12,189 research outputs found
Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems
The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine
Fault detection and diagnosis based on extensions of PCA
The paper presents two approaches for fault detection and discrimination based on principal component analysis (PCA). The first approach proposes the concept of y-indices through a transposed formulation of the data matrices utilized in traditional PCA. Residual errors (REs) and faulty sensor identification indices (FSIIs) are introduced in the second approach, where REs are generated from the residual sub-space of PCA, and FSIIs are introduced to classify sensor- or component-faults. Through field data from gas turbines during commissioning, it is shown that in-operation sensor faults can be detected, and sensor- and component-faults can be discriminated through the proposed methods. The techniques are generic, and will find use in many military systems with complex, safety critical control and sensor arrangements
Fault detection in operating helicopter drive train components based on support vector data description
The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of
mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed
Wind turbine condition monitoring strategy through multiway PCA and multivariate inference
This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated
as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is
performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide
range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version
Multivariate Statistical Process Control Charts: An Overview
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
FAULT DIAGNOSIS TOOLS IN MULTIVARIATE STATISTICAL PROCESS AND QUALITY CONTROL
[EN] An accurate fault diagnosis of both, faults sensors and real process faults have become more and more important for process monitoring (minimize downtime, increase safety of plant operation and reduce the manufacturing cost). Quick and correct fault diagnosis is required in order to put back on track our processes or products before safety or quality can be compromised. In the study and comparison of the fault diagnosis methodologies, this thesis distinguishes between two different scenarios, methods for multivariate statistical quality control (MSQC) and methods for latent-based multivariate statistical process control: (Lb-MSPC). In the first part of the thesis the state of the art on fault diagnosis and identification (FDI) is introduced. The second part of the thesis is devoted to the fault diagnosis in multivariate statistical quality control (MSQC). The rationale of the most extended methods for fault diagnosis in supervised scenarios, the requirements for their implementation, their strong points and their drawbacks and relationships are discussed. The performance of the methods is compared using different performance indices in two different process data sets and simulations. New variants and methods to improve the diagnosis performance in MSQC are also proposed. The third part of the thesis is devoted to the fault diagnosis in latent-based multivariate statistical process control (Lb-MSPC). The rationale of the most extended methods for fault diagnosis in supervised Lb-MSPC is described and one of our proposals, the Fingerprints contribution plots (FCP) is introduced. Finally the thesis presents and compare the performance results of these diagnosis methods in Lb-MSPC. The diagnosis results in two process data sets are compared using a new strategy based in the use of the overall sensitivity and specificity[ES] La realización de un diagnóstico preciso de los fallos, tanto si se trata de fallos de sensores como si se trata de fallos de procesos, ha llegado a ser algo de vital importancia en la monitorización de procesos (reduce las paradas de planta, incrementa la seguridad de la operación en planta y reduce los costes de producción). Se requieren diagnósticos rápidos y correctos si se quiere poder recuperar los procesos o productos antes de que la seguridad o la calidad de los mismos se pueda ver comprometida. En el estudio de las diferentes metodologías para el diagnóstico de fallos esta tesis distingue dos escenarios diferentes, métodos para el control de estadístico multivariante de la calidad (MSQC) y métodos para el control estadístico de procesos basados en el uso de variables latentes (Lb-MSPC). En la primera parte de esta tesis se introduce el estado del arte sobre el diagnóstico e identificación de fallos (FDI). La segunda parte de la tesis está centrada en el estudio del diagnóstico de fallos en control estadístico multivariante de la calidad. Se describen los fundamentos de los métodos más extendidos para el diagnóstico en escenarios supervisados, sus requerimientos para su implementación sus puntos fuertes y débiles y sus posibles relaciones. Los resultados de diagnóstico de los métodos es comparado usando diferentes índices sobre los datos procedentes de dos procesos reales y de diferentes simulaciones. En la tesis se proponen nuevas variantes que tratan de mejorar los resultados obtenidos en MSQC. La tercera parte de la tesis está dedicada al diagnóstico de fallos en control estadístico multivariante de procesos basados en el uso de modelos de variables latentes (Lb-MSPC). Se describe los fundamentos de los métodos mas extendidos en el diagnóstico de fallos en Lb-MSPC supervisado y se introduce una de nuestras propuestas, el fingerprint contribution plot (FCP). Finalmente la tesis presenta y compara los resultados de diagnóstico de los métodos propuestos en Lb-MSPC. Los resultados son comparados sobre los datos de dos procesos usando una nueva estrategia basada en el uso de la sensitividad y especificidad promedia.[CA] La realització d'un diagnòstic precís de les fallades, tant si es tracta de fallades de sensors com si es tracta de fallades de processos, ha arribat a ser de vital importància en la monitorització de processos (reduïx les parades de planta, incrementa la seguretat de l'operació en planta i reduïx els costos de producció) . Es requerixen diagnòstics ràpids i correctes si es vol poder recuperar els processos o productes abans de que la seguretat o la qualitat dels mateixos es puga veure compromesa. En l'estudi de les diferents metodologies per al diagnòstic de fallades esta tesi distingix dos escenaris diferents, mètodes per al control estadístic multivariant de la qualitat (MSQC) i l mètodes per al control estadístic de processos basats en l'ús de variables latents (Lb-MSPC). En la primera part d'esta tesi s'introduïx l'estat de l'art sobre el diagnòstic i identificació de fallades (FDI). La segona part de la tesi està centrada en l'estudi del diagnòstic de fallades en control estadístic multivariant de la qualitat. Es descriuen els fonaments dels mètodes més estesos per al diagnòstic en escenaris supervisats, els seus requeriments per a la seua implementació els seus punts forts i febles i les seues possibles relacions. Els resultats de diagnòstic dels mètodes és comparat utilitzant diferents índexs sobre les dades procedents de dos processos reals i de diferents simulacions. En la tesi es proposen noves variants que tracten de millorar els resultats obtinguts en MSQC. La tercera part de la tesi està dedicada al diagnòstic de fallades en control estadístic multivariant de processos basat en l'ús de models de variables latents (Lb-MSPC). Es descriu els fonaments dels mètodes més estesos en el diagnòstic de fallades en MSPC supervisat i s'introdueix una nova proposta, el fingerprint contribution plot (FCP). Finalment la tesi presenta i compara els resultats de diagnòstic dels mètodes proposats en MSPC. Els resultats són comparats sobre les dades de dos processos utilitzant una nova estratègia basada en l'ús de la sensibilitat i especificitat mitjana.Vidal Puig, S. (2016). FAULT DIAGNOSIS TOOLS IN MULTIVARIATE STATISTICAL PROCESS AND QUALITY CONTROL [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61292TESI
Process Monitoring and Data Mining with Chemical Process Historical Databases
Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events
Deviation contribution plots of multivariate statistics
As data analytic techniques evolve and the accessibility of process measurements improves, data-driven process monitoring has enjoyed a quick development in both theoretical and application perspectives recently. Although abundant process measurements will facilitate data-driven process monitoring and lead to better monitoring indices, it becomes difficult to identify the underlying variables that are responsible for a fault directly with the monitoring indices as the scope of measured variables is getting broader. To restrain the scope and identify the source of fault, contribution plots are commonly used in fault diagnosis in order to quantify the influence of process variables in presence of fault. Nevertheless, as sophisticated monitoring techniques become more and more complicated, deriving corresponding contribution plots is challenging. The concept of deviation contribution plots is proposed to address this issue. By extending the original definition of contribution for linear processes, the deviation contribution is defined to quantify the contribution of deviations in originally measured variables to the deviation of monitoring indices. The ability of proposed deviation contribution plots to identify influential variables in monitoring algorithms based on nonlinear feature extractions is verified by both numerical simulation and the Tennessee Eastman Process benchmark case study
MVBatch: A matlab toolbox for batch process modeling and monitoring
[EN] A novel user-friendly graphical interface for process understanding, monitoring and troubleshooting has been developed as a freely available MATLAB toolbox, called the MultiVariate Batch (MVBatch) Toolbox. The main contribution of this software package is the integration of recent developments in Principal Component Analysis (PCA) based Batch Multivariate Statistical Process Monitoring (BMSPM) that overcome modeling problems such as missing data, different speed of process evolution and length of batch trajectories, and multiple stages. An interactive user interface is provided, which aims to guide users in handling batch data through the main BMSPM steps: data alignment, data modeling, and the development of monitoring schemes. In addition, a small-scale non-linear dynamic simulator of the fermentation process of the Saccharomyces cerevisiae cultivation is available to generate realistic batch data under normal and abnormal operating conditions. This generator of synthetic data can be used for teaching purposes or as a benchmark to illustrate and compare the performance of new methods with sound techniques published in the field of BMSPM.This work is partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through the projects DPI2017-82896-C2-1-R and TIN2017-83494-R. Authors also acknowledge the volunteers to test MVBatch and report their impressions for this software tutorial.González Martínez, JM.; Camacho Paez, J.; Ferrer, A. (2018). MVBatch: A matlab toolbox for batch process modeling and monitoring. Chemometrics and Intelligent Laboratory Systems. 183:122-133. https://doi.org/10.1016/j.chemolab.2018.11.001S12213318
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