Monitoring, fault detection and estimation in processes using multivariate statistical

Multivariate statistical techniques are one of the most widely used approaches in data driven monitoring and fault detection schemes in industrial processes. Concretely, principal component analysis (PCA) has been applied to many complex systems with good results. Nevertheless, the PCA-based fault detection and isolation approaches present some problems in the monitoring of processes with different operating modes and in the identification of the fault root in the fault isolation phase. PCA uses historical databases to build empirical models. The models obtained are able to describe the system¿s trend. PCA models extract useful information from the historical data. This extraction is based on the calculation of the relationship between the measured variables. When a fault appears, it can change the covariance structure captured, and this situation can be detected using different control charts. Another widely used multivariate statistical technique is partial least squares regression (PLS). PLS has also been applied as a data driven fault detection and isolation method. Moreover, this type of methods has been used as estimation techniques in soft sensor design. PLS is a regression method based on principal components. The main goal of this Thesis deals with the monitoring, fault detection and isolation and estimation methods in processes based on multivariate statistical techniques such as principal component analysis and partial least squares. The main contributions of this work can be arranged in the three following topics: ¿ The first topic is related with the monitoring of continuous processes. When a process operates in several operating modes, the classical PCA approach is not the most suitable method. In this work, an approach for monitoring the whole behaviour of a process, taking into account the different operating modes and transient states, is presented. The monitoring of transient states and start-ups is studied in detail. Also, the continuous processes which do not operate in a strict steady state are monitored in a similar way to the transient states. ¿ The second topic is related with the combination of model-based structural model decomposition techniques and principal component analysis. Concretely, the possible conflicts (PCs) approach is applied. PCs compute subsystems within a system model as minimal subsets of equations with an analytical redundancy property to detect and isolate faults. The residuals obtained with this method can be useful to perform a complete fault isolation procedure. These residuals are monitored using a PCA model in order to simplify and improve the fault detection task. ¿ The third topic addresses the estimation task in soft sensor design. In this case, the soft sensors of a real process are studied and improved using neural networks and multivariate statistical techniques. In this case, a dry substance (DS) content sensor based on indirect measurements is replaced by a neural network-based sensor. This type of sensors take into account more variables of the process and obtain more robust and accurate estimations. Moreover, this sensor can be improved using a PCA layer at the network input in order to reduce the number of inputs in the network. Also, a PLS-based sensor is designed in this topic. It also improves the sensor based on indirect measurements. Finally, the different approaches developed in this work have been applied to several process plants. Concretely, a two-communicated tanks system, the evaporation section of a sugar factory and a reverse osmosis desalination plant are the systems used in this dissertationDepartamento de Ingenieria de Sistemas y Automátic

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Statistical process monitoring of a multiphase flow facility

Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

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

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

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