Neural methods in process monitoring, visualization and early fault detection

This technical report is based on five our recent articles: ”Self-organizing map based visualization techniques and their assessment”, ”Combining neural methods and knowledge-based methods in accident management”, ”Abnormal process state detection by cluster center point monitoring in BWR nuclear power plant”, “Generated control limits as a basis of operator-friendly process monitoring”, and “Modelling power output at nuclear power plant by neural networks”. Neural methods are applied in process monitoring, visualization and early fault detection. We introduce decision support schemes based on Self-Organizing Map (SOM) combined with other methods. Visualizations based on various data-analysis methods are developed in large Finnish research project many Universities and industrial partners participating. In our subproject the industrial partner providing data into our practical examples is Teollisuuden Voima Oy, Olkiluoto Nuclear power plant. Measurement of the information value is one challenging issue. On long run our research has moved from Accident Management to more Failure Management. One interesting case example introduced is detecting pressure drift of the boiling water reactor by multivariate methods including innovative visualizations. We also present two different neural network approaches for industrial process signal forecasting. Preprosessing suitable input signals and delay analysis are important phases in modelling. Optimized number of delayed input signals and neurons in hidden-layer are found to make a possible prediction of an idle power process signal. Algorithms on input selection and finding the optimal model for one-step-ahead prediction are developed. We introduce a method to detect abnormal process state based on cluster center point monitoring in time. Typical statistical features are extracted, mapped to n-dimensional space, and clustered online for every step. The process signals in the constant time window are classified into two clusters by the K-means method. In addition to monitoring features of the process signals, signal trends and alarm lists, a tool is got that helps in early detection of the pre-stage of a process fault. We also introduce data generated control limits, where alarm balance feature clarifies the monitoring. This helps in early and accurate fault detection

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

Using a high fidelity CCGT simulator for building prognostic systems

Pressure to reduce maintenance costs in power utilities has resulted in growing interest in prognostic monitoring systems. Accurate prediction of the occurrence of faults and failures would result not only in improved system maintenance schedules but also in improved availability and system efficiency. The desire for such a system has driven research into the emerging field of prognostics for complex systems. At the same time there is a general move towards implementing high fidelity simulators of complex systems especially within the power generation field, with the nuclear power industry taking the lead. Whilst the simulators mainly function in a training capacity, the high fidelity of the simulations can also allow representative data to be gathered. Using simulators in this way enables systems and components to be damaged, run to failure and reset all without cost or danger to personnel as well as allowing fault scenarios to be run faster than real time. Consequently, this allows failure data to be gathered which is normally otherwise unavailable or limited, enabling analysis and research of fault progression in critical and high value systems. This paper presents a case study of utilising a high fidelity industrial Combined Cycle Gas Turbine (CCGT) simulator to generate fault data, and shows how this can be employed to build a prognostic system. Advantages and disadvantages of this approach are discussed

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Doprinos modeliranju otpora prljanja u izmjenjivaču topline-kondenzatoru izravnom i inverznom umjetnom neuronskom mrežom

