Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Sapienz: Multi-objective automated testing for android applications

We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1, 000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes

Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data

Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets

Identifying and Detecting Attacks in Industrial Control Systems

The integrity of industrial control systems (ICS) found in utilities, oil and natural gas pipelines, manufacturing plants and transportation is critical to national wellbeing and security. Such systems depend on hundreds of field devices to manage and monitor a physical process. Previously, these devices were specific to ICS but they are now being replaced by general purpose computing technologies and, increasingly, these are being augmented with Internet of Things (IoT) nodes. Whilst there are benefits to this approach in terms of cost and flexibility, it has attracted a wider community of adversaries. These include those with significant domain knowledge, such as those responsible for attacks on Iran’s Nuclear Facilities, a Steel Mill in Germany, and Ukraine’s power grid; however, non specialist attackers are becoming increasingly interested in the physical damage it is possible to cause. At the same time, the approach increases the number and range of vulnerabilities to which ICS are subject; regrettably, conventional techniques for analysing such a large attack space are inadequate, a cause of major national concern. In this thesis we introduce a generalisable approach based on evolutionary multiobjective algorithms to assist in identifying vulnerabilities in complex heterogeneous ICS systems. This is both challenging and an area that is currently lacking research. Our approach has been to review the security of currently deployed ICS systems, and then to make use of an internationally recognised ICS simulation testbed for experiments, assuming that the attacking community largely lack specific ICS knowledge. Using the simulator, we identified vulnerabilities in individual components and then made use of these to generate attacks. A defence against these attacks in the form of novel intrusion detection systems were developed, based on a range of machine learning models. Finally, this was further subject to attacks created using the evolutionary multiobjective algorithms, demonstrating, for the first time, the feasibility of creating sophisticated attacks against a well-protected adversary using automated mechanisms

Multiple Objective Fitness Functions for Cognitive Radio Adaptation

This thesis explores genetic algorithm and rule-based optimization techniques used by cognitive radios to make operating parameter decisions. Cognitive radios take advantage of intelligent control methods by using sensed information to determine the optimal set of transmission parameters for a given situation. We have chosen to explore and compare two control methods. A biologically-inspired genetic algorithm (GA) and a rule-based expert system are proposed, analyzed and tested using simulations. We define a common set of eight transmission parameters and six environment parameters used by cognitive radios, and develop a set of preliminary fitness functions that encompass the relationships between a small set of these input and output parameters. Five primary communication objectives are also defined and used in conjunction with the fitness functions to direct the cognitive radio to a solution. These fitness functions are used to implement the two cognitive control methods selected. The hardware resources needed to practically implement each technique are studied. It is observed, through simulations, that several trade offs exist between both the accuracy and speed of the final decision and the size of the parameter sets used to determine the decision. Sensitivity analysis is done on each parameter in order to determine the impact on the decision making process each parameter has on the cognitive engine. This analysis quantifies the usefulness of each parameter

Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection

Combinatorial interaction testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. Simulated Annealing (SA) and Greedy Algorithms have been widely used to find CIT test suites. From the literature, there is a widely-held belief that SA is slower, but produces more effective tests suites than Greedy and that SA cannot scale to higher strength coverage. We evaluated both algorithms on seven real-world subjects for the well-studied two-way up to the rarely-studied six-way interaction strengths. Our findings present evidence to challenge this current orthodoxy: real-world constraints allow SA to achieve higher strengths. Furthermore, there was no evidence that Greedy was less effective (in terms of time to fault revelation) compared to SA; the results for the greedy algorithm are actually slightly superior. However, the results are critically dependent on the approach adopted to constraint handling. Moreover, we have also evaluated a genetic algorithm for constrained CIT test suite generation. This is the first time strengths higher than 3 and constraint handling have been used to evaluate GA. Our results show that GA is competitive only for pairwise testing for subjects with a small number of constraints

Automated Unit Testing of Evolving Software

As software programs evolve, developers need to ensure that new changes do not affect the originally intended functionality of the program. To increase their confidence, developers commonly write unit tests along with the program, and execute them after a change is made. However, manually writing these unit-tests is difficult and time-consuming, and as their number increases, so does the cost of executing and maintaining them. Automated test generation techniques have been proposed in the literature to assist developers in the endeavour of writing these tests. However, it remains an open question how well these tools can help with fault finding in practice, and maintaining these automatically generated tests may require extra effort compared to human written ones. This thesis evaluates the effectiveness of a number of existing automatic unit test generation techniques at detecting real faults, and explores how these techniques can be improved. In particular, we present a novel multi-objective search-based approach for generating tests that reveal changes across two versions of a program. We then investigate whether these tests can be used such that no maintenance effort is necessary. Our results show that overall, state-of-the-art test generation tools can indeed be effective at detecting real faults: collectively, the tools revealed more than half of the bugs we studied. We also show that our proposed alternative technique that is better suited to the problem of revealing changes, can detect more faults, and does so more frequently. However, we also find that for a majority of object-oriented programs, even a random search can achieve good results. Finally, we show that such change-revealing tests can be generated on demand in practice, without requiring them to be maintained over time