Browsing by Author "Neville, Stephen"
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Item Approaches for early fault detection in large scale engineering plants(2017-06-30) Neville, Stephen; Dimopoulos, Nikitas J.In general, it is difficult to automatically detect faults within large scale engineering plants early during their onset. This is due to a number of factors including the large number of components typically present in such plants and the complex interactions of these components, which are typically poorly understood. Traditionally, fault detection within these plants has been performed through the use of status monitoring systems employing limit checking fault detection. In this approach, upper and lower bounds are placed on what is prescribed as “normal” behaviour for each of the plant's collected status data signals and fault flags are generated if and when the given status data signal exceeds either of its bounds. This approach tends to generate relatively large numbers of false alarms, due to the technique's inability to model known signal dependencies, and it also tends to produce inconsistent fault flags, in the sense that the flags do not tend to be produced throughout the “fault” event. The limit checking approach also is not particularly adept at early fault detection tasks since as long as the given status data signal remains between the upper and lower bounds any signal behaviour is deemed as acceptable. Hence, behavioural changes in the status data signals go undetected until their severity is such that either the upper or lower bounds are exceeded. In this dissertation, two novel fault detection methodologies are proposed which are better suited to the early fault detection task than traditional limit checking. The first technique is directed at modeling of signals exhibiting unknown linear dependencies. This detection system utilizes fuzzy membership functions to model signal behaviour and through this modelling approach fault detection bounds are generated which meet a prescribed probability of false alarm rate. The second technique is directed at modelling signals exhibiting unknown non-linear, dynamic dependencies. This system utilizes recurrent neural network technology to model the signal behaviours and prescribed statistical methods are employed to determine appropriate fault detection thresholds. Both of these detection systems have been designed to be able to be retrofitted into existing industrial status monitoring system and, as such, they have been designed to achieve good modelling performance in spite of the coarsely quantized status data signals which are typical of industrial status monitoring systems constructed to employ limit checking. The fault detection properties of the proposed fault detection systems were also compared to an in situ limit checking fault detection system for a set of real-world data obtained from an operational large scale engineering plant. This comparison showed that both of the proposed fault detection systems achieved marked improvements over traditional limit checking both in terms of their false alarm rates and their fault detection sensitivities.Item Dynamic resource allocation in computing clouds through distributed Multiple Criteria Decision Analysis(2010-04-01T00:01:36Z) Yazır, Yağız Onat; Matthews, Chris; Farahbod, Roozbeh; Guitouni, Adel; Neville, Stephen; Ganti, Sudhakar; Coady, YvonneIn computing clouds, it is desirable to avoid wasting resources as a result of under-utilization and to avoid lengthy response times as a result of over-utilization. In this paper, we propose a new approach for dynamic autonomous resource management in computing clouds. The main contribution of this work is two-fold. First, we adopt a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center. Second, the Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method. Simulation results show that the proposed approach is promising in terms of scalability, feasibility and flexibility.Item Quantifying artifacts of virtualization: a framework for mirco-benchmarks (sic)(IEEE, 2009) Matthews, C; Coady, Y; Neville, StephenOne of the novel benefits of virtualization is its ability to emulate many hosts with a single physical machine. This approach is often used to support at-scale testing for large-scale distributed systems. To better understand the precise ways in which virtual machines differ from their physical counterparts, we have started to quantify some of the timing artifacts that appear to be common to two modern approaches to virtualization. Here we present several systematic experiments that highlight four timing artifacts, and begin to decipher their origins within virtual machine implementations. These micro-benchmarks serve as a means to better understand the mappings that exist between virtualized and real-world testing infrastructure. Our goal is to develop a reusable framework for micro-benchmarks that can be customized to quantify artifacts associated with specific cluster configurations and workloads. This type of quantification can then be used to better anticipate behavioral characteristics at-scale in real settings.