Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Space engine safety system

A rocket engine safety system was designed to initiate control procedures to minimize damage to the engine or vehicle or test stand in the event of an engine failure. The features and the implementation issues associated with rocket engine safety systems are discussed, as well as the specific concerns of safety systems applied to a space-based engine and long duration space missions. Examples of safety system features and architectures are given, based on recent safety monitoring investigations conducted for the Space Shuttle Main Engine and for future liquid rocket engines. Also, the general design and implementation process for rocket engine safety systems is presented

Opportunities for improving animal welfare in rodent models of epilepsy and seizures

Animal models of epilepsy and seizures, mostly involving mice and rats, are used to understand the pathophysiology of the different forms of epilepsy and their comorbidities, to identify biomarkers, and to discover new antiepileptic drugs and treatments for comorbidities. Such models represent an important area for application of the 3Rs (replacement, reduction and refinement of animal use). This report provides background information and recommendations aimed at minimising pain, suffering and distress in rodent models of epilepsy and seizures in order to improve animal welfare and optimise the quality of studies in this area. The report includes practical guidance on principles of choosing a model, induction procedures, in vivo recordings, perioperative care, welfare assessment, humane endpoints, social housing, environmental enrichment, reporting of studies and data sharing. In addition, some model-specific welfare considerations are discussed, and data gaps and areas for further research are identified. The guidance is based upon a systematic review of the scientific literature, survey of the international epilepsy research community, consultation with veterinarians and animal care and welfare officers, and the expert opinion and practical experience of the members of a Working Group convened by the United Kingdom's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs)

A probabilistic model for information and sensor validation

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model

Development of an Automated Physician Review Classification System: A hybrid Machine Learning Approach

Patients are increasingly turning to physician rating websites to help them make important healthcare decisions, such as selecting primary care doctors, specialists, and supplementary medical care providers. Previous research has identified a variety of topics and themes that emerge on these review platforms. However, there is little or no work that has been done to create an automated classifier that automatically categorizes these reviews into distinct topics after they have been explored in this context. Building such an automated classifier could assist IS developers and other stakeholders in automatically classifying patient reviews and understanding patient needs. Furthermore, using design science research we strategize how such machine learning systems can be built using design guidelines in turn having the potential to be generalized to other specific contextual problem spaces. Our work focuses on laying the foundation to design guidelines that need to be followed while building automated systems in specific contexts

Novel anisotropic continuum-discrete damage model capable of representing localized failure of massive structures. Part II: identification from tests under heterogeneous stress field

In Part I of this paper we have presented a simple model capable of describing the localized failure of a massive structure. In this part, we discuss the identification of the model parameters from two kinds of experiments: a uniaxial tensile test and a three-point bending test. The former is used only for illustration of material parameter response dependence, and we focus mostly upon the latter, discussing the inverse optimization problem for which the specimen is subjected to a heterogeneous stress field.Comment: 18 pages, 12 figures, 6 table

Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner