Hidden Markov Models With Covariates for Analysis of Defective Industrial Machine Parts

Sirima, Pornpit; Pokorný, Přemysl

doi:10.3844/jmssp.2014.322.330

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Islam and Chowdhury [48] studied a covariate dependent Markov model and provided a comprehensive explication for the higher order. Sirima and Pokorny [49] proposed a two-state hidden Markov model (HMM) to describe the effect covariate such as machine part error correction and also the time spent on the error correction of defective industrial machine parts. Other researchers also studied the modelling of the covariates using the Markov chain [50,51].…”

Section: Introductionmentioning

confidence: 99%

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

2021

View full text Add to dashboard Cite

Recently, maintaining a complex mechanical system at the appropriate times is considered a significant task for reliability engineers and researchers. Moreover, the development of advanced mechanical systems and the dynamics of the operating environments raises the complexity of a system’s degradation behaviour. In this aspect, an efficient maintenance policy is of great importance, and a better modelling of the operating system’s degradation is essential. In this study, the non-monotonic degradation of a centrifugal pump system operating in the dynamic environment is considered and modelled using variance gamma stochastic process. The covariates are introduced to present the dynamic environmental effects and are modelled using a finite state Markov chain. The degradation of the system in the presence of covariates is modelled and prognostic results are analysed. Two machine learning algorithms k-nearest-neighbour (KNN) and neural network (NN) are applied to identify the various characteristics of degradation and the environmental conditions. A predefined degradation threshold is assigned and used to propose a prognostic result for each classification state. It was observed that this methodology shows promising prognostic results.

show abstract

Section: Introductionmentioning

confidence: 99%

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

2021

View full text Add to dashboard Cite

show abstract

“…Reluctance motor was successfully diagnosed for fault by classification method using hidden Markov Model by Lerner et al [29] while industrial machines was considered in Ref. [30].…”

Section: Introductionmentioning

confidence: 99%

Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model

Omoregbee

Heyns

2018

J Mech Sci Technol

View full text Add to dashboard Cite

This paper uses Bayesian robust new hidden Markov modeling (BRNHMM) for bearing fault detection and diagnosis based on its acoustic emission signal. A variational Bayesian approach is used that simultaneously approximates the distribution over the hidden states and parameters with simpler distribution hence using Bayesian inference for the estimation of the posterior HMM hyperparameters. This allows for online detection as small data sets can be used. Also, the Kullback-Leibler (KL) divergence is effectively used to access the divergence of the probability function of the BRNHMM, to find its lower bound approximation and by applying a linear transform to the maximum output probability parameter generation (MOPPG). The training set result obtained from BRNHMM is then compared to the result from artificial neural network (ANN) fault detection for same complex system of low speed and varying load conditions which are difficult from a diagnostic perspective, as found in rolling mills.

show abstract

Hidden Markov Models With Covariates for Analysis of Defective Industrial Machine Parts

Cited by 2 publications

References 15 publications

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model

Contact Info

Product

Resources

About