Subhadeep Chakraborty

Estimation of slowly varying parameters in nonlinear systems via symbolic dynamic filtering

This paper introduces a novel method for real-time estimation of slowly varying parameters in nonlinear dynamical systems. The core concept is built upon the principles of symbolic dynamic filtering (SDF) that has been reported in literature for anomaly detection in complex systems. In this method, relevant system outputs are measured, at different values of a critical system parameter, to generate an ensemble of time series data. The space of wavelet-transform coefficients of time series data is partitioned to generate symbol sequences that, in turn, are used to construct a special class of probabilistic finite state automata (PFSA), called the D-Markov machine. The parameter is estimated based on the statistical information derived from the PFSA. The bounds and statistical confidence levels, associated with parameter estimation, are also computed. The proposed method has been validated in real time for two nonlinear electronic systems, governed by Duffing equation and van der Pol equation, on a laboratory apparatus.

Void fraction measurement in two-phase flow processes via symbolic dynamic filtering of ultrasonic signals

A slant-shelf magazine for an automatic, coin controlled, vending machine adapted to dispense cylindrical articles, such as canned or bottled beverages, which are stored and gravitationally fed from plural, parallel, horizontally inclined superposed storage racks into a vertical drop chute located opposite the lower ends of such racks. The drop chute communicates with a horizontally inclined delivery chute having a vend mechanism at its lowermost end for releasing articles one-by-one to a discharge hopper upon customer selection. The delivery chute is oppositely inclined from the storage racks and is joined to the drop chute by an intervening curvilinear guideway formed to reverse the gravitational movement direction of the articles prior to entry into the delivery chute for purposes of reducing article load forces on the vend mechanism.

Analytic signal space partitioning and symbolic dynamic filtering for degradation monitoring of electric motors

This study presents an application of the recently reported theories of analytic signal space partitioning (ASSP) and symbolic dynamic filtering (SDF) to address degradation monitoring in permanent magnet synchronous motors (PMSM). An (experimentally validated) mathematical model of generic PMSM is chosen to monitor degradation/fault events on a simulation test bed; and the estimated parameter of health condition is observed to vary smoothly and monotonically with degradation in magnetization of the PMSM.

Damage monitoring of refractory wall in a generic entrained-bed slagging gasification system

Abstract The main cause of performance degradation in entrained-bed slagging gasification systems is attributed to evolution of structural damage in the refractory walls. Early detection of such damage is necessary to avert unscheduled shutdown of a gasification plant. This paper develops an integrated computer simulation model of a generic entrained-bed slagging gasifier for formulation of a damage prediction algorithm with the objective of real-time degradation monitoring and condition-based maintenance of refractory walls. The integrated simulation model yields: (a) quasi-steady-state spatial temperature profiles at any cross-section of the gasification system, and (b) dynamic response of the refractory wall temperature that is measured by an array of sensors installed at specified locations on the external surface of the gasifier wall. The key idea for early detection of refractory-wall damage is built upon the fact that a local anomaly (i.e. deviation from the nominal condition) is likely to influence the temperature gradient in the refractory wall due to changes in the thermal impedance. The information from dynamic response of refractory temperature is extracted in a compressed form as statistical patterns of evolving anomaly through usage of a recently reported data-driven pattern identification tool called symbolic dynamic filtering (SDF). The results of this model-based investigation show that the proposed anomaly detection and damage prediction method is potentially capable of characterizing the health status of refractory walls in particular and the entire gasification system in general. The SDF algorithms in this paper are implemented on the MATLAB platform and are interfaced with the gasification plant simulation model for emulation of real-time degradation monitoring.

Data-driven fault detection and estimation in thermal pulse combustors

Abstract This paper presents the development of a dynamic data-driven statistical method for: (a) early detection of incipient faults and (b) parameter estimation for prognosis of forthcoming failures and operational disruptions (e.g. flame extinction) in thermal pulse combustors. From these perspectives, reduction in the tailpipe friction coefficient is estimated from time-series data of pressure oscillations. The algorithms for parameter estimation are built upon the principles of Symbolic Dynamics, Information Theory and Statistical Pattern Recognition. The proposed algorithms have been tested on an experimentally validated simulation model of a generic thermal pulse combustor.

Analytical methods to investigate the effects of external influence on socio-cultural opinion evolution

This paper extends the framework for merging sociologically inspired rational cognitive models of decision making with social media inspired feedback mechanisms. This model, with certain simplifying assumptions, is used to analyze the effects of external influence on the dynamics of opinion evolution in a fully connected society. The master equation is shown to have the form of the Fokker-Planck equation, and the necessary and sufficient conditions for a polynomial solution are investigated. It is proved that the parameters of the model guarantees the existence of a polynomial solution.

Symbolic identification for fault detection in aircraft gas turbine engines

This article presents a robust and computationally inexpensive technique of component-level fault detection in aircraft gas-turbine engines. The underlying algorithm is based on a recently developed statistical pattern recognition tool, symbolic dynamic filtering (SDF), that is built upon symbolization of sensor time series data. Fault detection involves abstraction of a language-theoretic description from a general dynamical system structure, using state space embedding of output data streams and discretization of the resultant pseudo-state and input spaces. System identification is achieved through grammatical inference based on the generated symbol sequences. The deviation of the plant output from the nominal estimated language yields a metric for fault detection. The algorithm is validated for both single- and multiple-component faults on a simulation test-bed that is built upon the NASA C-MAPSS model of a generic commercial aircraft engine.

Classification of Two-Phase Flow Patterns by Ultrasonic Sensing

This paper presents a methodology for classification of twophase flow patterns in fluid systems, which takes the measurements of an in situ ultrasonic sensor as inputs. In contrast to the common practice of having an array of ultrasonic detectors, the underlying algorithm requires only a single sensor hardware in combination with an integrated software of signal conditioning, feature extraction, and pattern classification. The proposed method is noninvasive and can be implemented in a variety of industrial applications (e.g., petrochemical processes and nuclear power plants). This concept of flow pattern classification is experimentally validated on a laboratory test apparatus. [DOI: 10.1115/1.4007555]

Socio-cultural evolution of opinion dynamics in networked societies

This paper introduces a modeling paradigm based on a language theoretic framework for stochastic simulation of decision-making in a social setting, where choices and decisions by individuals are increasingly being influenced by a persons online social interactions. In this paper, the dynamics of opinion formation in a networked society have been studied with a joint model that bridges micro-level decisions based on reward maximization and the corresponding social influences which alter the estimate of these reward values. The effect of long term government policies on the stability and dynamics of the population opinion and the effect of including an influencing agent group has been studied. Simulated results on a sample society demonstrate the major impact of a relatively small but sharply opinionated influencing group toward pushing the society toward a desired outcome.

Data driven anomaly detection via symbolic identification of complex dynamical systems

Some of the critical and practical issues regarding the problem of health monitoring of multi-component human-engineered systems have been discussed, and a syntactic method has been proposed. The method involves abstraction of a qualitative description from a general dynamical system structure, using state space embedding of the output data-stream and discretization of the resultant pseudo state and input spaces. The system identification is achieved through grammatical inference techniques, and the deviation of the plant output from the nominal estimated language gives a measure of anomaly in the system. The technique is validated on an experimental test-bed of a permanent magnet synchronous motor undergoing a gradual degradation of the encoder orientation feedback.