Review of remaining useful life prediction using support vector machine for engineering assets

Zhang, Longlong; Liu, Zhiliang; Luo, Dashuang; Li, Jing; Huang, Hong‐Zhong

doi:10.1109/qr2mse.2013.6625925

Cited by 16 publications

(9 citation statements)

References 42 publications

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“…57 SVMs are excellent for addressing prognostic problems regarding complex rotating machinery because there are no limitations on the dimensionality of the input vectors and because the computational burden is relatively low. 59 Moreover, SVM-based models have been reported to be capable of handling situations that are highly nonlinear. 60 However, a standard method for choosing an appropriate kernel function for SVMs does not exist, which is problematic.…”

Section: Svm and Rvmmentioning

confidence: 99%

Multidimensional prognostics for rotating machinery: A review

Duan

Mba

et al. 2017

Advances in Mechanical Engineering

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Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and availability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidimensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and drawbacks and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment.

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Section: Svm and Rvmmentioning

confidence: 99%

Multidimensional prognostics for rotating machinery: A review

Duan

Mba

et al. 2017

Advances in Mechanical Engineering

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“…Figure 3 shows the architecture of a simple SVM based prognostic model. SVMs are excellent in addressing prognostic problems regarding complex rotating machinery because they have no limitations on the dimensionality of the input vectors and have relatively a low computational burden [13]. Besides, SVMs can achieve highly accurate results with nonlinear inputs [54].…”

Section: Support Vector Machine Based Modelsmentioning

confidence: 99%

“…Several review papers on prognostic techniques for engineering systems have been published [8] [9] [10] [11] [12] [13]. However, limited numbers of these papers have highlighted the system-level prognostic options for complex rotating machines.…”

Section: Introductionmentioning

confidence: 99%

Rotating Machine Prognostics Using System-Level Models

Duan

David

et al. 2017

Lecture Notes in Mechanical Engineering

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The prognostics of rotating machines is crucial for the reliable and safe operation as well as maximizing usage time. Many reliability studies focus on component-level prognostics. However, in many cases, the desired information is the residual life of the system, rather than the lifetimes of its constituent components. This review paper focuses on system-level prognostic techniques that can be applied to rotating machinery. These approaches use multi-dimensional condition monitoring data collected from different parts of the system of interest to predict the remaining useful life at the system level. The working principles, merits and drawbacks as well as field of applications of these techniques are summarized.

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“…They also provided a synopsis of methods, algorithms, and models used for battery RUL prognosis and SOH approximation with a comprehensive electrochemical approach to statistical methods dependent on data. The same year, [24] published a review on applications and enhancement algorithms of using a support vector machine (SVM) to estimate RUL. Those reviews were comprehensive at the time of publication, but the rapid extension of research on LIB RUL prognosis in the past few years necessitates further review.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

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Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.INDEX TERMS Battery datasets, battery data repository, remaining useful life (RUL), battery management, li-ion battery, RUL prediction methods.

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Review of remaining useful life prediction using support vector machine for engineering assets

Cited by 16 publications

References 42 publications

Multidimensional prognostics for rotating machinery: A review

Multidimensional prognostics for rotating machinery: A review

Rotating Machine Prognostics Using System-Level Models

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

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