A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

Dong, Ming; He, David

doi:10.1016/j.ymssp.2006.10.001

Cited by 279 publications

(173 citation statements)

References 21 publications

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“…2) Data-driven prognostic: this approach consists in transforming the monitoring data provided by the sensors installed on the system into reliable behavioral models of the degradations [3], [11]. The collected data are first processed in order to extract relevant features.…”

Section: )mentioning

confidence: 99%

“…Previous research works related to failure diagnostic and prognostic methods based on HMMs and their variants [11], [15] have been developed. The proposed methods are done in two phases: a learning phase and an exploitation phase.…”

Section: Based On Hmmsmentioning

confidence: 99%

“…This assumption is a source of inaccurate duration modeling because most of real-life applications do not obey this function. Thus, to solve this problem a model with explicit time durations, called Hidden semi Markov Model, has been proposed [11]. The learning and exploitation phase using this model are performed as follows.…”

Section: B the Hsmms Casementioning

confidence: 99%

“…The HSMMs permits to estimate the RUL in two ways: a) By using the expression proposed in [11] where the stay durations are merged with the state probability transitions:…”

Section: ) Learning Phasementioning

confidence: 99%

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Hidden Markov Models for failure diagnostic and prognostic

Diego

Medjaher

Zerhouni

et al. 2011

2011 Prognostics and System Health Managment Confernece

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Abstract-This paper deals with an estimation of the Remaining Useful Life of bearings based on the utilization of Mixture of Gaussians Hidden Markov Models (MoG-HMMs). The raw signals provided by the sensors are first processed to extract features, which permit to model the physical component and its degradation. The prognostic process is done in two phases: a learning phase and an evaluation phase. During the first phase, the sensors' data are processed in order to extract appropriate and useful features, which are then used as inputs of dedicated learning algorithms in order to estimate the parameters of a MoG-HMM. The obtained model represents the behavior of the component including its degradation. In addition, the model contains the number of health states and the stay durations in each state. Once the learning phase is done, the generated model is exploited during the second phase, where the extracted features are continuously injected to the learned model to assess the current health state of the physical component and to estimate its remaining useful life and the associated confidence. The proposed method is tested on a benchmark data taken from the "NASA prognostic data repository" related to bearings used under several operating conditions. Moreover, the developed method is compared to two methods: the first using traditional HMMs with exponential time durations and the second using regular Hidden Semi Markov Model (HSMM). Finally, simulation results are given and discussed at the end of the paper.

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Section: )mentioning

confidence: 99%

Section: Based On Hmmsmentioning

confidence: 99%

Section: B the Hsmms Casementioning

confidence: 99%

“…The HSMMs permits to estimate the RUL in two ways: a) By using the expression proposed in [11] where the stay durations are merged with the state probability transitions:…”

Section: ) Learning Phasementioning

confidence: 99%

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Hidden Markov Models for failure diagnostic and prognostic

Diego

Medjaher

Zerhouni

et al. 2011

2011 Prognostics and System Health Managment Confernece

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“…Levinson (1986)); engineering (e.g. Dong and He (2007)); climate (e.g. Sansom and Thomson (2001)); finance (e.g.…”

Section: Introductionmentioning

confidence: 99%

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

2013

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Hidden Markov models (HMMs) are flexible, wellestablished models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity.Keywords HSMM · random effects · MCMC · recursive algorithms · Bayesian model · water pipes.

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2016

From Prognostics and Health Systems Management to Predictive Maintenance 1

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A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

Cited by 279 publications

References 21 publications

Hidden Markov Models for failure diagnostic and prognostic

Hidden Markov Models for failure diagnostic and prognostic

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

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