Integration of intelligent systems in development of smart adaptive systems:linguistic equation approach

Abstract Smart adaptive systems provide advanced tools for monitoring, control, diagnostics and management of nonlinear multivariate processes. Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but highly complex systems require domain expertise and more compact approaches at the basic level. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. This research is focused on the smart adaptive applications, where different intelligent modules are used in a smart way. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are defined separately for the scaling and the interactions. The LE based intelligent analysers are useful in the multilevel LE control and diagnostics: the LE control is enhanced with the intelligent analysers, adaptive and model-based modules and high level control. The operating area is extended with the predefined adaptation and specific events activate appropriate control actions. The condition, stress and trend indices are used for the detection of operating conditions. The same overall structure is extended to the scheduling and managerial decision support. The linguistic representation becomes increasingly important when the human interaction is essential. The new scaling approach is used in control and diagnostic applications and discussed in connection with previous multivariate modelling cases. The LE based intelligent analysers are the key modules of the system integration, which produces hybrid systems: fuzzy systems move gradually to higher levels, neural networks and evolutionary computing are used for tuning. The overall system is reinforced with advanced statistical analysis, signal processing, feature extraction, classification and mechanistic modelling.Tiivistelmä Viisaat mukautuvat järjestelmät sisältävät kehittyneitä työkaluja epälineaaristen monimuuttujaisten prosessien valvontaan, säätöön, diagnostiikkaan ja johtamiseen. Laajaan menetelmäpohjaan perustuva tiedonrikastus on pohjana älykkäiden järjestelmien yhdistämiselle. Pienille erikoistuneille järjestelmille on monia toteutettavissa olevia ratkaisuja, mutta erittäin monimutkaiset järjestelmät vaativat alan asiantuntemusta ja kompakteja lähestymistapoja perustasolla. Sumeaan logiikkaan pohjautuva lingvististen yhtälöiden (linguistic equation, LE) menetelmä on tehokas ratkaisu näissä ongelma-alueissa. Tämä tutkimus kohdistuu viisaisiin mukautuviin sovelluksiin, jossa useita älykkäitä moduuleja käytetään yhdessä viisaalla tavalla. Kehittyneeseen tilastolliseen analyysiin perustuva epälineaarinen skaalausmenetelmä muodostaa ratkaisun kulmakiven: muuttujien merkitykset soveltuvat säädössä ja diagnostiikassa käytettävien älykkäiden indeksien kehittämiseen. Uudet rajoituksien käsittelymenetelmät yhdessä yleistettyjen normien ja momenttien kanssa mahdollistavat rekursiivisen parametriestimoinnin olosuhteisiin mukautuvassa skaalauksessa. Tunnettuja lineaarisia menetelmiä käytetään staattisessa, dynaamisessa ja tapauspohjaisessa mallintamisessa, jossa kaskadi- ja vuorovaikutusrakenteet laajentavat mallit tarvittaessa monimutkaisiin sovelluksiin. Prosessituntemuksen ja järjestelmien robustisuuden varmistamiseksi parametrit määritellään erikseen skaalausta ja vuorovaikutuksia varten. LE-pohjaiset älykkäät analysaattorit ovat hyödyllisiä monitasoisessa säädössä ja diagnostiikassa: LE-säätöä parannetaan älykkäiden analysaattorien, adaptiivisten ja mallipohjaisten moduulien sekä ylemmän tason säädön avulla. Käyttöaluetta laajennetaan ennalta määrätyllä adaptoinnilla sekä tiettyjen tapahtumien aktivoimilla erityisillä säätötoimenpiteillä. Kunto-, rasitus- ja trendi-indeksejä käytetään olosuhteiden tunnistamiseen. Sama rakenne laajennetaan tuotannon ajoitukseen ja päätöksenteontukeen, jossa inhimillisen vuorovaikutuksen käsittely tekee lingvistisen esityksen yhä tärkeämmäksi. Uutta skaalausmenetelmää tarkastellaan säätö- ja diagnostiikkasovelluksissa sekä vertaillaan lyhyesti sen käyttömahdollisuuksia aikaisemmin toteutetuissa monimuuttujamalleissa. LE-pohjaiset älykkäät analysaattorit ovat keskeisiä integroitaessa moduuleja hybridiratkaisuiksi: sumeat järjestelmät siirtyvät vähitellen ylemmille tasoille ja neuro- ja evoluutiolaskennassa keskitytään järjestelmien viritykseen. Kokonaisjärjestelmää vahvistetaan kehittyneellä tilastollisella analyysilla, signaalinkäsittelyllä, piirteiden erottamisella, luokittelulla ja mekanistisella mallintamisella

