Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

Dong, Ming; He, David

doi:10.1016/j.ejor.2006.01.041

Cited by 239 publications

(149 citation statements)

References 26 publications

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“…Here, we will extend our previous work [29] in which we derived Bayesian online classifier using vector autoregressive hierarchical hidden Markov models (VARHHMM), with a classifier based on vector autoregressive hierarchical hidden semi-Markov models (VARHHSMM) [26][27][28]. The flowchart for the classifier's components is shown in Figure 4.…”

Section: Classification Algorithmmentioning

confidence: 99%

“…As one of the approaches for decoding finger movements using only multiple EMG signals, in this study we propose a novel classification algorithms based on the combination of EMG feature extraction and piecewise modeling of the feature temporal dynamics, incorporated in hierarchical hidden semi-Markov models (HHSMM) [26][27][28] and an online Bayesian classifier implemented through model inversion. We developed and assessed two variations of the aforementioned method and included special cases of these algorithms with reduced computational requirements.…”

Section: Introductionmentioning

confidence: 99%

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Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

et al. 2018

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We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.

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Section: Classification Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

et al. 2018

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“…HSMM has overcome the strict geometric distribution restriction of HMM when describing state duration and the duration of any distribution can be depicted. In recent years, HSMM is extensively applied to voice recognition [9] and failure prediction [10]. Therefore, HSMM is suitable for modeling and prediction of HSC system.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Anomaly in the Behavior Characteristics of Operators in HSMM-based Multi-UAV Supervisory Control System

Zhang¹,

Yuan²,

Ma³

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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： Solo operation of multi-unmanned Aerial Vehicle (UAV) will be the trend of future, and operators play a significant role in the UAV supervisory control system, but human may be impacted by all kinds of factors, which may decline the performance of UAV system. In this paper, modeling is conducted for the behavioral characteristics of operators in the multi-UAV supervisory control system with improved Hidden semi-Markov Model (HSMM), and the relationship between the predication result and anomaly of HSMM model is analyzed. This method can be applied for monitoring the anomaly in the behaviors of operators, which is of strong and practical significance in the multi-UAV supervisor control system and Human Supervisory Control (HSC) system.

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“…For example, the conventional observer approach that can be envisaged for studying determinist systems prognosis is highlighted in [14]. The second type is dedicated to the stochastic models (see, e.g., [15,16]) and Bayesian filters [17,18]. For stochastic models, which attract our attention, the prediction techniques can be classified according to their natures or uncertainties.…”

Section: Introductionmentioning

confidence: 99%

The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter

Ouladsine

Outbib

Bakhouya

2017

International Journal of Photoenergy

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The degradation of photovoltaic (PV) modules remains a major concern on the control and the development of the photovoltaic field, particularly, in regions with difficult climatic conditions. The main degradation modes of the PV modules are corrosion, discoloration, glass breaks, and cracks of cells. However, corrosion and discoloration remain the predominant degradation modes that still require further investigations. In this paper, a model-based PV corrosion prognostic approach, based on an ensemble Kalman filter (EnKF), is introduced to identify the PV corrosion parameters and then estimate the remaining useful life (RUL). Simulations have been conducted using measured data set, and results are reported to show the efficiency of the proposed approach.

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Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

Cited by 239 publications

References 26 publications

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Analysis of Anomaly in the Behavior Characteristics of Operators in HSMM-based Multi-UAV Supervisory Control System

The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter

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