb la inferència múltiple utilitzada per la detecció de dany en aerogeneradors, amb una correcta diagnosis d'estructures sanes i danyades.
Book Synopsis Fault Detection and Isolation in Wind Turbines Using PCA and Statistical Hypothesis Testing by : Ricard Ollé Navarro
Download or read book Fault Detection and Isolation in Wind Turbines Using PCA and Statistical Hypothesis Testing written by Ricard Ollé Navarro and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Aquest projecte té l'objectiu de demostrar l'efectivitat de dues estratègies de detecció de dany en l'estructura d'un aerogenerador, basades en l'obtenció d'un patró de referencia a través de l'anàlisi de components principals (PCA) en l'estructura sana de l'aparell. Quan recollim les dades de l'estructura de la qual en volem comprovar la seva integritat, les projectem sobre el patró i mitjançant dos tipus de test d'hipòtesis diferents –inferència estadística univariable i multivariable-, en podem definir el seu diagnòstic. També té l'objectiu d'utilitzar les dues estratègies per intentar, no només saber si l'estructura està danyada o no, sinó que també detectar quin tipus de dany l'afecta. Per tal de verificar el correcte funcionament dels plans de detecció de dany, analitzarem dades provinents d'estructures afectades per diferents tipus de dany que han estat generades a partir d'un simulador (FAST software). D'aquesta manera, podrem distingir si som capaços de diferenciar les dades d'un aerogenerador sa, de les d'un danyat.
Book Synopsis Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques by : Mohamed N. Nounou
Download or read book Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques written by Mohamed N. Nounou and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advances in Principal Component Analysis by : Ganesh R. Naik
Download or read book Advances in Principal Component Analysis written by Ganesh R. Naik and published by Springer. This book was released on 2017-12-11 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.
Download or read book Wind Turbines written by Frede Blaabjerg and published by MDPI. This book was released on 2019-01-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Wind Turbines" that was published in Energies
Book Synopsis Condition Monitoring of Wind Turbine Structures Through Univariate and Multivariate Hypothesis Testing by : Francesc Pozo
Download or read book Condition Monitoring of Wind Turbine Structures Through Univariate and Multivariate Hypothesis Testing written by Francesc Pozo and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This chapter presents a fault detection method through uni- and multivariate hypothesis testing for wind turbine (WT) faults. A data-driven approach is used based on supervisory control and data acquisition (SCADA) data. First, using a healthy WT data set, a model is constructed through multiway principal component analysis (MPCA). Afterward, given a WT to be diagnosed, its data are projected into the MPCA model space. Since the turbulent wind is a random process, the dynamic response of the WT can be considered as a stochastic process, and thus, the acquired SCADA measurements are treated as a random process. The objective is to determine whether the distribution of the multivariate random samples that are obtained from the WT to be diagnosed (healthy or not) is related to the distribution of the baseline. To this end, a test for the equality of population means is performed in both the univariate and the multivariate cases. Ultimately, the test results establish whether the WT is healthy or faulty. The performance of the proposed method is validated using an advanced benchmark that comprehends a 5-MW WT subject to various actuators and sensor faults of different types.
Book Synopsis Structural Health Monitoring from Sensing to Processing by : Magd Abdel Wahab
Download or read book Structural Health Monitoring from Sensing to Processing written by Magd Abdel Wahab and published by BoD – Books on Demand. This book was released on 2018-09-26 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural health monitoring (SHM) has attracted more attention during the last few decades in many engineering fields with the main aim of avoiding structural disastrous events. This aim is achieved by using advanced sensing techniques and further data processing. SHM has experienced booming advancements during recent years due to the developments in sensing techniques. The reliable operation of current, sophisticated, man-made structures drives the development of incipient reliable damage diagnosis and assessment. This book aims to illustrate the background and applications of SHM from both sensing and processing approaches. Its main objective is to summarize the advantages and disadvantages of SHM methodologies and their applications, which may provide a new perspective in understanding SHM for readers from diverse engineering fields.
Book Synopsis Fault Detection & Prediction in Wind Turbines by : Herp Jürgen
Download or read book Fault Detection & Prediction in Wind Turbines written by Herp Jürgen and published by . This book was released on 2016 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model by : Ian Kuiler
Download or read book Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model written by Ian Kuiler and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Vigilant fault diagnosis and preventive maintenance has the potential to significantly decrease costs associated with wind generators. As wind energy continues the upward growth in technology and continued worldwide adoption and implementation, the application of fault diagnosis techniques will become more imperative. Fault diagnosis and preventive maintenance techniques for wind turbine generators are still at an early stage compared to matured strategies used for generators in conventional power plants. The cost of wind energy can be further reduced if failures are predicted in advance of a major structural failure, which leads to less unplanned maintenance. High maintenance cost of wind turbines means that predictive strategies like fault diagnosis and preventive maintenance techniques are necessary to manage life cycle costs of critical components. Squirrel-Cage Induction Generators (SCIG) are the prevailing generator type and are more robust and cheaper to manufacturer compared to other generator types used in wind turbines. A statistical model was developed using SCADA data to estimate the relationships between winding temperatures and other variables. Predicting faults in stator windings are challenging because the unhealthy condition rapidly evolves into a functional failure.
Book Synopsis Fault Detection and Isolation of Wind Turbines Using Immune System Inspired Algorithms by : Esmaeil AliZadeh
Download or read book Fault Detection and Isolation of Wind Turbines Using Immune System Inspired Algorithms written by Esmaeil AliZadeh and published by . This book was released on 2017 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, the research focus on renewable sources of energy has been growing intensively. This is mainly due to potential depletion of fossil fuels and its associated environmental concerns, such as pollution and greenhouse gas emissions. Wind energy is one of the fastest growing sources of renewable energy, and policy makers in both developing and developed countries have built their vision on future energy supply based on and by emphasizing the wind power. The increase in the number of wind turbines, as well as their size, have led to undeniable care and attention to health and condition monitoring as well as fault diagnosis of wind turbine systems and their components.In this thesis, two main immune inspired algorithms are used to perform Fault Detection and Isolation (FDI) of a Wind Turbine (WT), namely the Negative Selection Algorithm (NSA) as well as the Dendritic Cell Algorithm (DCA).First, an NSA-based fault diagnosis methodology is proposed in which a hierarchical bank of NSAs is used to detect and isolate both individual as well as simultaneously occurring faults common to the wind turbines. A smoothing moving window filter is then utilized to further improve the reliability and performance of the proposed FDI scheme. Moreover, the performance of the proposed scheme is compared with the state-of-the-art data-driven technique, namely Support Vector Machine (SVM) to demonstrate and illustrate the superiority and advantages of the proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate the proposed methodology with that of the SVM under various fault severities.In the second part, another immune inspired methodology, namely the Dendritic Cell Algorithm (DCA) is used to perform online sensor fault FDI. A noise filter is also designed to attenuate the measurement noise, resulting in better FDI results. The proposed DCA-based FDI scheme is then compared with the previously developed NSA-based FDI scheme, and a nonparametric statistical comparison test is also performed.Both of the proposed immune inspired frameworks are applied to a well-known wind turbine benchmark model in order to validate the effectiveness of the proposed methodologies.
Book Synopsis Fault Detection in Structures (wind Turbine) Through Statistical Techniques, Singular Spectrum Analysis (SSA) and Frequency-based Methods by : Victor Verdezoto Pereira
Download or read book Fault Detection in Structures (wind Turbine) Through Statistical Techniques, Singular Spectrum Analysis (SSA) and Frequency-based Methods written by Victor Verdezoto Pereira and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This master thesis aims to demonstrate the effectiveness of a fault detection strategy in the structure of the wind turbine through a new strategy involving singular spectrum analysis (SSA), statistical methods and methods based on frequency. Based on a dynamic model of wind turbine, the time series behavior was obtained in the first instance without presenting any system failure and a second instance of system failure. From these series we can intervene with SSA and with statistical methods (variance, means, covariance, Fisher criteria) to design the fault detection system. The baseline will be designed with 850 healthy samples and a total sampling time of 6.25 seconds. This baseline which will provide us with the components to be compared so that the system can detect various faults that occur (fault types: fixed value, gain factor, offset and dynamics changed) in an efficient way. The results show that a sensor fault system with a high percentage of effectiveness was designed. The greater or lesser effectiveness thereof will depend on the established base line and the components used.
Book Synopsis Fault Detection and Diagnosis Via Improved Statistical Process Control by : Noorlisa Harun
Download or read book Fault Detection and Diagnosis Via Improved Statistical Process Control written by Noorlisa Harun and published by LAP Lambert Academic Publishing. This book was released on 2012-07 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis (FDD). 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. Multivariate analysis technique i.e Principal Component Analysis, PCA and Partial Correlation Analysis, PCorrA are utilized to determine the correlation coefficient between quality variables and process variables. A precut multicomponent distillation column that has been installed with controllers is used as the study unit operation. Improved SPC method is implemented to detect and diagnose various kinds of faults, which occur in the process. Individual charting technique such as Shewhart, Exponential Weight Moving Average (EWMA) and Moving Average and Moving Range (MAMR) charts are used to facilitate the FDD.