Smart integration of energy production

Abstract Heat is primarily important in the world’s total energy consumption and especially, peak loads and seasonal variations result in problems which are difficult to efficiently come up with electricity networks. Renewable energy sources bring important new possibilities, especially for the heat energy. This paper focuses on heat energy: compact parametric models are essential in the smart integration of production in the district heating and efficient collection of the solar thermal energy. Thermal masses of the buildings are used in the peak load cutting as energy storages. The physical parameters calculated from the building information facilitate the use of different types of buildings simultaneously in the calculations. Smart adaptive control solutions extend feasible operating periods in collecting solar thermal energy. A combination of multiple control actions is essential in keeping the system in control during strong fluctuations in cloudiness and energy demand. Heat storages increase the collecting power and extend the utilisation of the solar energy utilisation over daily and seasonal periods

Advanced machine learning in recursive data-based modelling

Abstract Recursive data-based modelling is needed for making decision online in varying operating conditions. Recursive algorithms are useful in adapting the parameters within selected memory horizons. Abrupt changes can be handled when the situation change is approved to be drastic. The nonlinear scaling based on generalized norms includes additional alternatives: the norm orders adapt to the gradually changing operating conditions. The drastic shape changes of the scaling functions require full analyses of the orders. The orders can also be stored for different situations and re-used later. Fuzzy inequalities are useful in finding out if the feasible ranges of the most recent period are different from the current active ranges or similar with some of previous feasible ranges. Machine learning is integrated in the system in three levels: (1) finding the appropriate time windows, (2) interactions of feasible levels, and (3) finding decision support when some of feasible ranges need to change. These decisions are supported by expert knowledge. Other model parameters can be included in the analysis. The solution has been tested with measurement data from several application cases. The recursive approach is beneficial in the control and maintenance in varying operating conditions

Intelligent dynamic simulation of fed-batch fermentation processes

Abstract Batch bioprocesses are difficult to model due to strong nonlinearities, dynamic behaviour, lack of complete understanding and unpredictable disturbances. A cell produces more cells, chemical products and heat from chemical substrates. Typical growth characteristics include several phases whose appearances and lengths depend on the type of organisms and the environmental conditions. Large differences exist between different fermentation runs. The simulator developed for fed-batch fermentation processes consists of three interacting dynamic models, each with three phase specific versions. The models predict dissolved oxygen concentration, oxygen transfer rate and concentration of carbon dioxide in the exhaust gas through the whole process, by using only the control variables as inputs. A decision system based on fuzzy logic to provide smooth gradual changes between phases. The detection of the changes between process phases is improved by using the intelligent trend analysis. The dynamic simulator is suitable for an online forecasting tool in connection with the real process. The operation is based on the ideas of model predictive control (MPC): the previous online measurements on a chosen horizon are used for constructing a starting point and the simulator predicts the operation on a chosen prediction horizon by using the planned control actions. The simulation is started on fairly long time intervals

Intelligent performance analysis with a natural language interface

Abstract Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions

Intelligent multimodel simulation in decomposed systems

Abstract Intelligent methodologies provide a good basis for multimodel simulation. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. The nonlinear scaling approach extends the application areas of linear methodologies to nonlinear modelling and reduces the need for decomposition with local models. Fuzzy set systems provide a good basis for understandable models for decomposed systems. Data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Complex problems are solved level by level to keep the domain expertise as an essential part of the solution

