A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data

Liu, Jie; Vitelli, Valeria; Zio, Enrico; Seraoui, Rédouane

doi:10.1109/tr.2015.2427156

Cited by 63 publications

(31 citation statements)

References 41 publications

(49 reference statements)

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“…Specifically in nuclear energy, [23] found success in utilising kernel method-based techniques for prognostics applied to reactor coolant pumps (RCPs) for the pressurized water reactor (PWR) at the component level. While the RCP operates in differing conditions from AGR GC units and the case study differs in nature (the system in [23] specifically concerns leakage), their function and importance as critical primary cycle coolant machines is similar.…”

Section: Machine Learning In Reliability Engineeringmentioning

confidence: 99%

Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring

2017

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The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-Gas circulator (GC) units are an important rotating asset used in the Advanced Gas-cooled Reactor (AGR) design, facilitating the flow of CO2 gas through the reactor core. The ongoing maintenance and examination of these machines is important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of non-steady state behavior at regular refuelling intervals, posing a unique analysis problem for reliability engineers.In line with the increased data volumes and sophistication of available technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-theart approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more-complex but expected events in the data.In this paper we demonstrate a novel modelling approach for examining refuelling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life (RUL) measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring.

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Section: Machine Learning In Reliability Engineeringmentioning

confidence: 99%

Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring

2017

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“…In contrast, the machine learning methods such as artificial neural networks (ANN) [21,22], similarity-based RUL prediction (SbRP) method [11,23] and support vector machine (SVM) [24][25][26][27][28][29][30][31], can effectively avoid these issues. Although the machine learning methods don't provide a probability density function (PDF) estimate of the RUL, they are capable of dealing with prognostic issues of complex systems whose degradation processes are difficult to be interrelated by physics-based methods or degradation-modeling based methods, and have been widely studied and applied in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Benkedjouh et al [27,28] used SVR to map the degradation time series into nonlinear regression, and then fitted it into the power model for RUL prediction of the mechanical equipment. Liu et al [29,30] proposed an improved probabilistic SVR model to predict the RUL of equipment components of a nuclear power plant. Fumeo et al [31] developed an online SVR model to predict the RUL of bearings by optimizing the tradeoff between accuracy and computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction based on a modified SVR method with health stage division, cluster sampling and similarity matching

Yan

Zhong

Cheng

et al. 2020

Preprint

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Remaining useful life (RUL) prediction is an advanced methodology of prognostics and health management (PHM), and is in advantage of life cycles management of equipment and maintenance cost reduction. Among the data driven methods, support vector regression (SVR) is one of the most suitable methods when there are limited failure history data for analysis. However, many uncertain factors such as individual variation and time varying operating conditions, will lead that the failure time of all equipment statistically dispersive, and with the increase of sample set, such dispersity may inevitably increase and further reflects in the model training. In consequent, the dispersity may cause two drawbacks. On the one hand, the linearity of SVR model will increase with the increase of sample set, and overfitting or underfitting tends to occur. And on the other hand, a single model only performs well in its generalization and robustness, but may lost its effectiveness that it may fail to work well for a new on-service equipment. In order to deal with the two drawbacks, this paper proposes a modified SVR method with health stage division, cluster sampling and similarity matching. Through health stage division and cluster sampling, each state of the whole degradation process can obtain the optimal parameters, then the irrelevant linearity can be reduced. In addition, since that similar input results similar output, the optimal parameters of the most similar testing sample are also suitable for the on-service equipment, and through similarity matching the most similar testing sample can be obtained, thus the drawback of a single model can be avoided. Finally, the effectiveness of the proposed method is verified systematically by a simulated dataset of fatigue crack growth and a real-world degradation dataset of GaAs-based lasers.

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“…Related modeling methods are generally divided into four types: multipoint input modeling [5][6][7], dynamic weighting modeling [8,9], feedback network modeling [10,11], and multimodel structure modeling [12][13][14]. Among these types of methods, multipoint input modeling boasts the advantages of simplicity, ease in implementation, and full reflection of the process characteristics.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Dai

2019

Symmetry

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To solve the soft sensor modeling (SSMI) problem in a nonlinear chemical process with dynamic time variation and multi-rate data, this paper proposes a dynamic SSMI method based on an autoregressive moving average (ARMA) model of weighted process data with discount (DSSMI-AMWPDD) and optimization methods. For the sustained influence of auxiliary variable data on the dominant variables, the ARMA model structure is adopted. To reduce the complexity of the model, the dynamic weighting model is combined with the ARMA model. To address the weights of auxiliary variable data with different sampling frequencies, a calculation method for AMWPDD is proposed using assumptions that are suitable for most sequential chemical processes. The proposed method can obtain a discount factor value (DFV) of auxiliary variable data, realizing the dynamic fusion of chemical process data. Particle swarm optimization (PSO) is employed to optimize the soft sensor model parameters. To address the poor convergence problem of PSO, ω-dynamic PSO (ωDPSO) is used to improve the PSO convergence via the dynamic fluctuation of the inertia weight. A continuous stirred tank reactor (CSTR) simulation experiment was performed. The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process. The AMWPDD proposed in this paper can reflect the dynamic change of chemical process and improve the accuracy of SSM data prediction. The ω dynamic PSO method proposed in this paper has faster convergence speed and higher convergence accuracy, thus, these models correlate with the concept of symmetry.

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A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data

Cited by 63 publications

References 41 publications

Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring

Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring

Remaining useful life prediction based on a modified SVR method with health stage division, cluster sampling and similarity matching

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

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