Hidden semi-Markov models for predictive maintenance
type: Item
instance of: scholarly article
title: Hidden semi-Markov models for predictive maintenance
zbMATH Open document ID: 1393.60103
DOI: 10.1155/2015/278120
author: Francesco Cartella
author: Jan Lemeire
author: Luca Dimiccoli
author: Hichem Sahli
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.
Mathematics Subject Classification ID: 60K15
zbMATH DE Number: 6925933
Wikidata QID: Q59117828
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/278120
OpenAlex ID: W2024123627
cites work: A survey of maintenance policies of deteriorating systems
cites work: Q4406016
cites work: Hidden semi-Markov models
cites work: Practical implementation of an efficient forward-backward algorithm for an explicit-duration hidden Markov model
cites work: A hidden semi-Markov model with duration-dependent state transition probabilities for prognostics
cites work: Offline and online identification of hidden semi-Markov models
cites work: Model-Based Clustering, Discriminant Analysis, and Density Estimation
cites work: Inference in hidden Markov models.
cites work: Q4864761
cites work: Estimating the order of a hidden markov model
cites work: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
cites work: Q4139463
description: scientific article; zbMATH DE number 6925933