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

Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in Singapore and focused on the RP of four current travel modes and the SP with autonomous vehicles (AV) as the one new travel mode in addition to those in RP. We found that MTLDNNs consistently outperform six benchmark models and particularly the classical NL models by about 5% prediction accuracy in both RP and SP datasets. This performance improvement can be mainly attributed to the soft constraints specific to MTLDNNs, including its innovative architectural design and regularization methods, but not much to the generic capacity of automatic feature learning endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs are also interpretable. The empirical results show that AV is mainly the substitute of driving and AV alternative-specific variables are more important than the socio-economic variables in determining AV adoption. Overall, this study introduces a new MTLDNN framework to combine RP and SP, and demonstrates its theoretical flexibility and empirical power for prediction and interpretation. Future studies can design new MTLDNN architectures to reflect the speciality of RP and SP and extend this work to other behavioral analysis

Rule-based integrated building management systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants

A neural network and rule based system application in water demand forecasting

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation

A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity

Author name used in this publication: Chun-Tian Cheng2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe