Abnormality Diagnosis Model for Nuclear Power Plants Using Two-Stage Gated Recurrent Units

A nuclear power plant is a large complex system with tens of thousands of components. To ensure plant safety, the early and accurate diagnosis of abnormal situations is an important factor. To prevent misdiagnosis, operating procedures provide the anticipated symptoms of abnormal situations. While the more severe emergency situations total less than ten cases and can be diagnosed by dozens of key plant parameters, abnormal situations on the other hand include hundreds of cases and a multitude of parameters that should be considered for diagnosis. The tasks required of operators to select the appropriate operating procedure by monitoring large amounts of information within a limited amount of time can burden operators. This paper aims to develop a system that can, in a short time and with high accuracy, select the appropriate operating procedure and sub-procedure in an abnormal situation. Correspondingly, the proposed model has two levels of prediction to determine the procedure level and the detailed cause of an event. Simulations were conducted to evaluate the developed model, with results demonstrating high levels of performance. The model is expected to reduce the workload of operators in abnormal situations by providing the appropriate procedure to ultimately improve plant safety. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC

Serial control of phonology in speech production

The aim of this thesis is to further our understanding of the processes which control the sequencing of phonemes as we speak: this is an example of what is commonly known as the serial order problem. Such a process is apparent in normal speech and also from the existence of a class of speech errors known as sound movement errors, where sounds are anticipated (spoken too soon), perseverated (repeated again later), or exchanged (the sounds are transposed). I argue that this process is temporally governed, that is, the serial ordering mechanism is restricted to processing sounds that are close together in time. This is in conflict with frame-based accounts (e.g. Dell, 1986; Lapointe & Dell, 1979), serial buffer accounts (Shattuck-Hufnagel, 1979) and associative chaining theories (Wickelgren, 1969). An analysis of sound movement errors from Harley and MacAndrew's (1995) corpus shows how temporal processing bears on the production of speech sounds by the temporal constraint observed in the pattern of errors, and I suggest an appropriate computational model of this process. Specifically, I show how parallel temporal processing in an oscillator-based model can account for the movement of sounds in speech. Similar predictions were made by the model to the pattern of movement errors actually observed in speech error corpora. This has been demonstrated without recourse to an assumption of frame and slot structures. The OSCillator-based Associative REcall (OSCAR) model, on the other hand, is able to account for these effects and other positional effects, providing support for a temporal based theory of serial control

An investigation into current and vibration signatures of three phase induction motors

This research aimed at investigating the relationship between three phase induction motors vibration (MVS) and current signatures (MCS). This is essential due to the cost of vibration measuring equipment and in cases where vibration of interest point is not accessible; such as electrical submersible pumps (ESP) used in oil industry. A mathematical model was developed to understand the effects of two types of induction motors common faults; rotor bar imperfections and phase imbalance on the motor vibration and current signatures. An automated test facility was developed in which 1.1 kW three phase motor could be tested under varying shaft rotation speeds and loads for validating the developed model. Time and frequency domains statistical parameters of the measured signals were calculated for fault detection and assessing its severity. The measured signals were also processed using the short time Fourier transform (STFT), the Wigner-Ville distribution (WVD), the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) and wavelet multi-resolution analysis (MRA). The non-stationary components, representing faults within induction motor measured vibration and current signals, were successfully detected using wavelet decomposition technique. An effective alternative to direct vibration measurement scheme, based on radial basis function networks, was developed to the reconstruction of motor vibration using measurements of one phase of the motor current. It was found that this method captured the features of induction motor faults with reasonable degrees of accuracy. Another method was also developed for the early detection and diagnosis of faults using an enhanced power factor method. Experimental results confirmed that the power factor can be used successfully for induction motor fault diagnosis and is also promising in assessing fault severity. The suggested two methods offer inexpensive, reliable and non-intrusive condition monitoring tools that suits real-time applications. Directions for further work were also outlined

Development, implementation and testing of an expert system for detection of defects in gas turbine engines

