Process fault prediction and prognosis based on a hybrid technique

The present study introduces a novel hybrid methodology for fault detection and diagnosis (FDD) and fault prediction and prognosis (FPP). The hybrid methodology combines both data-driven and process knowledge driven techniques. The Hidden Markov Model (HMM) and the auxiliary codes detect and predict the abnormalities based on process history while the Bayesian Network (BN) diagnoses the root cause of the fault based on process knowledge. In the first step, the system performance is evaluated for fault detection and diagnosis and in the second step, prediction and prognosis are evaluated. In both cases, an HMM trained with Normal Operating Condition data is used to determine the log-likelihoods (LL) of each process history data string. It is then used to develop the Conditional Probability Tables of BN while the structure of BN is developed based on process knowledge. Abnormal behaviour of the system is identified through HMM. The time of detection of an abnormality, respective LL value, and the probabilities of being in the process condition at the time of detection are used to generate the likelihood evidence to BN. The updated BN is then used to diagnose the root cause by considering the respective changes of the probabilities. Performance of the new technique is validated with published data of Tennessee Eastman Process. Eight of the ten selected faults were successfully detected and diagnosed. The same set of faults were predicted and prognosed accurately at different levels of maximum added noise

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Development of an advanced artificial intelligent reliability analysis tool to enhance ship operations and maintenance activities

No Abstract availableNo Abstract availabl

Predictive Modeling for Intelligent Maintenance in Complex Semiconductor Manufacturing Processes.

Semiconductor fabrication is one of the most complicated manufacturing processes, in which the current prevailing maintenance practices are preventive maintenance, using either time-based or wafer-based scheduling strategies, which may lead to the tools being either “over-maintained” or “under-maintained”. In literature, there rarely exists condition-based maintenance, which utilizes machine conditions to schedule maintenance, and almost no truly predictive maintenance that assesses remaining useful lives of machines and plans maintenance actions proactively. The research presented in this thesis is aimed at developing predictive modeling methods for intelligent maintenance in semiconductor manufacturing processes, using the in-process tool performance as well as the product quality information. In order to achieve an improved maintenance decision-making, a method for integrating data from different domains to predict process yield is proposed. The self-organizing maps have been utilized to discretize continuous data into discrete values, which will tremendously reduce the computational cost of Bayesian network learning process that can discover the stochastic dependences among process parameters and product quality. This method enables one to make more proactive product quality prediction that is different from traditional methods based on solely inspection results. Furthermore, a method of using observable process information to estimate stratified tool degradation levels has been proposed. Single hidden Markov model (HMM) has been employed to represent the tool degradation process under a single recipe; and the concatenation of multiple HMMs can be used to model the tool degradation under multiple recipes. To validate the proposed method, a simulation study has been conducted, which shows that HMMs are able to model the stratified unobservable degradation process under variable operating conditions. This method enables one to estimate the condition of in-chamber particle contamination so that maintenance actions can be initiated accordingly. With these two novel methods, a methodological framework to perform better maintenance in complex manufacturing processes is established. The simulation study shows that the maintenance cost can be reduced by performing predictive maintenance properly while highest possible yield is retained. This framework provides a possibility of using abundant equipment monitoring data and product quality information to coordinate maintenance actions in a complex manufacturing environment.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58530/1/yangliu_1.pd

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available