Prognostics of rotating machines through generalized Gaussian hidden Markov models

Soave, Elia; D’Elia, Gianluca; Dalpiaz, Giorgio

doi:10.1016/j.ymssp.2022.109767

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Based on (17), the inverse of the augmented quaternion valued covariance matrix expressed in terms of the real valued quadrivariate covariance matrix is…”

Section: Definitions a Augmented Quaternion Statisticsmentioning

confidence: 99%

“…Many articles have been published presenting the applications of GGD in many research areas, e.g., in image segmentation [9], stereoscopic images [10], image synthesis [11], the binarization of historical degraded document images [12], image compression [7], [13], [14], in testing adaptive filters [15], and the vibroacoustic method of detecting damage to the power transformer core [16]. In [17] the probabilistic function as a mixture of generalized Gaussian distributions was built in order to predict the failure of rotating machines. Thus, the generalized Gaussian hidden Markov model (GGHMM) was created for machinery prognostic purposes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum Likelihood Estimators of Generalized Gaussian Distribution With an ℍ-Proper Quaternion Random Variable

Krupiński,

Marciniak,

Oyerinde

2024

IEEE Access

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The currently known generalized Gaussian distributions (GGD) with an augmented quaternion random variable are based on the 3D GGD distribution. That is, they are based on random variables with three components. In this article, GGD for an augmented quaternion random variable based on GGD for a random variable with four components is given. Then the distribution obtained is simplified for an Hproper quaternion random variable. For this simplified distribution the maximum likelihood estimators are presented. The simplification allows the presentation of the ML estimator for the shape parameter of GGD by eliminating the coefficients related to the covariance from the non-linear equation. The performance of the derived ML estimators is examined in the simulations.INDEX TERMS Augmented quaternion statistics, maximum likelihood method, quaternion generalized Gaussian distribution, quaternion random variable.

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“…Based on (17), the inverse of the augmented quaternion valued covariance matrix expressed in terms of the real valued quadrivariate covariance matrix is…”

Section: Definitions a Augmented Quaternion Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimators of Generalized Gaussian Distribution With an ℍ-Proper Quaternion Random Variable

Krupiński,

Marciniak,

Oyerinde

2024

IEEE Access

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show abstract

“…In this field, Medjaher et al [19] used GMM-HMM for bearing RUL estimation. Soave et al [20] proposed a generalised GMM-HMM model for rotating machinery prognosis. Zhao et al [21] developed a multi-feature HMM model for bearing diagnosis and prognosis.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling

Galli,

Weber,

Hoblos

et al. 2024

Machines

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Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for ball bearing RUL (Remaining Useful Life) estimation. The proposed methodology is composed of three main steps: HI (Health Indicator) construction, bearing diagnosis and RUL estimation. The HI is obtained by processing non-stationary vibration data with the MODWPT (Maximum Overlap Discrete Wavelet Packet Transform). After that, a degradation profile is defined and coupled with crack initiation and crack propagation fatigue models. Lastly, a MB-HMM (Hidden Markov Model) is trained to capture the bearing degradation dynamics. This latter model is used to estimate the current degradation state as well as the RUL. The obtained results show good RUL prediction capabilities. In particular, the fatigue models allowed a reduction of the ML (Machine Learning) model size, improving the algorithms training phase.

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Physics-based prognostics of rolling-element bearings: The equivalent damaged volume algorithm

Gabrielli,

Battarra,

Mucchi

et al. 2024

Mechanical Systems and Signal Processing

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Prognostics of rotating machines through generalized Gaussian hidden Markov models

Cited by 7 publications

References 32 publications

Maximum Likelihood Estimators of Generalized Gaussian Distribution With an ℍ-Proper Quaternion Random Variable

Maximum Likelihood Estimators of Generalized Gaussian Distribution With an ℍ-Proper Quaternion Random Variable

Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling

Physics-based prognostics of rolling-element bearings: The equivalent damaged volume algorithm

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