The aim of this study was to predict the fouling resistance (FR) using the artificial neural networks (ANN) approach. An experimental database collected from the literature regarding the fouling of condenser tubes cooling seawater of a nuclear power plant was used to build the ANN model. All models contained 7 inputs: dimensionless condenser cooling seawater temperature, dimensionless inside overall heat transfer coefficient, dimensionless outside overall heat transfer coefficient, dimensionless condenser temperature, dimensionless condenser pressure, dimensionless output power, and dimensionless overall thermal efficiency. Dimensionless fouling resistance was the output. The accuracy of the model was confirmed by comparing the predicted and experimental data. The results showed that ANN with a configuration of 7 input neurons, 7 hidden neurons, and 1 output neuron presented an excellent agreement, with the root mean squared error RMSE = 3.6588 ∙ 10–7, average absolute percentage error MAPE = 0.1295 %, and high determination coefficient of R2 = 0.99996. After conducting the sensitivity analysis (all input variables had strong effect on the estimation of the fouling resistance), in order to control the fouling, an inverse artificial neural network (ANNi) model was established, and showed good agreement in the case of different values of dimensionless condenser cooling seawater temperature. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog istraživanja bio je predvidjeti otpor prljanja primjenom umjetnih neuronskih mreža (ANN). Baza podataka za ANN modeliranje preuzeta je iz dostupne literature i sadrži podatke vezane uz prljanje kondenzacijskih cijevi u sustavu hlađenja morskom vodom u nuklearnoj elektrani. Sedam parametara korišteno je kao ulaz u neuronske mreže: bezdimenzijska temperatura morske vode, bezdimenzijski unutarnji ukupni koeficijent prijenosa topline, bezdimenzijski vanjski ukupni koeficijent prijenosa topline, bezdimenzijska temperatura kondenzatora, bezdimenzijski tlak u kondenzatoru, bezdimenzijska izlazna snaga i bezdimenzijska ukupna toplinska efikasnost. Kao izlaz uzet je bezdimenzijski otpor prljanja. Točnost modela potvrđena je statističkom analizom podudarnosti predviđenih i eksperimentalno dobivenih podataka. Rezultati su pokazali izvrsno slaganje u slučaju neuronske mreže sa 7 ulaza, 7 neurona u skrivenom sloju i 1 izlazom, uz korijen srednje kvadratne pogreške (RMSE) od 3,6588 ∙ 10–7, srednju apsolutnu postotnu pogrešku (MAPE) od 0,1295 % te visoki koeficijent determinacije (R2 = 0,99996). Nakon provedene analize osjetljivosti (sve ulazne varijable imale su snažan utjecaj na procjenu otpora prljanja), s ciljem kontrole prljanja, uspostavljen je model inverzne umjetne neuronske mreže (ANNi); model je pokazao dobro slaganje za različite vrijednosti bezdimenzijske temperature morske vode. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Advanced Information System for Safety-Critical Processes

The paper deals with the design and implementation of an intelligent modular information system (IMIS) for modeling and predictive decision making supervisory control of some important critical processes in a nuclear power plant (nuclear reactor) using selected soft computing methods. The developed IMIS enables monitoring critical states, safety impact analysis and prediction of dangerous situations. It also recommends the operator possibilities how to proceed to ensure safety of operations and humans and environment. The proposed complex IMIS has been tested on real data from a nuclear power plant process primarily used as supervisory information for decision making support and management of critical processes. The core of the proposed IMIS is a general nonlinear neural network mathematical model. For prediction of selected process variables an artificial neural network of multilayer perceptron type (MLP) has been used. The effective Levenberg-Marquardt method was used to train the MLP network. Testing and verification of the neural prediction model were carried out on real operating data measurements obtained from the NPP Jaslovske Bohunice

Alloy Design for a Fusion Power Plant

Fusion power is generated when hot deuterium and tritium nuclei react, producing alpha particles and 14 MeV neutrons. These neutrons escape the reaction plasma and are absorbed by the surrounding material structure of the plant, transferring the heat of the reaction to an external cooling circuit. In such high-energy neutron irradiation environments, extensive atomic displacement damage and transmutation production of helium affect the mechanical properties of materials. Among these effects are irradiation hardening, embrittlement, and macroscopic swelling due to the formation of voids within the material. To aid understanding of these effects, Bayesian neural networks were used to model irradiation hardening and embrittlement of a set of candidate alloys, reduced-activation ferritic-martensitic steels. The models have been compared to other methods, and it is demonstrated that a neural network approach to modelling the properties of irradiated steels provides a useful tool in the future engineering of fusion materials, and for the first time, predictions are made on irradiated property changes based on the full range of available experimental parameters rather than a simplified model. In addition, the models are used to calculate optimised compositions for potential fusion alloys. Recommendations on the most fruitful ways of designing future experiments have also been made. In addition, a classical nucleation theory approach was taken to modelling the incubation and nucleation of irradiation-induced voids in these steels, with a view to minimising this undesirable phenomenon in candidate materials. Using these models, recommendations are made with regards to the engineering of future reduced-activation steels for fusion applications, and further research opportunities presented by the work are reviewed