Book Synopsis Observability and Economic Aspects of Fault Detection and Diagnosis Using CUSUM Based Multivariate Statistics by : Mohamed Bin Shams
Download or read book Observability and Economic Aspects of Fault Detection and Diagnosis Using CUSUM Based Multivariate Statistics written by Mohamed Bin Shams and published by . This book was released on 2010 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project focuses on the fault observability problem and its impact on plant performance and profitability. The study has been conducted along two main directions. First, a technique has been developed to detect and diagnose faulty situations that could not be observed by previously reported methods. The technique is demonstrated through a subset of faults typically considered for the Tennessee Eastman Process (TEP); which have been found unobservable in all previous studies. The proposed strategy combines the cumulative sum (CUSUM) of the process measurements with Principal Component Analysis (PCA). The CUSUM is used to enhance faults under conditions of small fault/signal to noise ratio while the use of PCA facilitates the filtering of noise in the presence of highly correlated data. Multivariate indices, namely, T2 and Q statistics based on the cumulative sums of all available measurements were used for observing these faults. The ARLo.c was proposed as a statistical metric to quantify fault observability. Following the faults detection, the problem of fault isolation is treated. It is shown that for the particular faults considered in the TEP problem, the contribution plots are not able to properly isolate the faults under consideration. This motivates the use of the CUSUM based PCA technique previously used for detection, for unambiguously diagnose the faults. The diagnosis scheme is performed by constructing a family of CUSUM based PCA models corresponding to each fault and then testing whether the statistical thresholds related to a particular faulty model is exceeded or not, hence, indicating occurrence or absence of the corresponding fault. Although the CUSUM based techniques were found successful in detecting abnormal situations as well as isolating the faults, long time intervals were required for both detection and diagnosis. The potential economic impact of these resulting delays motivates the second main objective of this project. More specifically, a methodology to quantify the potential economical loss due to unobserved faults when standard statistical monitoring charts are used is developed. Since most of the chemical and petrochemical plants are operated under closed loop scheme, the interaction of the control is also explicitly considered. An optimization problem is formulated to search for the optimal tradeoff between fault observability and closed loop performance. This optimization problem is solved in the frequency domain by using approximate closed loop transfer function models and in the time domain using a simulation based approach. The optimization in the time domain is applied to the TEP to solve for the optimal tuning parameters of the controllers that minimize an economic cost of the process.
Book Synopsis Statistical Modeling and Sensor Fault Diagnosis Using PCA by : Edward Y. Bai
Download or read book Statistical Modeling and Sensor Fault Diagnosis Using PCA written by Edward Y. Bai and published by . This book was released on 2005 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fault Detection and Root Cause Diagnosis Using Sparse Principal Component Analysis (SPCA). by : Abdalhamid Ahmad Rahoma
Download or read book Fault Detection and Root Cause Diagnosis Using Sparse Principal Component Analysis (SPCA). written by Abdalhamid Ahmad Rahoma and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Investigation of Model-based Sensor Fault Detection for Wind Turbines Using a Single Sensor by : Fatima Azzahra El Azzouzi
Download or read book Investigation of Model-based Sensor Fault Detection for Wind Turbines Using a Single Sensor written by Fatima Azzahra El Azzouzi and published by . This book was released on 2012 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Structural Control and Fault Detection of Wind Turbine Systems by : Hamid Reza Karimi
Download or read book Structural Control and Fault Detection of Wind Turbine Systems written by Hamid Reza Karimi and published by . This book was released on 2018 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid growth of wind energy worldwide, challenges in the operation and control of wind turbine systems are becoming increasingly important. These affect all parts of the system, and require an integrated approach to optimize safety, cost, integrity and survivability of the system, while retaining the desired performance quality. This book conveys up to date theoretical and practical techniques applicable to the control of wind turbine systems. Topics covered include wave loads on monopole-supported offshore wind turbines; numerical and experimental tools for small wind turbine load analysis; structural control concepts for load reduction of offshore wind turbines; towards farm-level health management of wind turbine systems; data-based approaches to the monitoring of wind turbines; fault diagnostics for electrically operated pitch systems in offshore wind turbines; an emulator prototype design for vibration control of magnetic bearings for wind turbine power generator shafts; condition monitoring and diagnostics of wind turbine power trains; and robust fuzzy fault tolerant control wind energy systems subject to actuator and sensor faults.
