Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model

Gao, Zhiyong; Jiwu, Li; Wang, Rongxi

doi:10.17531/ein.2021.1.16

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…Once the fnal degradation state is reached, they extract the degradation features and obtain the survival function through the ftted PHM. Te proposed methods present higher accuracy than traditional methods [23]. Te studies confrm the role of HMM in the feld of fault detection and prediction.…”

Section: Introductionmentioning

confidence: 88%

Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model

Jiarula

Zhang

et al. 2022

International Journal of Chemical Engineering

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Hidden Markov models (HMMs) have been recently used for fault detection and prediction in continuous industrial processes; however, the expected maximum (EM) algorithm in the HMM has local optimality problems and cannot accurately find the fault root cause variables in complex industrial processes with high-dimensional data and strong variable coupling. To alleviate this problem, a hidden Markov model-Bayesian network (HMM-BN) hybrid model is proposed to alleviate the local optimum problem in the EM algorithm and diagnose the fault root cause variable. Firstly, the model introduces expert empirical knowledge for constructing BN to accurately diagnose the fault root cause variable. Then, the EM algorithm is improved by sequential and parallel learning to alleviate the initial sensitivity and local optimum problems. Finally, the log-likelihood estimates (LL) calculated by the improved hidden Markov model provide empirical evidence for the BN and give fault detection, prediction, and root cause variable detection results based on information about the similar increasing and decreasing patterns of LL for the training data and the online data. Combining the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process, the feasibility and effectiveness of the model are verified. The results show that the model can not only find the fault in time but also find the cause of the fault accurately.

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Section: Introductionmentioning

confidence: 88%

Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model

Jiarula

Zhang

et al. 2022

International Journal of Chemical Engineering

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“…Regression models [14][15][16] Markov models [17,18] Proportional hazard models [19,20] Stochastic process-based prediction methods Wiener process [21,22] Gamma process [23,24] Machine learning-based prediction methods…”

Section: Statistical Model-based Prediction Methodsmentioning

confidence: 99%

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

Jia,

An,

et al. 2024

Meas. Sci. Technol.

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In contemporary industrial processes, vibration signals collected from bearings often contain significant noise, challenging the efficacy of conventional predictive models in extracting critical degradation features and accurately predicting the remaining useful life (RUL) of bearings. Addressing these challenges, this paper introduces a novel method for predicting bearing RUL under noisy conditions, leveraging a dual-branch multi-scale convolutional attention network (DMCSA) integrated with a dense residual feature fusion network (DRF). Initially, the method applies continuous wavelet trans-form (CWT) to vibration signals to extract color time-frequency image data, followed by grayscale processing to construct a comprehensive color-grayscale time-frequency image dataset, thereby augmenting the model's input features. Enhanced channel and spatial attention mechanisms, combined with multi-scale convolutions, facilitate supe-rior feature extraction and selection. The model's resilience to noise is fortified by in-corporating noise into the training dataset. Subsequently, selected color-gray time-frequency features undergo fusion and relearning through the DRF framework at the model's backend. The crayfish optimization algorithm (COA) is deployed for the astute determination of the model's critical hyperparameters. The proposed DMCSA-DRF model is then applied to predict the health indicator (MSCA-DRF-HI) of the test dataset, culminating in the accurate prediction of the bearings' RUL. Validation experiments demonstrate that our method surpasses comparative models in prediction accuracy un-der diverse noise interferences, signifying a substantial advancement in predictive performance.

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“…Each node represents a feature in a classification category, and each subset specifies a value the node may accept. Decision trees have several implementation domains due to their easy analysis and accuracy in various data formats [21][22][23].…”

Section: Decision Treementioning

confidence: 99%

Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning

Bensid¹,

Lati²,

Benlamoudi³

et al. 2022

Diagnostyka

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The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation; second, cough signal extraction; and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM.

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Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model

Cited by 17 publications

References 42 publications

Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model

Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning

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