Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance

Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into decisions on what to do with the equipment. Hidden Markov model (HMM) is a useful framework to probabilistically model the condition of complex engineering systems with partial observability of the underlying states. Condition monitoring and prediction of such type of system requires accurate knowledge of HMM that describes the degradation of such a system with data collected from the sensors mounted on it, as well as understanding of the uncertainty of the HMMs identified from the available data. To that end, this thesis proposes a novel HMM estimation scheme based on the principles of Bayes theorem. The newly proposed Bayesian estimation approach for estimating HMM parameters naturally yields information about model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation process. In addition, a novel condition monitoring scheme based on uncertain HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor manufacturing facility. Portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation process, while the remainder of the data was used for monitoring of machine condition using the uncertain degradation HMMs yielded by Bayesian estimation. Comparison with a traditional signature-based statistical monitoring method showed that the newly proposed approach effectively utilizes the fact that its parameters are uncertain themselves, leading to orders of magnitude fewer false alarms. This methodology is further extended to address the practical issue that maintenance interventions are usually imperfect. We propose both a novel non-ergodic and non-homogeneous HMM that assumes imperfect maintenances and a novel process monitoring method capable of monitoring the hidden states considering model uncertainty. Significant improvement in both the log-likelihood of estimated HMM parameters and monitoring performance were observed, compared to those obtained using degradation HMMs that always assumed perfect maintenance. Finally, behavior of the posterior distribution of parameters of unidirectional non- ergodic HMMs modeling in this thesis for degradation was theoretically analyzed in terms of their evolution as more data become available in the estimation process. The convergence problem is formulated as a Bernstein-von Mises theorem (BvMT), and under certain regularity conditions, the sequence of posterior distributions is proven to converge to a Gaussian distribution with variance matrix being the inverse of the Fisher information matrix. An example of a unidirectional HMM is presented for which the regularity conditions are verified, and illustrations of expected theoretical results are given using simulation. The understanding of such convergence of posterior distributions enables one to determine when Bayesian estimation of degradation HMMs is justified and converges toward true model parameters, as well as how much data one then needs to achieve desired accuracy of the resulting model. Understanding of these issues is of utmost important if HMMs are to be used for degradation modeling and monitoring.Operations Research and Industrial Engineerin

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity

On Bayesian Networks for Structural Health and Condition Monitoring

The first step in data-driven approaches to Structural Health Monitoring (SHM) is that of damage detection. This is a problem that has been well studied in laboratory conditions. Yet, SHM remains an academic topic, not yet widely implemented in industry. One of the main reasons for this is arguably the difficulty in dealing with Environmental and Operational Variabilities (EOVs), which have a tendency to influence damage-sensitive features in ways similar to damage itself. A large number of the methods developed for SHM applications make use of linear Gaussian models for various tasks including dimensionality reduction, density estimation and system identification. A wide range of linear Gaussian models can be formulated as special cases of a general class of probabilistic graphical models, or Bayesian networks. The work presented here discusses how Bayesian networks can be used systematically to approach different types of damage detection problems, through their likelihood function. A likelihood evaluates the probability that an observation belongs to a particular model. If this model correctly captures the undamaged state of the system, then a likelihood can be used as a novelty index, which can point to the presence of damage. Likelihood functions can be systematically exploited for damage detection purposes across the vast range of linear Gaussian models. One of the key benefits of this fact is that simple models can easily be extended to mixtures of linear Gaussian models. It is shown how this approach can be effective in dealing with operational and environmental variabilities. This thesis thus provides a point of view on performing novelty detection under this wide class of models systematically with their likelihood functions. Models that are typically used for other purposes can become powerful novelty detectors in this view. The relationship between Principal Component Analysis (PCA) and Kalman filters is a good example of this. Under the graphical model perspective these two models are a simple variation of each other, where they model data with and without time dependence. Provided these models are trained with representative data from a non-damaged system, their likelihood function presents a useful novelty index. Their limitation to modelling linear Gaussian data can be overcome through the mixture modelling interpretation. Through graphical models, this is a straightforward extension, but one that retains a probabilistic interpretation. The impact of this interpretation is that environmental and operational variability, as well as potential nonlinearity, in SHM features can be captured by these models. Even though the interpretation changes depending on the model, the likelihood function can consistently be used as a damage indicator, throughout models like Gaussian mixtures, PCA, Factor Analysis, Autoregressive models, Kalman filters and switching Kalman filters. The work here focuses around these models. There are various ways in which these models can be used, but here the focus is narrowed to exploring them as novelty detectors, and showing their application in different contexts. The context in this case refers to different types of SHM data and features, as this could be either vibration, acoustics, ultrasound, performance metrics, etc. %The thesis divides into three main sections. The first presents an overview and scope, with introductions to SHM data, machine learning and the use of likelihood functions for novelty detection. This thesis provides a discussion on the theoretical background for probabilistic graphical models, or Bayesian networks. Separate chapters are dedicated to the discussion of Bayesian networks to model static and dynamic data (with and without temporal dependencies, respectively). Furthermore, three different application examples are presented to demonstrate the use of likelihood function inference for damage detection. These systems are a simulated mass-spring-damper system, with varying stiffness in its non-damaged condition, and with a cubic spring nonlinearity. This system presents a challenge from the point of view of the characterisation of the changing environment in terms of global stiffness and excitation energy. It is shown how mixtures of PCA models can be used to tackle this problem if frequency domain features are used, and mixtures of linear dynamical systems (Kalman filters) can be used to successfully characterise the baseline undamaged system and to identify the presence of damage directly from time domain measurements. Another case study involves the detection of damage on the Z-24 bridge. This is a well-studied problem in SHM research, and it is of interest due to the nonlinear stiffness effect due to temperature changes. The features used here are the first four natural frequencies of the bridge. It is demonstrated how a Gaussian mixture model can characterise the undamaged condition, and its likelihood is able to accurately predict the presence of damage. The third case study involves the prediction of various stages of damage on a wind turbine bearing. This is an experimental laboratory investigation - and the problem is also tackled with a Gaussian mixture model. This problem is of interest because the lowest damage level seeded in the bearing was subsurface yield. This is of great relevance to the wind turbine community, as detecting this level of damage is currently not feasible. Features from Acoustic Emission (AE) measurements were used to train a Gaussian mixture model. It is shown that the likelihood function of this model can correctly predict the presence of damage

Learning and Using Multimodal Stochastic Models : A Unified Approach

This dissertation presents a principled approach to representing and using instance-based knowledge. Perceptions and actions are probabilistically modelled in a unified structure which allows for simultaneous perception modelling and reasoning about desired actions. In particular, a new method for online instance-based learning of such models is presented and analyzed. This method, called Dynamic Gaussian Mixture Estimation (DGME), adapts a model's complexity to the process being modelled. The models produced by DGME are evaluated on several classification, prediction, and control applications, and its characteristics are compared with other state-of-the-art methods. In the context of control applications, an additional novel method, Gaussian Mixture Control (GMC), is introduced for precisely controlling systems that exhibit multimodality