Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

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

Time-triggered Runtime Verification of Real-time Embedded Systems

In safety-critical real-time embedded systems, correctness is of primary concern, as even small transient errors may lead to catastrophic consequences. Due to the limitations of well-established methods such as verification and testing, recently runtime verification has emerged as a complementary approach, where a monitor inspects the system to evaluate the specifications at run time. The goal of runtime verification is to monitor the behavior of a system to check its conformance to a set of desirable logical properties. The literature of runtime verification mostly focuses on event-triggered solutions, where a monitor is invoked when a significant event occurs (e.g., change in the value of some variable used by the properties). At invocation, the monitor evaluates the set of properties of the system that are affected by the occurrence of the event. This type of monitor invocation has two main runtime characteristics: (1) jittery runtime overhead, and (2) unpredictable monitor invocations. These characteristics result in transient overload situations and over-provisioning of resources in real-time embedded systems and hence, may result in catastrophic outcomes in safety-critical systems. To circumvent the aforementioned defects in runtime verification, this dissertation introduces a novel time-triggered monitoring approach, where the monitor takes samples from the system with a constant frequency, in order to analyze the system's health. We describe the formal semantics of time-triggered monitoring and discuss how to optimize the sampling period using minimum auxiliary memory and path prediction techniques. Experiments on real-time embedded systems show that our approach introduces bounded overhead, predictable monitoring, less over-provisioning, and effectively reduces the involvement of the monitor at run time by using negligible auxiliary memory. We further advance our time-triggered monitor to component-based multi-core embedded systems by establishing an optimization technique that provides the invocation frequency of the monitors and the mapping of components to cores to minimize monitoring overhead. Lastly, we present RiTHM, a fully automated and open source tool which provides time-triggered runtime verification specifically for real-time embedded systems developed in C

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Continuous Maintenance System for optimal scheduling based on real-time machine monitoring

Nowadays, the maintenance activities are the ones that most draw the attention of companies due to the increased costs of sudden machines stop, and consequently, stop the production processes. These stops are mostly caused by wear-out of its components that lead to machine breakdown and a close monitoring of the manufacturing processes need to be made. Based on this, and to increase the production line efficiency, there's a need to continuously monitor the machines' performance, and together with all the historical maintenance data, create strategies to minimize the maintenance phases and costs. These strategies may lie in the prediction of a suitable time periods to perform maintenance operations, a based on that, group a set of machines together to perform maintenance activities between day-off and day-on shifts. This represents a difficulty mainly because the increased complexity of scheduling and planning activities of a production line, being necessary to minimize the impact of maintenance activities based on failure prediction in all the already existing plan

Unsupervised Methods for Condition-Based Maintenance in Non-Stationary Operating Conditions

Maintenance and operation of modern dynamic engineering systems requires the use of robust maintenance strategies that are reliable under uncertainty. One such strategy is condition-based maintenance (CBM), in which maintenance actions are determined based on the current health of the system. The CBM framework integrates fault detection and forecasting in the form of degradation modeling to provide real-time reliability, as well as valuable insight towards the future health of the system. Coupled with a modern information platform such as Internet-of-Things (IoT), CBM can deliver these critical functionalities at scale. The increasingly complex design and operation of engineering systems has introduced novel problems to CBM. Characteristics of these systems - such as the unavailability of historical data, or highly dynamic operating behaviour - has rendered many existing solutions infeasible. These problems have motivated the development of new and self-sufficient - or in other words - unsupervised CBM solutions. The issue, however, is that many of the necessary methods required by such frameworks have yet to be proposed within the literature. Key gaps pertaining to the lack of suitable unsupervised approaches for the pre-processing of non-stationary vibration signals, parameter estimation for fault detection, and degradation threshold estimation, need to be addressed in order to achieve an effective implementation. The main objective of this thesis is to propose set of three novel approaches to address each of the aforementioned knowledge gaps. A non-parametric pre-processing and spectral analysis approach, termed spectral mean shift clustering (S-MSC) - which applies mean shift clustering (MSC) to the short time Fourier transform (STFT) power spectrum for simultaneous de-noising and extraction of time-varying harmonic components - is proposed for the autonomous analysis of non-stationary vibration signals. A second pre-processing approach, termed Gaussian mixture model operating state decomposition (GMM-OSD) - which uses GMMs to cluster multi-modal vibration signals by their respective, unknown operating states - is proposed to address multi-modal non-stationarity. Applied in conjunction with S-MSC, these two approaches form a robust and unsupervised pre-processing framework tailored to the types of signals found in modern engineering systems. The final approach proposed in this thesis is a degradation detection and fault prediction framework, termed the Bayesian one class support vector machine (B-OCSVM), which tackles the key knowledge gaps pertaining to unsupervised parameter and degradation threshold estimation by re-framing the traditional fault detection and degradation modeling problem as a degradation detection and fault prediction problem. Validation of the three aforementioned approaches is performed across a wide range of machinery vibration data sets and applications, including data obtained from two full-scale field pilots located at Toronto Pearson International Airport. The first of which is located on the gearbox of the LINK Automated People Mover (APM) train at Toronto Pearson International Airport; and, the second which is located on a subset of passenger boarding tunnel pre-conditioned air units (PCA) in Terminal 1 of Pearson airport. Results from validation found that the proposed pre-processing approaches and combined pre-processing framework provides a robust and computationally efficient and robust methodology for the analysis of non-stationary vibration signals in unsupervised CBM. Validation of the B-OCSVM framework showed that the proposed parameter estimation approaches enables the earlier detection of the degradation process compared to existing approaches, and the proposed degradation threshold provides a reasonable estimate of the fault manifestation point. Holistically, the approaches proposed in thesis provide a crucial step forward towards the effective implementation of unsupervised CBM in complex, modern engineering systems

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Shallow neural networks for autonomous robots

The use of Neural Networks (NNs) in modern applications is already well established thanks to the technological advancements in processing units and Deep Learning (DL), as well as the availability of deployment frameworks and services. However, the embedding of these methods in robotic systems is problematic when it comes to field operations. The use of Graphics Processing Units (GPUs) for such networks requires high amounts of power which would lead to shortened operational times. This is not desired since autonomous robots already need to manage their power supply to accommodate the lengths of their missions which can extend from hours to days. While external processing is possible, real-time monitoring can become unfeasible where delays are present. This also applies to autonomous robots that are deployed for underwater or space missions. For these reasons, there is a requirement for shallow but robust NN-based solutions that enhance the autonomy of a robot. This dissertation focuses on the design and meticulous parametrization complemented by methods that explain hyper-parameter importance. This is performed in the context of different settings and problems for autonomous robots in field operations. The contribution of this thesis comes in the form of autonomy augmentation for robots through shallow NNs that can potentially be embedded in future systems carrying NN processing units. This is done by implementing neural architectures that use sensor data to extract representations for event identification and learn patterns for event anticipation. This work harnesses Long Short-Term Memory networks (LSTMs) as the underpinning framework for time series representation and interpretation. This has been tested in three significant problems found in field operations: hardware malfunction classification, survey trajectory classification and hazardous event forecast and detection

Bayesian networks in additive manufacturing and reliability engineering

A Bayesian network (BN) is a powerful tool to represent the quantitative and qualitative features of a system in an intuitive yet sophisticated manner. The qualitative aspect is represented with a directed acyclic graph (DAG), depicting dependency relations between the random variables of the system. In a DAG, the variables of the system are shown with a set of nodes and the dependencies between them are shown with a directed edge. A DAG in the Bayesian network can be a causal graph under certain circumstances. The quantitative aspect is the local conditional probabilities associated with each variable, which is a factorization of the joint probability distribution of the variables in the system based on the dependency relation represented in the DAG. In this study, the benefits of using BNs in reliability engineering and additive manufacturing is investigated. In the case of reliability engineering, there are several methods to create predictive models for reliability features of a system. Predicting the possibility and the time of a possible failure is one of the important tasks in the reliability engineering principle. The quality of the corrective maintenance after each failure is affecting consecutive failure times. If a maintenance task after each failure involves replacing all the components of an equipment, called perfect maintenance, it is considered that the equipment is restored to an “as good as new” (AGAN) condition, and based on that, the consecutive failure times are considered independent. Not only in most of the cases the maintenance is not perfect, but the environment of the equipment and the usage patterns have a significant effect on the consecutive failure times. In this study, this effect is investigated by using Bayesian network structural learning algorithms to learn a BN based on the failure data of an industrial water pump. In additive manufacturing (AM) field, manufacturing systems are normally a complex combination of multiple components. This complex nature and the associated uncertainties in design and manufacturing parameters in additive manufacturing promotes the need for models that can handle uncertainties and are efficient in calculations. Moreover, the lack of AM knowledge in practitioners is one of the main obstacles for democratizing it. In this study, a method is developed for creating Bayesian network models for AM systems that includes experts’ and domain knowledge. To form the structure of the model, causal graphs obtained through dimensional analysis conceptual modeling (DACM) framework is used as the DAG for a Bayesian network after some modifications. DACM is a framework for extracting the causal graph and the governing equations between the variables of a complex system. The experts’ knowledge is extracted through a probability assessment process, called the analytical hierarchy process (AHP) and encoded into local probability tables associated with the independent variables of the model. To complete the model, a sampling technique is used along with the governing equations between the intermediate and output variables to obtain the rest of the probability tables. Such models can be used in many use cases, namely domain knowledge representation, defect prognosis and diagnosis and design space exploration. The qualitative aspect of the model is obtained from the physical phenomena in the system and the quantitative aspect is obtained from the experts’ knowledge, therefore the model can interactively represent the domain and the experts’ knowledge. In prognosis tasks, the probability distribution for the values that an output variable can take is calculated based on the values chosen for the input variables. In diagnosis tasks, the designer can investigate the reason for having a specific value in an output variable among the inputs. Finally, the model can be used to perform design space exploration. The model reduces the design space into a discretized and interactive Bayesian network space which is very convenient for design space exploration

Insights into the function of short interspersed degenerated retroposons in the protozoan parasite Leishmania

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal