Automated Feature Selection for Embeddable Prognostic and Health Monitoring (PHM) Architectures

Ginart, Antonio; Barlas, Irtaza; Goldin, Jonathan W.; Dorrity, J.L.

doi:10.1109/autest.2006.283625

Cited by 32 publications

(14 citation statements)

References 10 publications

(10 reference statements)

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“…With the estimated parameters and the following variant of the degradation model (20). These particles can only approximate the a priori PDF of the state.…”

Section: The Improved Exponential Modelmentioning

confidence: 99%

“…Therefore, the FPT in the exponential model is generally selected subjectively, which restricts the applications of the exponential model. Several approaches for selecting FPT have been reported in literature, such as the engineering norm ISO 10816 and the approach based on the longest time constant of a machine and statistical properties of a candidate baseline [20], [21]. In these approaches, the FPT is selected based on an alarm, which is set according to the statistical properties of large numbers of systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

Lei

Lin

et al. 2015

IEEE Trans. Ind. Electron.

499

234

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The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. The exponential model is one of the most widely used methods for RUL prediction of rolling element bearings. However, two shortcomings exist in the exponential model: (1) the first predicting time (FPT) is selected subjectively; and (2) random errors of the stochastic process decrease the prediction accuracy. To deal with these two shortcomings, an improved exponential model is proposed in this paper. In the improved model, an adaptive FPT selection approach is established based on the 3σ interval and particle filtering is utilized to reduce random errors of the stochastic process. In order to demonstrate the effectiveness of the improved model, a simulation and four tests of bearing degradation processes are utilized for the RUL prediction. The results show that the improved model is able to select an appropriate FPT and reduce random errors of the stochastic process. Consequently, it performs better in the RUL prediction of rolling element bearings than the original exponential model. Index Terms-Remaining useful life prediction, rolling element bearings, exponential model, first predicting time, particle filtering. He is currently a full professor of control engineering and the head of the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Duisburg, Germany. His research interests are model-based and data-driven fault diagnosis, fault tolerant systems, real-time control, and their application in industry with a focus on automotive systems and chemical processes.

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“…With the estimated parameters and the following variant of the degradation model (20). These particles can only approximate the a priori PDF of the state.…”

Section: The Improved Exponential Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

Lei

Lin

et al. 2015

IEEE Trans. Ind. Electron.

499

234

View full text Add to dashboard Cite

show abstract

“…However, the interaction between the statistical features makes the SPT point unstable and reduces the prediction accuracy of the RUL. A. Ginart et al [12] used a large number of statistical features to determine the SPT point. The baselines of acceptance or rejection are created using the maximum time constant of the system based on various features, and then the SPT points are determined by selecting the least baseline.…”

Section: Introductionmentioning

confidence: 99%

Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Gao

Wu³

et al. 2019

Electronics

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Aimed at addressing the problem that the subjective selection of start prediction time (SPT) in rolling bearing remaining useful life (RUL) prediction will lead to excessive noise in the prediction signal, a linear-regression-based SPT point determination was proposed. The sliding window linear regression method was used to establish sliding windows in the root mean square (RMS) range to obtain the RMS gradient domain. The threshold for the RMS gradient was set, and the continuous trigger threshold mechanism to determine the SPT point was used. The experimental results show that the linear-regression-based method can adaptively determine the SPT point and improve the accuracy of life prediction.

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“…Various fault trigger mechanisms have been designed to deal with this problem. Ginart et al [ 12 ] presented a fault trigger mechanism based on constant alarm threshold for fault detection of rotating machinery. Li et al [ 7 ] detected gearbox faults using a probability trigger mechanism, i.e., if more than a given percentage of features are greater than a fixed fault detection threshold, then it is believed that the incipient fault happens.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

Xue

Lei

2017

Materials

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Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

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Automated Feature Selection for Embeddable Prognostic and Health Monitoring (PHM) Architectures

Cited by 32 publications

References 10 publications

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

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