Fault Diagnostics of Helicopter Gearboxes Based on Multi-Sensor Mixtured Hidden Markov Models

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Zhongsheng Chen ◽  
Yongmin Yang
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Fault Diagnostics ◽  
Learning Mechanisms ◽  
Safe Operation ◽  
Accurate Identification ◽  
Observation Sequence ◽  
Sensor Signals ◽  
Non Gaussian

Accurate identification of faults in gearboxes is of vital importance for the safe operation of helicopters. Although hidden Markov models (HMMs) with Gaussian observations have been successfully used for fault diagnostics of mechanical systems, a Gaussian HMM must assume that the observation sequence is generated from a Gaussian process. Conversely, vibration signals from helicopter gearboxes are often non-Gaussian and non-stationary. Also, it always needs to use multi-sensors for more accurate fault diagnostics in practice. Thus, a classical Gaussian HMM may not meet the need of helicopter gearboxes, and it needs to study novel HMMs to model multi-sensor, non-Gaussian signals. This paper presents a multi-sensor mixtured HMM (MSMHMM), which is built on multi-sensor signals. For a MSMHMM, each sensor signal will be considered as the mixture of non-Gaussian sources, so it can depict non-Gaussian observation sequences very well. Then, learning mechanisms of MSMHMM parameters are formulated in detail based on the expectation-maximization (EM) algorithm and a framework of MSMHMM-based fault diagnostics is proposed. In the end, the proposed method is validated on a helicopter gearbox, and the results are very exciting.

Extending hidden Markov models to allow conditioning on previous observations

2018 ◽  
Vol 16 (05) ◽  
pp. 1850019 ◽  
Author(s):  
Ioannis A. Tamposis ◽  
Margarita C. Theodoropoulou ◽  
Konstantinos D. Tsirigos ◽  
Pantelis G. Bagos
Keyword(s):  
Hidden Markov Models ◽  
Probabilistic Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Simple Extension ◽  
Computational Molecular Biology ◽  
Current State ◽  
Observation Sequence ◽  
Hidden States

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%–8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

scholarly journals Statistical Skimming of Feature Films

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Sergio Benini ◽  
Pierangelo Migliorati ◽  
Riccardo Leonardi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Statistical Framework ◽  
Feature Films ◽  
Observation Sequence ◽  
Visual Concepts ◽  
A Chain ◽  
Film Genres ◽  
Temporal Dependencies

We present a statistical framework based on Hidden Markov Models(HMMs)for skimming feature films. A chain ofHMMsis used to model subsequent story units:HMMstates represent different visual-concepts, transitions model the temporal dependencies in each story unit, and stochastic observations are given by single shots. The skim is generated as an observation sequence, where, in order to privilege more informative segments for entering the skim, shots are assigned higher probability of observation if endowed with salient features related to specific film genres. The effectiveness of the method is demonstrated by skimming the first thirty minutes of a wide set of action and dramatic movies, in order to create previews for users useful for assessing whether they would like to see that movie or not, but without revealing the movie central part and plot details. Results are evaluated and compared through extensive user tests in terms of metrics that estimate the content representational value of the obtained video skims and their utility for assessing the user's interest in the observed movie.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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