Condition prediction based on wavelet packet transform and least squares support vector machine methods

Zhao, Fengnian; Chen, J; Xu, Weiya

doi:10.1243/09544089jpme220

Cited by 24 publications

(31 citation statements)

References 21 publications

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“…The result interprets that this support vector regression model has better performance than the conventional models. A new health prognostic technique is proposed by Zhao et al, which is based on LS-SVM as well as wavelet packet transform [154]. Using an artificial neural network and support vector regression, Reddy et al, trade with the development of accurate warpage estimation model for plastic injection moulded parts [155].…”

Section: ) Support Vector Machine (Svm) and Support Vector Regressiomentioning

confidence: 99%

Review of Health Prognostics and Condition Monitoring of Electronic Components

et al. 2020

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To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' visa -vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted. INDEX TERMS Reliability methods, condition monitoring, faults and failures, prognostics, diagnostics.

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Section: ) Support Vector Machine (Svm) and Support Vector Regressiomentioning

confidence: 99%

Review of Health Prognostics and Condition Monitoring of Electronic Components

et al. 2020

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“…The final outcome of the study implied that the method has optimal predictive capability. Zhao [13] modified SVM technique by integrating SVM and wavelet function. The attained outcome demonstrated best prediction for the movement of stock market.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

et al. 2020

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The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.

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“…The time-domain and frequency-domain feature indexes of bearing vibration signals are extracted as the input of state prediction model to predict the life trend of rolling bearings [3]. The kurtosis is used as the degradation performance index to establish a rolling bearing residual life prediction model based on SVM [4]. Based on the normalized state deviation, the method of state evaluation and residual life prediction of rolling bearings is constructed [5].…”

Section: Introductionmentioning

confidence: 99%