Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Data-driven modelling and monitoring of industrial processes with applications in nuclear waste vitrification

PhD ThesisProcess models are critical for process monitoring, control, and optimisation. With the increasing amount of process data and advancements in computational hardware, data-driven models are a good alternative to mechanistic models, which often have inaccuracies or are too costly to develop. One problem with data-driven models is the difficulty in ensuring that the models perform well on new data and produce accurate predictions in complex situations, which are frequently encountered in the process industry. Within this context, part of this thesis explores developing better data-driven models through using a latent variable technique, known as slow feature analysis, as a pre-processing step to regression. Slow feature analysis extracts slow varying features that contain underlying trends in the data, which can improve model performance through providing more meaningful information to regression, reducing noise, and reducing dimensionality. Firstly, the effectiveness of combining linear slow feature analysis with a neural network is demonstrated on two industrial case studies of soft sensor development and is compared with conventional techniques, such as neural networks and integration of principal component analysis with a neural network. It is shown that integration of slow feature analysis with neural networks can significantly improve model performance. However, linear slow feature analysis can fail to extract the driving forces behind data in nonlinear situations such as batch processes. Therefore, using kernel slow feature analysis with a neural network is proposed to further enhance process model performance. A numerical example was used to demonstrate the effective extraction of driving forces in a nonlinear case where linear slow feature analysis cannot. Model generalisation performance was improved using the proposed method on both this numerical example, and an industrial penicillin process case study. Dealing with radioactive nuclear waste is an important obstacle in nuclear energy. Sellafield Ltd have a nuclear waste vitrification plant which converts high-level nuclear waste into a more stable, lower volume glass form, which is more appropriate for long term storage in sealed containers. This thesis presents three applications of data-driven modelling to this nuclear waste vitrification process. A predictive model of the pour rate of processed nuclear waste into containers, an early detection system for blockages in the dust scrubber, and a model of the long-term chemical durability of the stored glass waste. These applications use the previously developed slow feature analysis methods, as well as other data-driven techniques such as extreme learning machine and bootstrap aggregation, for enhancing the model performance.Engineering and Physical Sciences Research Council (EPSRC) and Sellafield Lt

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Fault Detection, Diagnosis and Fault Tolerance Approaches in Dynamic Systems based on Black-Box Models

In this dissertation new contributions to the research area of fault detection and diagnosis in dynamic systems are presented. The main research effort has been done on the development of new on-line model-based fault detection and diagnosis (FDD) approaches based on blackbox models (linear ARX models, and neural nonlinear ARX models). From a theoretical point of view a white-box model is more desirable to perform the FDD tasks, but in most cases it is very hard, or even impossible, to obtain. When the systems are complex, or difficult to model, modelling based on black-box models is usually a good and often the only alternative. The performance of the system identification methods plays a crucial role in the FDD methods proposed. Great research efforts have been made on the development of linear and nonlinear FDD approaches to detect and diagnose multiplicative (parametric) faults, since most of the past research work has been done focused on additive faults on sensors and actuators. The main pre-requisites for the FDD methods developed are: a) the on-line application in a real-time environment for systems under closed-loop control; b) the algorithms must be implemented in discrete time, and the plants are systems in continuous time; c) a two or three dimensional space for visualization and interpretation of the fault symptoms. An engineering and pragmatic view of FDD approaches has been followed, and some new theoretical contributions are presented in this dissertation. The fault tolerance problem and the fault tolerant control (FTC) have been investigated, and some ideas of the new FDD approaches have been incorporated in the FTC context. One of the main ideas underlying the research done in this work is to detect and diagnose faults occurring in continuous time systems via the analysis of the effect on the parameters of the discrete time black-box ARX models or associated features. In the FDD methods proposed, models for nominal operation and models for each faulty situation are constructed in off-line operation, and used a posteriori in on-line operation. The state of the art and some background concepts used for the research come from many scientific areas. The main concepts related to data mining, multivariate statistics (principal component analysis, PCA), linear and nonlinear dynamic systems, black-box models, system identification, fault detection and diagnosis (FDD), pattern recognition and discriminant analysis, and fault tolerant control (FTC), are briefly described. A sliding window version of the principal components regression algorithm, termed SW-PCR, is proposed for parameter estimation. The sliding window parameter estimation algorithms are most appropriate for fault detection and diagnosis than the recursive algorithms. For linear SISO systems, a new fault detection and diagnosis approach based on dynamic features (static gain and bandwidth) of ARX models is proposed, using a pattern classification approach based on neural nonlinear discriminant analysis (NNLDA). A new approach for fault detection (FDE) is proposed based on the application of the PCA method to the parameter space of ARX models; this allows a dimensional reduction, and the definition of thresholds based on multivariate statistics. This FDE method has been combined with a fault diagnosis (FDG) method based on an influence matrix (IMX). This combined FDD method (PCA & IMX) is suitable to deal with SISO or MIMO linear systems. Most of the research on the fault detection and diagnosis area has been done for linear systems. Few investigations exist in the FDD approaches for nonlinear systems. In this work, two new nonlinear approaches to FDD are proposed that are appropriate to SISO or MISO systems. A new architecture for a neural recurrent output predictor (NROP) is proposed, incorporating an embedded neural parallel model, an external feedback and an adjustable gain (design parameter). A new fault detection and diagnosis (FDD) approach for nonlinear systems is proposed based on a bank of neural recurrent output predictors (NROPs). Each neural NROP predictor is tuned to a specific fault. Also, a new FDD method based on the application of neural nonlinear PCA to ARX model parameters is proposed, combined with a pattern classification approach based on neural nonlinear discriminant analysis. In order to evaluate the performance of the proposed FDD methodologies, many experiments have been done using simulation models and a real setup. All the algorithms have been developed in discrete time, except the process models. The process models considered for the validation and tests of the FDD approaches are: a) a first order linear SISO system; b) a second order SISO model of a DC motor; c) a MIMO system model, the three-tank benchmark. A real nonlinear DC motor setup has also been used. A fault tolerant control (FTC) approach has been proposed to solve the typical reconfiguration problem formulated for the three-tank benchmark. This FTC approach incorporates the FDD method based on a bank of NROP predictors, and on an adaptive optimal linear quadratic Gaussian controller

Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique

A Data-Driven Toolchain for the Operational Performance Analysis and Optimization of Buildings

Automation has proven indispensable to advancing human endeavors. Within the built environment its evolution and sophistication are on the cusp of moving beyond automatic control into automated prediction and diagnosis. A data-driven toolchain is developed so human efforts can be focused on high-value concerns. The research examines smart buildings as a cyberphysical construct and places the Bayesian perspective as paramount. Prior knowledge is leveraged through common building energy modeling and simulation tools, which are utilized and extended. An iterative, three-step process is developed to 1) classify building energy performance scenarios, 2) forecast dynamics over a planning horizon of interest, and 3) signal human decision-makers concerning deviations from ideal behavior. In the classification step, focus is placed on the discrete wavelet transformation of electrical demand profiles, producing energy and entropy feature extraction from the wavelet levels at definitive time frames, and Bayesian probabilistic hierarchical clustering. The process yields a categorized and manageable set of representative electrical demand profiles for smart grid applications. In the forecasting step, a cyclical two-stage model predictive control process of policy planning followed by execution is evaluated. The results show that even the most complicated nonlinear autoregressive neural network with exogenous input does not appear to warrant the additional efforts in forecasting model development and training in comparison to the simpler models. In the signaling step, a simulation study is considered to assess whole-building energy signaling accuracy in the presence of uncertainty and faults at the submetered level, which may lead to tradeoffs at the whole-building level that are not detectable without submetering. Together, the steps form a data-driven toolchain for the operational performance analysis and optimization of buildings

Processing hidden Markov models using recurrent neural networks for biological applications

Philosophiae Doctor - PhDIn this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have been developed by other researchers. However, to the best of our knowledge, no algorithm for processing HMMs through learning has been given. Taking advantage of the structural similarities of the architectural dynamics of the RNNs and HMMs, in this work we analyze the combination of these two systems into the hybrid architecture. To this end, the main objective of this study is to improve the sequence recognition/classi_cation performance by applying a hybrid neural/symbolic approach. In particular, trained HMMs are used as the initial symbolic domain theory and directly encoded into appropriate RNN architecture, meaning that the prior knowledge is processed through the training of RNNs. Proposed algorithm is then implemented on sample test beds and other real time biological applications

Learning Human Behaviour Patterns by Trajectory and Activity Recognition

The world’s population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, relying only on the periodic medical appointments. Therefore, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. The available technologies to monitor human behaviour are limited to indoors and require the installation of sensors around the user’s homes presenting high maintenance and installation costs. With the widespread use of smartphones, it is possible to take advantage of their sensing information to better assist the elderly population. This study investigates the question of what we can learn about human pattern behaviour from this rich and pervasive mobile sensing data. A deployment of a data collection over a period of 6 months was designed to measure three different human routines through human trajectory analysis and activity recognition comprising indoor and outdoor environment. A framework for modelling human behaviour was developed using human motion features, extracted in an unsupervised and supervised manner. The unsupervised feature extraction is able to measure mobility properties such as step length estimation, user points of interest or even locomotion activities inferred from an user-independent trained classifier. The supervised feature extraction was design to be user-dependent as each user may have specific behaviours that are common to his/her routine. The human patterns were modelled through probability density functions and clustering approaches. Using the human learned patterns, inferences about the current human behaviour were continuously quantified by an anomaly detection algorithm, where distance measurements were used to detect significant changes in behaviour. Experimental results demonstrate the effectiveness of the proposed framework that revealed an increase potential to learn behaviour patterns and detect anomalies

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible