contributor author | Ghalyan, Najah F. | |
contributor author | Mondal, Sudeepta | |
contributor author | Miller, David J. | |
contributor author | Ray, Asok | |
date accessioned | 2019-09-18T09:07:48Z | |
date available | 2019-09-18T09:07:48Z | |
date copyright | 5/8/2019 12:00:00 AM | |
date issued | 2019 | |
identifier issn | 0022-0434 | |
identifier other | ds_141_10_104502 | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4259205 | |
description abstract | Real-time decision-making (e.g., monitoring and active control of dynamical systems) often requires feature extraction and pattern classification from short-length time series of sensor data. An example is thermoacoustic instabilities (TAI) in combustion systems, caused by spontaneous excitation of one or more natural modes of acoustic waves. The TAI are typically manifested by large-amplitude self-sustained pressure oscillations in time scales of milliseconds, which need to be mitigated by fast actuation of the control signals, requiring early detection of the forthcoming TAI. This issue is addressed in this technical brief by hidden Markov modeling (HMM) and symbolic time series analysis (STSA) for near-real-time recognition of anomalous patterns from short-length time series of sensor data. An STSA technique is first proposed, which utilizes a novel HMM-based partitioning method to symbolize the time series by using the Viterbi algorithm. Given the observed time series and a hidden Markov model, the algorithm generates a symbol string with maximum posterior probability. This symbol string is optimal in the sense of minimizing string error rates in the HMM framework. Then, an HMM likelihood-based detection algorithm is formulated and its performance is evaluated by comparison with the proposed STSA-based algorithm as a benchmark. The algorithms have been validated on a laboratory-scale experimental apparatus. The following conclusions are drawn from the experimental results: (1) superiority of the proposed STSA method over standard methods in STSA for capturing the dynamical behavior of the underlying process, based on short-length time series and (2) superiority of the proposed HMM likelihood-based algorithm over the proposed STSA method for different lengths of sensor time series. | |
publisher | American Society of Mechanical Engineers (ASME) | |
title | Hidden Markov Modeling-Based Decision-Making Using Short-Length Sensor Time Series | |
type | Journal Paper | |
journal volume | 141 | |
journal issue | 10 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.4043428 | |
journal fristpage | 104502 | |
journal lastpage | 104502-6 | |
tree | Journal of Dynamic Systems, Measurement, and Control:;2019:;volume( 141 ):;issue: 010 | |
contenttype | Fulltext | |