Advanced Condition Monitoring of Complex Mechatronics Systems Based on Model-of-Signals and Machine Learning Techniques

Prognostics and Health Management (PHM) of machinery has become one of the pillars of Industry 4.0. The introduction of emerging technologies into the industrial world enables new models, new forms, and new methodologies to transform traditional manufacturing into intelligent manufacturing. In this context, diagnostics and prognostics of faults and their precursors has gained remarkable attention, mainly when performed autonomously by systems. The field is flourishing in academia, and researchers have published numerous PHM methodologies for machinery components. The typical course of actions adopted to execute servicing strategies on machinery components requires significant sensor measurements, suitable data processing algorithms, and appropriate servicing choices. Even though the industrial world is integrating more and more Information Technology solutions to keep up with Industry 4.0 new trends most of the proposed solutions do not consider standard industrial hardware and software. Modern controllers are built based on PCs and workstations hardware architectures, introducing more computational power and resources in production lines that we can take advantage of. This thesis focuses on bridging the gap in PHM between the industry and the research field, starting from Condition Monitoring and its application using modern industrial hardware. The cornerstones of this "bridge" are Model-of-Signals (MoS) and Machine Learning techniques. MoS relies on sensor measurements to estimate machine working condition models. Those models are the result of black-box system identification theory, which provides essential rules and guidelines to calculate them properly. MoS allows the integration of PHM modules into machine controllers, exploiting their edge-computing capabilities, because of the availability of recursive estimation algorithms. Besides, Machine Learning offers the tools to perform a further refinement of the extracted information, refining data for diagnostics, prognostics, and maintenance decision-making, and we show how its integration is possible within the modern automation pyramid

Fault detection for the Benfield process using a closed-loop subspace re-identification approach

Closed-loop system identification and fault detection and isolation are the two fundamental building blocks of process monitoring. Efficient and accurate process monitoring increases plant availability and utilisation. This dissertation investigates a subspace system identification and fault detection methodology for the Benfield process, used by Sasol, Synfuels in Secunda, South Africa, to remove CO2 from CO2-rich tail gas. Subspace identification methods originated between system theory, geometry and numerical linear algebra which makes it a computationally efficient tool to estimate system parameters. Subspace identification methods are classified as Black-Box identification techniques, where it does not rely on a-priori process information and estimates the process model structure and order automatically. Typical subspace identification algorithms use non-parsimonious model formulation, with extra terms in the model that appear to be non-causal (stochastic noise components). These extra terms are included to conveniently perform subspace projection, but are the cause for inflated variance in the estimates, and partially responsible for the loss of closed-loop identifiably. The subspace identification methodology proposed in this dissertation incorporates two successive LQ decompositions to remove stochastic components and obtain state-space models of the plant respectively. The stability of the identified plant is further guaranteed by using the shift invariant property of the extended observability matrix by appending the shifted extended observability matrix by a block of zeros. It is shown that the spectral radius of the identified system matrices all lies within a unit boundary, when the system matrices are derived from the newly appended extended observability matrix. The proposed subspace identification methodology is validated and verified by re-identifying the Benfield process operating in closed-loop, with an RMPCT controller, using measured closed-loop process data. Models that have been identified from data measured from the Benfield process operating in closed-loop with an RMPCT controller produced validation data fits of 65% and higher. From residual analysis results, it was concluded that the proposed subspace identification method produce models that are accurate in predicting future outputs and represent a wide variety of process inputs. A parametric fault detection methodology is proposed that monitors the estimated system parameters as identified from the subspace identification methodology. The fault detection methodology is based on the monitoring of parameter discrepancies, where sporadic parameter deviations will be detected as faults. Extended Kalman filter theory is implemented to estimate system parameters, instead of system states, as new process data becomes readily available. The extended Kalman filter needs accurate initial parameter estimates and is thus periodically updated by the subspace identification methodology, as a new set of more accurate parameters have been identified. The proposed fault detection methodology is validated and verified by monitoring process behaviour of the Benfield process. Faults that were monitored for, and detected include foaming, flooding and sensor faults. Initial process parameters as identified from the subspace method can be tracked efficiently by using an extended Kalman filter. This enables the fault detection methodology to identify process parameter deviations, with a process parameter deviation sensitivity of 2% or higher. This means that a 2% parameter deviation will be detected which greatly enhances the fault detection efficiency and sensitivity.Dissertation (MEng)--University of Pretoria, 2008.Electrical, Electronic and Computer Engineeringunrestricte

Parameter estimation in linear discrete system : new algorithms for stochastic approximation scheme

The application of modern control theory to solve dynamic optimization problem requires that the equation and parameters characterizing the system dynamics be known. This work is devoted to the on-line identification of linear discrete-time systems from noise corrupted input and output data, by the method of stochastic approximation. Criteria have been established on the gain matrix for the convergence of system identification algorithm by stochastic approximation. By minimizing the estimated error at each stage, expressions for the gain sequence namely (a) scalar gain (b) diagonal matrix gain and (c) square matrix gain are developed. A condition has been established under which these gain matrices satisfy the convergence criteria. The basic algorithm suggested in the past was restricted to \u27white\u27 measurement error and further required that the noise variances be known. This thesis extends the algorithms to overcome these limitations. The extensions are based on the following three techniques a) use of Instrumental Variables (Wong and Polak, 1967), b) use of a noise whitening filter (Hasting-James and Sage, 1969), c) subtraction of correlated part of residuals (Talman and Van den Boom, 1973). Finally, the algorithms are extended to multiple input-output systems and time varying systems. The proposed algorithms have been applied to the identification of simulated systems. The convergence,storage and computational requirement have been compared

Simulation and Theory of Large-Scale Cortical Networks

Cerebral cortex is composed of intricate networks of neurons. These neuronal networks are strongly interconnected: every neuron receives, on average, input from thousands or more presynaptic neurons. In fact, to support such a number of connections, a majority of the volume in the cortical gray matter is filled by axons and dendrites. Besides the networks, neurons themselves are also highly complex. They possess an elaborate spatial structure and support various types of active processes and nonlinearities. In the face of such complexity, it seems necessary to abstract away some of the details and to investigate simplified models. In this thesis, such simplified models of neuronal networks are examined on varying levels of abstraction. Neurons are modeled as point neurons, both rate-based and spike-based, and networks are modeled as block-structured random networks. Crucially, on this level of abstraction, the models are still amenable to analytical treatment using the framework of dynamical mean-field theory. The main focus of this thesis is to leverage the analytical tractability of random networks of point neurons in order to relate the network structure, and the neuron parameters, to the dynamics of the neurons—in physics parlance, to bridge across the scales from neurons to networks. More concretely, four different models are investigated: 1) fully connected feedforward networks and vanilla recurrent networks of rate neurons; 2) block-structured networks of rate neurons in continuous time; 3) block-structured networks of spiking neurons; and 4) a multi-scale, data-based network of spiking neurons. We consider the first class of models in the light of Bayesian supervised learning and compute their kernel in the infinite-size limit. In the second class of models, we connect dynamical mean-field theory with large-deviation theory, calculate beyond mean-field fluctuations, and perform parameter inference. For the third class of models, we develop a theory for the autocorrelation time of the neurons. Lastly, we consolidate data across multiple modalities into a layer- and population-resolved model of human cortex and compare its activity with cortical recordings. In two detours from the investigation of these four network models, we examine the distribution of neuron densities in cerebral cortex and present a software toolbox for mean-field analyses of spiking networks