Sergey Kirillov

By Sergey Kirillov, Aleksandr Kirillov, Vitalii Iakimkin, Michael Pecht and Yuri Kaganovich

The results of work on creating methods, models, and computational algorithms for remote preventive health-monitoring systems are presented, in particular, cardiac preventive monitoring. The main attention is paid to the models and computational algorithms of preventive monitoring, the interaction of the computing kernels of a remote cluster with portable ECG recorders, implantable devices, and sensors. Computational kernels of preventive monitoring are a set of several thousand interacting automata of analog of Turing machines, recognizing the characteristic features and evolution of the hidden predictors of atrial fibrillation(AF), ventricular tachycardia or fibrillation (VT-VF), sudden cardiac death, and heart failure (HF) revealed by them. The estimation of the time for reaching the heart events boundaries is calculated on the basis of the evolution equations for the ECG multi-trajectories determined by recognizing automata. Evaluation time of heart event (HE) boundaries to achieve is calculated on the basis of the evolution equations for ECG multi-paths defined by recognizing machines. Ultimately, the computational cores reconstruct the ECG of the forecast and give temporary estimates of its achievement. Cloud computing cluster supports low-cost ECG ultra-portable recorders and does not limit the possibilities of using a more complex patient telemetry containing wearable and implantable devices: CRT and ICD, CardioMEMS HF System, and so on.

Part of the book: Medical Internet of Things (m-IoT)

By Aleksandr Kirillov, Sergey Kirillov, Vitaliy Iakimkin and Michael Pecht

The chapter describes a mathematical model of the early prognosis of the state of high-complexity mechanisms. Based on the model, systems of recognizing automata are constructed, which are a set of interacting modified Turing machines. The purposes of the recognizing automata system are to calculate the predictors of the sensor signals (such as vibration sensors) and predict the evolution of hidden predictors of dysfunction in the work of the mechanism, leading in the future to the development of faults of mechanism. Hidden predictors are determined from the analysis of the internal states of the recognizing automata obtained from wavelet decompositions of time series of sensor signals. The results obtained are the basis for optimizing the maintenance strategies. Such strategies are chosen from the classes of solutions to management problems. Models and algorithms for self-maintenance and self-recovery systems are discussed.

Part of the book: Maintenance Management

By Sergey Kirillov, Aleksandr Kirillov, Vitalii Iakimkin, Michael Pecht and Yuri Kaganovich

This chapter describes the sets of interacting automata constructed on the cascades of wavelet coefficients of input signal. The basic principles of the evolution of automata during the processing of incoming cascades and the vector of processes consisting of segments of cascades of constant length are described. The main principles of constructing the family of automata are determined from the internal symmetry of incoming cascades and the definition of symmetry groups of vector processes and their isotropy groups. The trajectories of states are defined on nontrivial topological spaces, the so-called degeneration spaces of the characteristic functional. The family of evolving automata with tunable communications architecture is designed to predict the state of engineering objects and identify predictors, early predictors, and hidden predictors of failure. This chapter provides examples of the work of predictive automata in various fields of engineering and medicine. It demonstrates the operation of the automaton in spaces with a nontrivial topology of input cascades, algorithms of the predictor search, and estimations. The family of evolving automata with reconstructing architecture of connections is designed to predict the state of engineering objects and medicine and identify predictors, early predictors, and hidden predictors of failure. The architecture and functional properties of automata are determined from the results and main conclusions.

Part of the book: Fault Detection, Diagnosis and Prognosis