Smart adaptive big data analysis with advanced deep learning

Abstract Increasing volumes of data, referred as big data, require massive scale and complex computing. Artificial intelligence, deep learning, internet of things and cloud computing are proposed for heterogeneous datasets in hierarchical analytics to manage with the volume, variety, velocity and value of the big data. These solutions are not sufficient in technical systems where measurements, waveform signals, spectral data, images and sparse performance indicators require specific methods for the feature extraction before interactions can be properly analysed. In practical applications, the data analysis, knowledge-based methodologies and optimization need to be combined. The solutions require compact calculation units which can be adaptively modified. The artificial intelligence is extended with various methodologies of computational intelligence. The advanced deep learning approach proposed in this paper uses generalized norms in feature generation, nonlinear scaling in developing compact indicators and linear interactions in model-based systems. The intelligent temporal analysis is available for all indices, including for stress, condition and quality indicators. The service and automation solutions combine these data-driven solutions with the domain expertise by using fuzzy logic for case-based systems. The applications are developed gradually in connections, conversion, cyber, cognition and configuration layers. The advanced methodology is based on the integration of features, scaling functions and interaction models specified by parameters. All the sub-systems and different combinations of them can be recursively updated and optimized with evolutionary computing. The systems adapt to the changing operating conditions and provide situation awareness for the risk analysis. The approach supports different levels of the smart adaptive systems

Intelligent methodologies in recursive data-based modelling

Abstract Intelligent methodologies are beneficial in developing understandable multimodel simulation solutions. Nonlinear scaling extends these applications by facilitating compact nonlinear approaches already at the basic level. Composite local models can continue using linear methodologies for various case-based models. The flexible handling of the new structures and recursive tuning are the keys in adapting the systems in varying operating conditions. The recursive tuning of the scaling functions has two levels: smooth adaptation and strong shape changes. Fuzzy set systems further extend application areas of the models by combining composite local models in a flexible way. The extensions of the data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Evolutionary computation is used in the tuning of the resulting complex models both the scaling and interactions. Complex problems are solved level by level to keep the domain expertise as an essential part

Expertise and uncertainty processing with nonlinear scaling and fuzzy systems for automation

Abstract Integration of domain expertise and uncertainty processing is increasingly important in automation solutions which rely on data analytics and artificial intelligence. We need a level to assess what is approximately correct. Uncertainties of the inputs are taken into account by using fuzzy numbers as the inputs of different fuzzy and parametric systems. Nonlinear scaling functions (NSFs) integrate these solutions and make them easier to tune. Fuzzy rule-based systems are represented with scaled fuzzy inputs. Membership functions (MFs) can be developed from NSFs and existing MFs can be used in developing NSFs. Fuzzy set systems and linguistic equation (LE) systems become consistent within the limits of detail. In recursive analysis, both meanings and interactions on all levels can be tuned together with genetic algorithms. In applications, the modular overall system consists of similar subsystems, which are normally used, with extensions to fuzzy. The compact fuzzy modules can be developed for specific tasks which are combined within Cyber Physical Systems (CPS). Uncertainty processing is embedded in the recursive analysis. The fuzzy extensions provide a feasible way for the sensitivity analysis of the solution

Recursive data analysis in large scale complex systems

Abstract Advanced data analysis is needed in practical applications in large scale complex systems. Variable specific datadriven solutions provide consistent levels, which can be used in compact model structures. In changing operating conditions, the recursive analysis extends the applicability of these structures in building and tuning dynamic and case-based models for complex systems since the meanings change more frequently than the interactions. The methodology provides information about uncertainty, fluctuations and confidence in results. The scaling approach brings temporal analysis to all measurements and features: trend indices are calculated by comparing the averages in the long and short time windows, a weighted sum of the trend index and its derivative detects the trend episodes and severity of the trend is estimated by including also the variable level in the sum. The trend episodes and temporal adaptation of the scaling functions with time are used in the early detection of changes in the operating conditions. The levels are understood as fuzzy labels and the decision making is based on fuzzy calculus. The solution is highly compact: all variables, features and indices are transformed to the range [-2, 2] and represented in natural language which is important in integrating datadriven solutions with domain expertise