A generalized interval probability-based optimization method for training generalized hidden Markov model

Xie, Fei; Wu, Bo; Hu, Youmin; Wang, Yan; Jia, Guangfei; Cheng, Yao

doi:10.1016/j.sigpro.2013.06.009

Cited by 10 publications

(10 citation statements)

References 24 publications

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“…Additional signal processing tools such as wavelet transform and Hilbert-Huang transform (HHT) analyses are needed to discover the relations between frequency features and machine states in-depth other than only centroids and peaks. Wavelet transform is one of the classical and widely used time-frequency analysis tool for non-stationary signals in manufacturing process monitoring [8,13,59]. Similarly, HHT performs time-frequency-energy analysis based on empirical mode decomposition and Hilbert spectral analysis for non-stationary signals [21,22].…”

Section: The Study Of Two Normal Statesmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closedloop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a nonintrusive diagnostic and prognostic tool for FDM machine maintenance and process control.

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Section: The Study Of Two Normal Statesmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

Self Cite

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“…In addition, thermography images [42], multi-modal medical image [43] and protein sequence identification [44] were processed by using HMM combined with fuzzy set and fuzzy clustering algorithms. Recently Xie, et al [45] introduced a generalized hidden Markov model (GHMM) for solving the problems of aleatory and epistemic uncertainties. Zhao et al [46] developed FHMM for image segmentation, and Voronoi tessellation (VT) and hidden Markov random field (HMRF) based fuzzy c-means (FCM) algorithm (VTHMRF-FCM) for texture image segmentation and obtain superior results than other FCM based methods.…”

Section: Introductionmentioning

confidence: 99%

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Wang

Dey

et al. 2019

Biocybernetics and Biomedical Engineering

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Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of Diabetes mellitus patients. In this research, texture-based features of the Angular Second Moment (ASM), Moment of Inertia (MI), Inverse Difference Monument (IDM), and Entropy (E) have been selected and fused by using the an up-down algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved Fuzzy Hidden Markov Model (FHMM). In FHMM, type-I fuzzy set is proposed and Fuzzy Baum-Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back-forward algorithms and different fusion operations in FHMM. Improved HMMs with up-down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry.

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“…However, the neural network is often limited by the distribution and amount of training data (Lee et al, 2004), the rough set has a crude way of knowledge expression and reasoning strategy, and the expert systems cannot be applied universally owing to the poor robustness (Tony and Liang, 2011). In recent research, hidden Markov model (HMM) (Xie et al, 2014; Zong et al, 2014) has been proven effective diagnosis model to address the problem of system fault diagnosis in different industrial processes, such as the incipient faults of gear (Kang and Zhang, 2011), the chemical precess diagnosis (Li et al, 2014), the bearing diagnosis (Xu et al, 2015), and so forth. Specifically, the HMM demonstrates its rationality and practicability on its model structure.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of sucker rod pumping based on dynamometer card decomposition and hidden Markov model

Zheng

Gao

2018

Transactions of the Institute of Measurement and Control

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In oil field production, dynamometer card is the key source of information to analyze the down-hole operating conditions of sucker rod pumping. However, under different operating conditions, most of the existing diagnostic technologies are incapable to extract features from dynamometer cards automatically and comprehensively. Based on the mechanism analysis of dynamometer card, a useful diagnostic method with novel feature extraction method is proposed for diagnosing the operating condition of sucker rod pumping. A novel barycentric decomposition strategy is applied to divide the dynamometer cards, which can automatically adjust divided regions according to the shape change of dynamometer card. The curve moments are extracted from the parts of divided results to obtain the comprehensive features for the follow-up algorithm. Subsequently, the hidden Markov model with mixture density function is designed as a classifier to map the relationship between the operating condition and the features of dynamometer card. This technique is successfully carried out in a fault dynamometer card atlas that is collected from productive field. Finally, the experimental results confirm the validity of the proposed diagnosis approach.

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A generalized interval probability-based optimization method for training generalized hidden Markov model

Cited by 10 publications

References 24 publications

In situ monitoring of FDM machine condition via acoustic emission

In situ monitoring of FDM machine condition via acoustic emission

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Diagnosis of sucker rod pumping based on dynamometer card decomposition and hidden Markov model

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