Cognitive Sensor Technology for Structural Health ... - ScienceDirect

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Available online www.sciencedirect.com Available online at at www.sciencedirect.com Structural Integrity Procedia 00 (2017) 000–000

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Procedia Structural 5 (2017) 1160–1167 Structural IntegrityIntegrity Procedia 00 (2016) 000–000

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2nd International Conference on Structural Integrity, ICSI 2017, 4-7 September 2017, Funchal, Madeira, Portugal

Cognitive Sensor Technology Health Monitoring XV Portuguese Conference on Fracture, PCF for 2016,Structural 10-12 February 2016, Paço de Arcos, Portugal Alexander Thermo-mechanical modeling of aSerov* high pressure turbine blade of an Research Group of Automatic Intelligent Data Acquisition AIDA), Zelenograd, 124498, Moscow, Russian Federation airplane gas(RGturbine engine Abstract a

P. Brandãoa, V. Infanteb, A.M. Deusc*

Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa,

Current paper presents artificial neural network architecture Portugal based on Cognitive Sensor technology which may be used for b IDMEC, Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, development of intelligent SHM systems. Dynamic Artificial Portugal Neural Network (DANN) has time dependent structure which is c defined by experience of of processing input data streams. Advantages of proposed model include ability to learn both andLisboa, nonCeFEMA, Department Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, linear 1049-001 Portugal linear patterns on the basis of processing data streams. Evolution of DANN architecture includes stage of autonomous growth of subnets developed by separate Cognitive Sensors and stage of cooperative growth of resulting network. Example application of proposed cognitive technology for solution of Structural Health Monitoring problems is discussed. © Abstract 2017 The Authors. Published by Elsevier B.V. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of ICSI 2017. Peer-review underoperation, responsibility of the Scientific of ICSI 2017are subjected to increasingly demanding operating conditions, During their modern aircraft Committee engine components especially the high pressure turbine (HPT) blades. Such conditions cause these parts to undergo different types of time-dependent Keywords: cognitive architecture, dynamic artificial neural network, Structural Health Monitoring; evolved systems degradation, one of which is creep. A model using the finite element method (FEM) was developed, in order to be able to predict the creep behaviour of HPT blades. Flight data records (FDR) for a specific aircraft, provided by a commercial aviation company, were used to obtain thermal and mechanical data for three different flight cycles. In order to create the 3D model 1. needed Introduction for the FEM analysis, a HPT blade scrap was scanned, and its chemical composition and material properties were obtained. The data that was gathered was fed into the FEM model and different simulations were run, first with a simplified 3D Structuralblock Health Monitoring is basedthe onmodel, technologies detection, identification and characterization rectangular shape, in order to(SHM) better establish and then of with the real 3D mesh obtained from the blade scrap. of The overall and expected behaviour terms of displacement was structures. observed, inIntegration particular atof thesensors trailing inside edge ofthe thestructure blade. Therefore such a damage degradation ofinproperties of engineering of systems can be useful in thepossible goal of predicting turbine blade life, new given engineering a set of FDR data. to model be monitored makes to construct principally systems. One of most natural ways of

innovation in the field of SHM is connected with development of technologies for automatic analysis of health © 2016 The Authors. Published by Elsevier B.V. indicators – quantities which characterize the state of engineering system and optimize prediction of this state. Peer-review under responsibility of the Scientific Committee of PCF 2016. Success of exploitation of technical system highly depends upon the accuracy of detection of structural damage and evaluation degree ofTurbine influence ofCreep; this damage on functionality of this system. Each indicator of health of technical Keywords: of High Pressure Blade; Finite Element Method; 3D Model; Simulation. system highly depends upon the function of this system and operating conditions. Currently health indicators are

* Corresponding author. Tel.: +7-963-640-2100. E-mail address: [email protected] 2452-3216 © 2017 The Authors. Published by Elsevier B.V. Peer-review underauthor. responsibility the Scientific Committee of ICSI 2017. * Corresponding Tel.: +351of218419991. E-mail address: [email protected] 2452-3216 © 2016 The Authors. Published by Elsevier B.V.

Peer-review under responsibility of the Scientific Committee of PCF 2016. 2452-3216  2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of ICSI 2017 10.1016/j.prostr.2017.07.027

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constructed manually on the basis of results of numerical simulation or experimental results of using similar systems. This way doesn’t guarantee good accuracy of damage identification in the case of high complexity of engineering system. Often prediction of damage or of degradation of properties of engineering structure is complicated by poorly known conditions of its operation. These problems may be solved on the next stage of development of SHM systems. Next generation of technologies developed for monitoring of health of technical systems will include the set of means for the simulation of intelligent processing of data. The need to use intelligent techniques is dictated by continuous increasing of complexity of created technical systems and complexity of technologies that human society has. Phenomena and events in these systems may be essentially different on the scale, dynamics and character from those which are ordinary for us as human beings. Currently analysis of events in these systems is based on the transformation of patterns that characterize these events (and often are not directly interpretable) into the set of patterns which we can interpret. This way may be applied if there are no hard limitations on time boundaries of this analysis. It’s hard to use this way if the dynamics of source events is higher than that in our world. Development of technical systems which are able to cognitive activities will make possible to realize control and management of events in technological field providing the degree of detail which is inaccessible for human consciousness. Development of cognitive technical systems would make possible to realize ideas of Ubiquitous Computing, Weiser (1994) in the field of Structural Health Monitoring. Automated knowledge extraction is one of basic requirements for the construction of Integrated Vehicle Health Monitoring (IVHM) systems, Marzat et al, (2012), Price et al. (2003). IVHM architecture may be used as a basic architecture for the construction of a new type control systems of aircrafts, submarines, and spaceships, Price et al. (2003). We can suppose that IVHM-related direction of research and development in the field of SHM will lead in the future to creation of intelligent technical systems able to self-healing. In this paper we consider the problem of development cognitive architecture and application of this architecture for the monitoring of health of engineering systems. 2. Dynamic Artificial Neural Networks Workflow which traditionally is implemented in SHM systems includes the following set of stages: periodical measurements by the array of sensors; processing results of measurements aiming to extract damage sensitive features; analysis of extracted features to identify current state of the object, Chang at el. (2011), Kim et al. (2007). Engineering structures which are subject to condition monitoring may be characterized as dynamical systems. Structural properties of these systems change with time. Frequently this change has several different time scales. And dynamics of change of structural properties especially for complex technical systems is unknown. All this gives rise to a number of problems which concern adaptation of data processing methods to particular type of monitored object or even to particular instance of the same type. One of possible ways of solution is connected with application of adaptive methods of monitoring. According this way SHM system must adapt itself to particular instance of monitored object. We believe this adaptation may be done by application of principles of Cognitive Science. SHM system by periodical measurements and processing results of these measurements gradually constructs knowledge base. This knowledge base characterizes just one particular instance of engineering system. But we can develop knowledge repository which will be universal. For the solution of this problem we can use Artificial Neural Networks (ANN). Artificial Neural Networks are considered today as a most perspective way of development Artificial Intelligence (AI) based systems. The number of various types of architecture of ANN existing now is quite big (see, for example, Abdelwahab (2016), Schmidhuber (2015)). From this set of architectures we would like to highlight those neural networks which support Machine Learning Algorithms from a Deep Learning class: Deep Learning Architectures, Bengio et al. (2009), Bengio et al. (2015). The most important advantage of Deep Architectures is their ability to learn multiple levels of representation that correspond to different levels of abstraction of outer world. These levels realize the hierarchy of concepts which AI system has due to previous cognitive activity. Automatic construction of hierarchical description of outer world is one of most important problems in the field of AI based SHM systems. Patterns which are ordinary for human consciousness are very rough for representation of world which is observed by cognitive technical systems. Concepts which we learn from our experience (experience of human beings) are not fitted well for the description of events which arise in technical systems. For estimation of health of engineering structures and for prediction of dynamics of health it is necessary to use SHM systems that are

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able to learn their own patterns from the scratch. Another one key point for the choice of the class of neural networks is associated with ability to construct dynamic models. Neural networks must be able to learn in data streams. They must realize Life-Long Machine Learning methods and must be able to implement context based learning. Numerical technology proposed here is based on results of investigation of biological brains. These investigations were applied in the field of Artificial Intelligence as Spiking Neuron Models, Maass (1997), Pyle and Rosenbaum (2017), Jin et al. (2008), Merolla et al. (2014). Architecture of Spiking Neural Networks which we are considering here is dependent upon the time. Key ideological principle used at creation of this architecture is as follows: the appearance of a new element of knowledge requires the appearing of new elements in the structure of neural network. New knowledge of AI based system is stored in the network by arising of new neuron or by arising of new connection between existing neurons. The process of appearance of new structural elements is a threshold process. Quantity which controls this process is one of most important parameters which must be tuned for definite field of application of neural network. Proposed architecture of neural network assumes gradual accumulation of knowledge, which is expressed in an increase of total number of network elements. Evolution of neural network includes several epochs. Each separate epoch is characterized by arising of a new layer of neurons in the structure of network. Each layer of neurons fixes by its arising the appearance of patterns which have more high level of hierarchy than previous ones. In contrast to current classification of ANN on cyclic (recursive) and acyclic (feedforward) architectures, proposed architecture cannot be attributed with any of these classes. This is related with fact that change of network structure is not based on the definite architecture of connections between elements of network. Architecture of the network has dynamics which is determined by the definite experience of perception and learning. Hence principally it is possible the arising of both cyclically and acyclically connected elements of network. Driving force of evolution of neural network interconnections includes several components. Accuracy of prediction of outer world dynamics is one of these components. This prediction is necessary for planning actions by intelligent technical system. And planning is connected with goal-setting. Here we will not go into discussion of these problems. We will consider that construction of new layers of abstraction is completed when the error on a predetermined depth of prediction doesn’t exceed the value of accuracy limit which is required for solution of current tasks by intelligent system. The model of a neuron which we use in our model is as follows. Each neuron has several inputs and single output. Each neuron may be in one of two states: activated and deactivated. If neuron has deactivated state it cannot emit spike. Neuron makes processing of input signals each time when its input is changed by the network. Each neuron has  its own logic of input data processing. Processing of input signals produces some scalar value y: yi  f i z  , where i is the index, unique identifier of neuron in the network; z = (z1, …, zN) is vector which characterizes input signals; N is a total number of inputs of i-th neuron. Neuron passes into the activated state in the case if: yi > Thi, where Thi is the value of activation potential. If neuron has been activated it becomes emit spikes. Dynamic Artificial Neural Network (DANN) in general case has several layers of neurons: input layer, output layer and several intermediate layers. Neurons of first layer may be characterized as perceptive neurons, they receive signals from sensor system. Input layer of neurons is constructed as a set of filters. Neurons of this layer are characterized by zero value of activation potential. Second layer of neurons is constructed on the basis of information about purposes of the use of DANN. Function of this layer is to make pre-processing of signals coming from the input layer. Intermediate layers of neural network are created for representation of various space and time patterns. These patterns become parts of hierarchical structure which is used by cognitive system for perception, learning and understanding dynamics of observed world. Output layer of neurons is constructed for representation of most high level patterns which neural network learns from observations. Structure of DANN is constructed dynamically from the scratch on the basis of the set of input signals. Construction of layers of network is initialized when neural network starts processing input signals. Methods of learning of Dynamic Artificial Neural Networks may be developed on the basis of Harmony Theory formulated by Paul Smolensky, Smolensky (1986). The core of this theory is formulated as a harmony principle: the cognitive system is an engine for activating coherent assemblies of atoms and drawing inferences that are consistent with the knowledge represented by the activated atoms. The mathematics of Harmony Theory is founded on well known concepts of cognitive science: inference through activation of schemata. Schemata are coherent assemblies of knowledge atoms which are the means for supporting inference of knowledge. Knowledge atoms are fragments of

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representations that accumulate with experience. At the time of inference, stored knowledge atoms are dynamically assembled into context-sensitive schemata. Assembly of schemata (activation of atoms) and inference (completing missing parts of the representation) are both achieved by finding maximally self-consistent states of the system that are also consistent with the input. Each schema encodes the statistical relations among a few representational features. During inference, the probabilistic information in many active schemata are dynamically folded together to find the most probable state of the environment. Smolensky hypothesized that there is a procedure for accumulating knowledge atoms through exposure to the environment so that the system will perform the completion task optimally. Realization of this procedure is a key for implementation of Machine Learning methods for the development of Dynamic Neural Networks. Harmony Theory became the basis for creation of Deep Learning architectures, such as Helmholtz Machine, Hinton et al. (1995), Bornschein et al. (2016), and Restricted Boltzmann Machine, Salakhutdinov et al. (2007). In paper Serov (2016) we mentioned that it is the methods of unsupervised Machine Learning that must be the basis for the construction of cognitive technical systems. First stage of self-development of consciousness cannot be realized in supervised mode. Because cognitive system at this stage has no any representations of outer world. That is why Harmony Theory gives a most suitable way for the development of methods of machine learning applicable for DANN architectures. On a very first stage of evolution neural network must be learned with unsupervised methods. And when cognitive system already have created the set of basic representations, DANN may be learned further with supervised Machine Learning techniques. For the needs of Structural Health Monitoring we proposed DANN architecture which is based on the model of Cognitive Sensor. 3. Model of Cognitive Sensor In our previous work, Serov (2016), we proposed the model which may be used as a basis for the development of a new cognitive architecture: model of Cognitive Sensor (CS). We suppose that Cognitive Sensor is an element of a cognitive system which evolves according principles of self-organized systems. Each CS can percept outer world and can construct representations of this world in the range of perception which is provided by design of sensor. Architecture of Cognitive Sensor includes three subsystems: sensory subsystem, pre processing subsystem and logical subsystem. Sensory subsystem is responsible for measurements of scalar value x which characterizes some property of external world. Perception of CS is one-dimensional, and we assume that x can take values from some bounded set of values. Pre processing subsystem realizes three main functions. First, it makes the discretization of the input stream of values coming from sensory subsystem. Second, it makes the ordering of input values sampled in the stream according to their position within the stream. And third, it makes pre processing of sampled and ordered set of values. Logical subsystem can store and process the sequence of preprocessed values. A feature of logical subsystem is that it is capable of processing only a discrete set of values. Overall operating logic of CS is based on the ability to compare the values that x takes at different positions within the stream of data which must be analyzed. Mathematical model of Cognitive Sensor may be formulated as a non-deterministic finite state automaton: Kripke model. In this case we represent Kripke model as 4-tuple of the following type: K = (S, I, R, F), where S is the finite set of states of automaton; I is the set of initial states: I  S ; R is the set of transitions between the states: R  S  S , where s  S , s *  S so that s, s *  R ; F is the function which makes labeling of states. Here term “state” has the meaning of the representation of world which was previously recorded in the experience of perception of CS as something which can be distinguished by CS from other representations. Each state s and each transition between states (s, s*) are characterized in CS model by statistics of data stream processing, i.e. by the number of times that a given state or transition was observed during experience of perception. Represented model has the following features. First, this model is a non-stationary model. Contents of the sets S, R and their statistics depend upon the time. This feature represents the ability of Cognitive Sensor to perform learning in data streams. Second, both these sets depend upon the type of discretization of input data stream. In accordance with principles of Artificial Subjective Reality (ASR), Serov (2016), the memory of CS doesn’t include any representations of perceived world at the initial state of automaton: the set I is empty.

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The model of Cognitive Sensor includes two aspects of perception of the world. Static aspect is expressed in the model as a set of states S. Dynamic aspect of the model is expressed by a plurality of transitions between states. Accounting the dynamics of states is the key to one important problem of AI, the problem of constructing a hierarchical system of representations. Combining these two aspects into one model makes it possible to build the perception of cognitive system adaptively to the set of processes, which artificial intelligent system is able to observe in the outside world. 4. CS-DANN Architecture for Structural Health Monitoring Systems Construction of monitoring systems ordinary is based on the knowledge about normal and abnormal states of monitored object. This way assumes the presence of a set of interrelated scientific and technological structures responsible for developing, researching and supporting the means of certain monitoring systems. Personnel involved in this work should have expertise in the field of control methods. These personnel should also thoroughly understand the design and technology of controlled equipment. Gradual accumulation of knowledge allows to improve both methods of control, and the design of the object of control. This approach, however, has a number of shortcomings. These include increasing demands for training and an increasingly narrow specialization of monitoring systems. Increasing degree of automation of monitoring data analysis can help in solving these problems. We suppose that in the not too distant future, the technology of engineering used by mankind will undergo a significant transformation: the technology of evolving machines should appear. The emergence of machines that develop and produce other machines will allow human society to completely change its technological foundation. One of the main prerequisites for the emergence of this technology is the implementation of cognitive functions in technical systems. In this section, we would like to illustrate how cognitive functions could be implemented in Structural Health Monitoring systems. We suppose to use ideas of Artificial Subjective Reality, and implement these ideas in cognitive architecture of Dynamic Artificial Neural Networks. Architecture of DANN for using in the field of Structural Health Monitoring is based on the following. We suppose that SHM system is equipped by the set of sensors: Sr = (Sr1, …, SrM), where M is a total number of sensors. All sensors make periodical measurements synchronously. Each sensor can process measured data according the logic of Cognitive Sensor model. Use of ASR model supposes the absence of pre-determined information about world observed by SHM system. So the main purpose of actions performed by CS-DANN architecture is to construct representations of observable world on the basis of data gathered by sensor elements. Interaction of separate Cognitive Sensors inside this architecture may be explained by principle of self-organization. Evolution of cognitive CS-DANN architecture may be realized by different scenarios. According scenario represented here this evolution has three main stages. First stage may be characterized as autonomous evolution of different CS. On this stage each Cognitive Sensor makes formation of separate neural network. Neural networks built by different CS doesn’t interact each other. CS-DANN structure at this stage may be described as a set of separated subnets. Second stage of evolution is associated with beginning of mutual processing of data by subnets of different Cognitive Sensors. Interaction of networks constructed by different sensors results in originating of network elements that belong to several subnets. This stage of evolution of CS-DANN structure is completing with arising of united neural network. Third stage of the change of neural network may be described as an evolution of the totality of united subnets produced by different Cognitive Sensors. Transition between different stages of evolutionary growth of network occurs as a step-wise process. In numerical experiments with CS-DANN architecture we use emulation of data streams gathered by M = 8 tilt sensors. These sensors were used for condition monitoring of real technical object. Test object was exposed to several different types of impact. Our purpose during numerical simulation was to identify how different types of impact affect the evolution of CS-DANN structure. At the beginning of experiment each CS starts processing data streams; first stage of data processing is associated with intensive originating of perceptive neurons (PN). Spikes generated by originated PN are coming into domain of signal preprocessing. These spikes in turn initiate originating of neurons of preprocessing layer. Each neuron of preprocessing layer starts emitting spikes when it is activated by the set of input spikes from perceptive neurons. Here we will mean that the state s of separate Cognitive Sensor is described by activation of neuron from its preprocessing layer. Transition between states is described by the sequence of activation or reactivation of neurons. On this stage of

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evolution each cognitive sensor starts construction of its separate model of space and time from the set of observations. This model includes the set S of states and the set R of transitions between these states. The set of states describes static features of observable world, while the set of transitions describes its dynamic features. Fig. 1 illustrates measurements performed by sensor 5 during time range which includes four different types of impact on the object: creep, static, dynamic and repair. During analysis of data processing by CS-DANN architecture we used several quantities. This set includes total number of perceptive neurons, total number of neurons of preprocessing layer, total number of connections between neurons of preprocessing layer, and value of prediction RMSE.

Fig. 1. Tilt values measured by sensor 5 in the range between 260000 and 270000 measurements.

Fig. 2. Total number of transitions between states measured for subnet of CS 5 in the range between 260000 and 270000 measurements of tilt.

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Fig. 3. Values of prediction RMSE measured for subnet of CS 5 in the range between 260000 and 270000 measurements of tilt.

Each quantity has its own sensitivity with respect to external influences on the monitored object. Perceptive neurons are generated when cognitive system meets new values, i.e. something that still was not percept before in its experience. Appearance of new perceptive neurons means enlargement of the range of possible input signals. Generation of new perceptive neurons on late stages of history of data processing by CD-DANN architecture is an alarming symptom. This may mean sufficient impact on the object of control or sufficient change of its health. Total number of states (S) of Cognitive Sensor is sensitive only for creep and static types of impact. This may be explained by implementation of definite preprocessing technique in our experiments. During part of experiment represented in Fig. 1 total number of perceptive neurons was not changing. Appearance of new states in this case may be explained by the significant intensity of the load that was concentrated in time. Total number of transitions between states (R) of Cognitive Sensor (Fig. 2) is sensitive to all types of impact. High sensitivity of this quantity is the reflection of its physical nature. Each time when CS-DANN architecture generates new neurons the number of transitions is growing. And as well it is growing when cognitive system meets new sequence of activation of neurons. RMSE of prediction (Fig.3) is a most sensitive quantity which was found in our experiments on analysis of health of technical object. In our experiments we used one of most simple methods for prediction: maximum likelihood prediction which was applied for non-hierarchical representations. High sensitivity of RMSE with respect to change of statistics of neurons firing makes possible to identify weak change of dynamics of outer world. But otherwise this super-sensitivity may blur important changes in the system (compare, for example, Fig.2 and Fig.3 for dynamic and repair impacts). In normal case prediction RMSE tends to decrease to some typical values; increasing of prediction error mean that cognitive system meets unordinary dynamics of the world. On the basis of analysis of numerical experiments with CS-DANN architecture we can conclude that both the change of structure of artificial network and change of dynamics of firing spikes by neurons may be used for detection and identification of events related to structural health. In paper, Serov (2016), we mention that realization of high cognitive functions is two-fold problem connected with calculation of time-and-space model by cognitive system. First part of this problem relates to architecture of perceptive subsystem. Separate Cognitive Sensor is able to build a model of the world, which is based on the concept of sequence. In this type of model the concept of time is natural. It is introduced by cognitive system on the basis of empirical principles as a value which characterizes the dynamics of changes of state. But it is fundamentally impossible introduce the concept of space in this model. Empirically space appears if architecture of cognitive system includes multiple sensors designed to measure the same physical quantity. Second part of mentioned problem relates to way of evolution of neural architecture in attempts to improve time-and-space model. During first stage of evolution of CS-DANN architecture each Cognitive Sensor calculates its own model. Results of these activities are stored in architecture as a

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set of disconnected neural sub-networks. These models describe dynamic features of the world. On the next stage of evolution cognitive system must unite these multiple time models and construct true time-and-space model. 5. Conclusions In current paper we represent results of our activities on development of cognitive system on the basis of model of Cognitive Sensor. Main feature of proposed CS-DANN architecture is the dependence of architecture of network upon the time. We believe that the main advantage of proposed method is a significant degree of generality. There is no need to enter information about the specifics of phenomena in the observable world inside the model; this information can be embedded in the methods used to preprocess the signal. Thus resulting architecture of cognitive system becomes universal. It includes cognitive core which process input data and evolves in a way which is non-dependent upon the architecture and technology of perceptive subsystem. And it includes layer of architecture which was preliminary adapted to specific observable reality. On the next stage of work our research group plans to finish development of numerical method able to simulate all stages of artificial neural network evolution as a single process. Most interesting areas for our further work include development of layered Machine Learning techniques and techniques for autonomous automatic exploration. References Abdelwahab, S., Ojha, V., Abraham, A., 2016. Ensemble of Flexible Neural Trees for Predicting Risk in Grid Computing Environment. In: Innovations in Bio-Inspired Computing and Applications. Springer International Publishing, pp. 151-161. Bengio, Y., Goodfellow, I., Courville, A., 2015. Deep learning. Nature, 521, 436-444. Bengio, Y., 2009. Learning deep architectures for AI. Foundations and trends in Machine Learning, 2(1), 1-127. Bornschein, J., Shabanian, S., Fischer, A., Bengio, Y., 2016, May. Bidirectional Helmholtz Machines. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 2511-2519. Chang, F., Markmiller, J., Yang, J., Kim, Y., 2011. Structural health monitoring. System Health Management: With Aerospace Applications, pp. 419-428. Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The" wake-sleep" algorithm for unsupervised neural networks. Science, 268(5214), 1158. Jin, X., Furber, S. B., Woods, J., 2008, June. Efficient modelling of spiking neural networks on a scalable chip multiprocessor. In: Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on. IEEE, pp. 2812-2819 Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., Turon, M., 2007, April. Health monitoring of civil infrastructures using wireless sensor networks. In: Proceedings of the 6th international conference on Information processing in sensor networks, ACM, pp. 254-263. Maass, W., 1997. Networks of spiking neurons: the third generation of neural network models. Neural networks, 10(9), 1659-1671. Marzat, J., Piet-Lahanier, H., Damongeot, F., Walter, E., 2012. Model-based fault diagnosis for aerospace systems: a survey, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, SAGE Publications, 226(10), 1329-1360. Merolla, P., Arthur, J., Alvarez-Icaza, R., Cassidy, A., Sawada, J., Akopyan, F., Jackson, B., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S., Appuswamy, R., Taba, B., Amir, A., Flickner, M., Risk, W., Manohar, R., Modha, D., 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668-673. Price, D., Scott, D., Edwards, G., Batten, A., Farmer, A., Hedley, M., Johnson, M., Lewis, C., Poulton, G., Prokopenko, M., Valencia, P., Wang, P., 2003. An integrated health monitoring system for an ageless aerospace vehicle. In: Structural health monitoring 2003: from diagnostics & prognostics to structural health management, Chang, F.-K. (Ed). DEStec Publications, Lancaster, Pennsylvania, pp. 310-318. Pyle, R., Rosenbaum, R., 2017. Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks. Physical Review Letters, 118(1), 018103. Salakhutdinov, R., Mnih, A., Hinton, G., 2007, June. Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on Machine learning. ACM, pp. 791-798. Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural networks, 61, 85-117. Serov, A., 2016, July. Application of principles of Artificial General Intelligence in Structural Health Monitoring, 8th European Workshop On Structural Health Monitoring (EWSHM 2016), Bilbao, Spain, paper #22. Smolensky, P., 1986. Information Processing in Dynamical Systems: Foundations of Harmony Theory, CU-CS-321-86. Weiser, M., 1994, March. Ubiquitous computing. In ACM Conference on Computer Science, p. 418.