Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process

Gámiz, María Luz; Navas‐Gómez, Fernando; Miranda, Rocío Raya; Segovia-García, María del Carmen

doi:10.1016/j.ress.2023.109498

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…To solve the problem of significant deviation between HMMs and actual system health diagnosis, Liu et al [ 21 ] developed an improved degenerated HMM with a core of the quasi-power relation. Gámiz et al [ 22 ] used HMMs to describe the evolution-in-time of a system and to estimate its characteristics when direct observations of the system state are not available. The above work optimized the parameter solving algorithm and application mode of the model, but did not overcome the structural limitations of the model.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

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Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.

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Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

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A novel operational reliability and state prediction method based on degradation hidden Markov model with random threshold

Jiang,

Chen,

Lei

2024

Quality & Reliability Eng

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Bearings are widely used as a common mechanical component and significantly impacts the reliability and safety of various engineering equipment. In the field of large‐scale equipment such as wind turbines, statistical data for reliability and state prediction of bearings is usually limited, leading to the inapplicability of traditional statistical methods. Besides, individual differences commonly exist among bearings, and the appropriate failure threshold for a specific bearing is difficult to be determined due to the individual uncertainty, which may induce prediction errors. To address these issues, a novel operational reliability and state prediction method based on random threshold degradation hidden Markov model was proposed in the study. The traditional hidden Markov model was improved by considering the effect of performance degradation on state transition probability matrix. Moreover, random failure thresholds of performance degradation were employed to describe individual differences between bearings. The operational reliability and state of bearings was obtained by comprehensive analysis of prediction results under different failure thresholds. Verification of the proposed operational reliability and state prediction method were conducted using PHM2012 bearing operation data. The results underscored the effectiveness of the proposed method in achieving a comparatively high accuracy in operational reliability prediction without the prerequisite of an exact failure threshold, when contrasted with several established reliability and life prediction techniques.

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RAMD and transient analysis of a juice clarification unit in sugar plants

Saini,

Kumar

et al. 2024

Int. j. inf. tecnol.

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Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process

Cited by 5 publications

References 29 publications

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

A novel operational reliability and state prediction method based on degradation hidden Markov model with random threshold

RAMD and transient analysis of a juice clarification unit in sugar plants

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