Unbalance and misalignment are the major causes of vibration in rotating machinery, yet only limited research has been conducted on misalignment. The literature reports that misalignment results in an increase in the vibration at a frequency corresponding to two times the rotating speed (2x responses). The research on misalignment conducted so far has modeled the rotor as two coupled shafts supported on linear and non linear bearings, while misalignment is at the coupler. The results reported to date are inconsistent and the vibration response of a misaligned rotor system is not clearly understood. This dissertation presents a study on the effects of a single shaft misalignment on the dynamic response of a rotor-shaft system. A rotor system supported on two rigid bearings with unbalance and misalignment is modeled using the energy method, and Lagrange formulation is used to establish the equations of motion. The misalignment is modeled through introduction of pre-load and nonlinear shaft stiffness in the direction of pre-load. The model is validated by comparing the natural frequencies predicted using the simulation to the rotor system eigenvalue and the forced response from the simulation is verified using finite element method. A response due to perfectly aligned case is compared with those for parallel and angular misalignments of various magnitudes. Simulations are carried out for a speed range of 0 to 10,000 rpm, and the response of the rotor at the 2x is carefully examined to establish the effects of various misalignment and non-linear parameters on the response. Experiments are conducted using a rig test to compare with analytically predicted trends. Various gas turbine engine data gathered from the field are also used to confirm the vibration pattern predicted by the simulations. The simulated results are finally used to develop an expert system that can identify unbalance and misalignment in a rotor system. The expert system is developed using Neural Network. Two types of Neural Networks are explored, the back-propagation and the Logicon Projection Network. Finally, both networks are modified, trained and tested using simulation data. The Logicon projection network showed superior performance during training, and was chosen over the back-propagation network. The developed expert system is tested using field test data of gas turbine engines to demonstrate its effectiveness

Integrative (Synchronisations-)Mechanismen der (Neuro-)Kognition vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisationsparadigmas

Der Gegenstand der vorliegenden Arbeit besteht darin, aufbauend auf dem (Haupt-)Thema, der Darlegung und Untersuchung der Lösung des Bindungsproblems anhand von temporalen integrativen (Synchronisations-)Mechanismen im Rahmen der kognitiven (Neuro-)Architekturen im (Neo-)Konnektionismus mit Bezug auf die Wahrnehmungs- und Sprachkognition, vor allem mit Bezug auf die dabei auftretende Kompositionalitäts- und Systematizitätsproblematik, die Konstruktion einer noch zu entwickelnden integrativen Theorie der (Neuro-)Kognition zu skizzie-ren, auf der Basis des Repräsentationsformats einer sog. „vektoriellen Form“, u.z. vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisations-Paradigmas

The emergent patterns of Italian idioms:a dynamic-systems approach

In traditional generative linguistic theories, idiomatic constructions are seen as a sort of “anomaly”, and dismissed as non-decomposable items of non-literal language, uninteresting and “peripheral”. Contrary to this view, in the last decades psycholinguistic and corpus-linguistic studies have shown that idioms can often undergo structural modification and display different variation patterns, according to their specific formal and semantic properties. In virtue of these findings, the present study aims to investigate the levels of stability and variation in Italian idioms from a socio-cognitive point of view, in a two-step fashion. In the first stage, a set of 150 idiomatic constructions will be selected from a dictionary (Sorge 2010) and, taking the categorization proposed by Langlotz (2006a) as a starting point, a cognitively motivated typology of Italian idiomatic constructions will be drawn. Langlotz's parameters and categories will be used to classify Italian idioms into a structured taxonomy based on a set of notions which are generally accepted and employed by proponents of functionally-oriented approaches to language; these notions will be applied taking the Italian cultural context into consideration, in order to avoid (potentially hasty) claims about their supposed universality. Then, the mutual relationship between different idioms on the one hand and between idiomatic and non-idiomatic constructions on the other hand will be addressed and accounted for in the light of a constructionist perspective on language. In the second part of my study, a sample of occurrences of a subset of 50 idiomatic constructions will be downloaded from a large Italian corpus, in order to observe their variational behavior in the context of actual interactions in a contemporary setting. Particular attention will be paid to the potential correlation between the category an idiom was allocated to in the previous stage and the variation patterns observed in its occurrences, with the specific aim to understand if a causal connection can be established between the idiom category and the (quantitative and qualitative) level of variation observed in real language data. The two phases of the study will be treated as deeply interconnected, and a dynamic-systems approach will be adopted to highlight the several links between the two stages. An integrated model of the mechanisms which regulate the “life-dynamics” of idiomatic constructions will be provided, taking distinct dimensions, time-scales, and levels of granularity into account. Finally, the results of the study will be scrutinized in order to assess the adequacy of a dynamic-systems perspective to accurately explore and describe the self-organizing nature of linguistic constructions and their relationship with other aspects of human cognition and interactivity