IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0

The manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents the guidelines for the implementation of scalable, flexible and pluggable data analysis and real-time supervision systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage through the integration of a Cyber-Physical System at the edge with cloud computing. It combines distributed data acquisition, machine learning and run-time reasoning to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.info:eu-repo/semantics/publishedVersio

Modelling of reliable service based operations support system (MORSBOSS)

Philosophiae Doctor - PhDThe underlying theme of this thesis is identification, classification, detection and prediction of cellular network faults using state of the art technologies, methods and algorithms

Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context,

A problem faced by some Logistic Support Organisations (LSOs) is that of forecasting the demand for spare parts, corresponding to equipment failures within the system. Here we are particularly concerned with a final phase of operations and the opportunity to place only a single order to cover demand during this phase. The problem is further complicated when the service logistics context can change during this final phase, e.g. as the number of systems supported or the LSO's resources change. Such a problem is typical of the final phase of many military operations. The LSO operates the recovery and repair loop for the equipment in question. By developing a simulation of the LSO, we can generate synthetic operational data regarding equipment breakdowns, etc. We then split that data into a training set and a test set in order to compare several approaches to forecasting demand in the final operational phase. We are particularly interested in the application of Bayesian network models for this type of forecasting since these offer a way of combining hard observational data with subjective expert opinion. Different LSO configurations were simulated to create a test dataset and the simulation results were compared with the various forecasts. The BN that learned from training data performed best, followed by a hybrid BN design combining expert elicitation and machine learning, and then a logistic regression model. An expert-adjusted exponential smoothing model was the poorest performer and these differences were statistically significant. The paper concludes with a discussion of the results, some implications for practice and suggestions for future work

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Automated network optimisation using data mining as support for economic decision systems

The evolution from wired voice communications to wireless and cloud computing services has led to the rapid growth of wireless communication companies attempting to meet consumer needs. While these companies have generally been able to achieve quality of service (QoS) high enough to meet most consumer demands, the recent growth in data hungry services in addition to wireless voice communication, has placed significant stress on the infrastructure and begun to translate into increased QoS issues. As a result, wireless providers are finding difficulty to meet demand and dealing with an overwhelming volume of mobile data. Many telecommunication service providers have turned to data analytics techniques to discover hidden insights for fraud detection, customer churn detection and credit risk analysis. However, most are illequipped to prioritise expansion decisions and optimise network faults and costs to ensure customer satisfaction and optimal profitability. The contribution of this thesis in the decision-making process is significant as it initially proposes a network optimisation scheme using data mining algorithms to develop a monitoring framework capable of troubleshooting network faults while optimising costs based on financial evaluations. All the data mining experiments contribute to the development of a super–framework that has been tested using real-data to demonstrate that data mining techniques play a crucial role in the prediction of network optimisation actions. Finally, the insights extracted from the super-framework demonstrate that machine learning mechanisms can draw out promising solutions for network optimisation decisions, customer segmentation, customers churn prediction and also in revenue management. The outputs of the thesis seek to help wireless providers to determine the QoS factors that should be addressed for an efficient network optimisation plan and also presents the academic contribution of this research

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

The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents

Technology and its innovative applications make life easier and reduce the workload on seafarers. Today's ship bridges have much more modern and integrated navigation systems than before, and the ship's handling and management have become much easier. However, nonconformities encountered in the use of technological devices may cause accidents. In this study, the effect of human factor related errors associated with the use of the bridge's electronic navigational devices on grounding and collision-contact accidents was investigated. Non-conformities obtained from 175 collision-contact and 115 grounding accident reports were qualitatively analysed by means of human factor analysis and a classification system. Afterwards, relationships between nonconformities and their probabilities were evaluated quantitatively via a Bayesian network method. As a result of the study, the accident network was revealed. This accident network summarizes how operating errors in the use of technological equipment cause accidents. Recommendations on the prevention of accidents caused by operating errors associated with the use of new technologies are finally given

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii