Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

Chen, Shaowei; Wang, Meinan; Huang, Dengshan; Wen, Peng; Wang, Shengyue; Zhao, Shengzhi

doi:10.1109/access.2020.3041682

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…The deeper the architecture of the predictor, the more effective it is for accurate modelling for posterior estimation; however, this often leads to an increase in the number of parameters required for optimum prognostics results. Consequently, the need for optimal parameter selection arises [10], [13]. Traditionally, such selection depend on the analyst's intuition.…”

Section: Choice Of Prognostics Algorithm and Parameter Optimizationmentioning

confidence: 99%

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

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The remaining useful life (RUL) prediction of industrial cyber-physical system components demands the use of reliable prognostics parameters and frameworks. Against the traditional use of a single measure of degradation, data from multiple sensors provide abundant characteristic information for modeling, assessing, and extracting useful parameters via appropriate signal processing and sensor fusion methods. This study introduces a multi-sensor prognostics approach which merges highly prognosible statistical features from vibrational and pressure sensor measurements after a multi-level wavelet decomposition of the signals. The prognostic algorithm presented in this work for solenoid pump RUL prediction is a multi-objective genetic algorithm-optimized long short-term memory (MOGA-LSTM) which accepts the fused sensor features as input and returns the RUL of the pump as output. The framework was tested on a run-to-failure experiment on a VSC63A5 Solenoid pump following a significant pump malfunction caused by a clogged suction filter after the test. Using standard prognostic performance evaluation metrics, the performance of the prognostics framework was compared with other reliable state-of-the-art methods with a remarkable comparative advantage in addition to better automation potentials for real-time condition monitoring and RUL prediction.

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Section: Choice Of Prognostics Algorithm and Parameter Optimizationmentioning

confidence: 99%

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

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“…We use Eq. (15) to construct the SV's whole life cycle degradation data set. In order to obtain the real degradation of the SV, the Gaussian filtering and the Exponential smoothing is adopted.…”

Section: Rul Estimation Of the Sv Based On Apf Techniquementioning

confidence: 99%

“…The RUL estimation based on the physical failure model is ineffective when dealing with large and complex nonlinear multi-operation equipment systems, while the data-driven method is the leading research direction of RUL estimation in recent years [4]. These data-driven methods involve support vector machines [5,6,7], neural networks [8,9,10] and particle filters(PF) [11,12,13,14,15]. When dealing with nonlinear non-Gaussian noise systems, the PF technique has excellent performance [16].…”

Section: Introductionmentioning

confidence: 99%

A remaining useful life estimation method for solenoid valve based on mmWave radar and auxiliary particle filter technique

Liu

et al. 2021

IEICE Electron. Express

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Solenoid valves(SVs) as a kernel component are widely used in various control systems. Through accurately predicting the remaining useful life (RUL) of the SV, predictable maintenance its can be implemented to reduce system maintenance costs and system reliability improves. This paper proposes a frequency modulated continuous wave (FMCW) based on millimeter-wave radar and the auxiliary particle filter (APF) technique to estimate the RUL of SVs. Firstly, a 77GHz FMCW millimeter-wave radar with a single input and multiple outputs(SIMO) is used to obtain the core displacements of two SVs with the same frequencies. A degradation experiment is designed, and the distortion degree of the core displacement is taken as the degradation indicator to construct a degradation data set. Secondly, the APF technique is used to estimate the degradation state and RUL of the SV. Finally, an experiment platform is built using TI's millimeter-wave radar to verify the method proposed in this paper. The experimental results show that the RUL estimation results are satisfactory.

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“…The challenge is effectively integrating data from multiple sensors to achieve more accurate degradation modeling and predictive analysis [3]. Chen et al [4] utilized genetic algorithms to fuse multi-source data for RUL prediction. In subsequent research, Chen et al [5] achieved RUL prediction by fusing stochastic process models for each performance indicator.…”

Section: Introductionmentioning

confidence: 99%

The RUL prediction based on improved Wiener degradation model for wet friction components

Wu,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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The wet clutch is a critical component of the comprehensive transmission system, and its reliability determines the stability of heavy equipment operation. As the core component of the wet clutch, the life of the wet friction components directly affects the reliability of the wet clutch. To accurately predict the wet friction components remaining useful life (RUL), an improved Wiener degradation model is established. Compared to traditional models, it sets drift parameters as random parameters to consider the randomness of degradation process. The life and degradation data of wet friction components are obtained through accelerated life test, and the composite degradation indicator is constructed by screening and fusing degradation data. Innovatively, we fuse life and degradation data to reduce early errors in RUL prediction of wet friction components. The result shows that the RUL prediction average absolute error of the improved Wiener degradation model is 0.31h in the accelerated life range of 80h to 110h, which verifies the accuracy of the model.

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Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

Cited by 9 publications

References 28 publications

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

A remaining useful life estimation method for solenoid valve based on mmWave radar and auxiliary particle filter technique

The RUL prediction based on improved Wiener degradation model for wet friction components

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