Degradation Assessment for Machinery Prognostics Using Hidden Markov Models

2005 ◽  
Author(s):  
Hai Qiu ◽  
Haitao Liao ◽  
Jay Lee
Keyword(s):  
Hidden Markov Models ◽  
Prior Knowledge ◽  
Probabilistic Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Degradation Process ◽  
Full Understanding ◽  
Degradation Processes ◽  
Degradation Pattern ◽  
Detection And Recognition

Degradation detection and recognition of degradation pattern are crucial to the successful deployment of prognostics. A machine degradation process is known to be stochastic instead of deterministic. Recognizing the degradation pattern needs helps from stochastic and probabilistic models. Among various stochastic approaches. Hidden Markov Models (HMMs) have been proven to be very effective in modeling both dynamic and static signals [1]. In this paper, aiming to providing a guideline of how to effectively and efficiently use the HMMs to assess degradation for various machinery prognostic applications, three different approaches of applying the HMMs are reviewed and compared. It demonstrates that depending on the varieties of applications, available prior knowledge, and characteristics of degradation processes, those three implementation approaches perform differently. A full understanding of the strengths and weaknesses of each deployment approach is extremely important in order to effectively utilize this powerful tool for system degradation assessment.

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/ .

A TUTORIAL ON MARKOV MODELS BASED ON MENDEL'S CLASSICAL EXPERIMENTS

2005 ◽  
Vol 03 (06) ◽  
pp. 1441-1460 ◽  
Author(s):  
STEINAR THORVALDSEN
Keyword(s):  
Nucleic Acids ◽  
Hidden Markov Models ◽  
Prior Knowledge ◽  
Probabilistic Model ◽  
Markov Models ◽  
Hidden Markov ◽  
Biological Sequences ◽  
Dual Nature ◽  
Background Theory ◽  
Series Of Experiments

Hidden Markov Models (HMM) can be extremely useful tools for the analysis of data from biological sequences, and provide a probabilistic model of protein families. Most reviews and general introductions follow the excellent tutorial by Rabiner,1 where the focus is outside biology. Mendel's famous experiments in plant hybridisation were published in 1866 and are often considered the icebreaking work of modern genetics. He had no prior knowledge of the dual nature of genes, but through a series of experiments he was able to anticipate the hidden concept and name it "Elemente". In this paper we present the background, theory and algorithms of HMM based on examples from Mendel's experiments, and introduce the toolbox "mendelHMM". This approach is considered to have some intuitive advantages in a biological and bioinformatical setting. Applications to analysing bio-sequences like nucleic acids and proteins are also discussed.

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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