A survey on life prediction of equipment

Hu, Changhua; Zhou, Zhijie; Zhang, Jianxun; Si, Xiaosheng

doi:10.1016/j.cja.2014.12.020

Cited by 32 publications

(15 citation statements)

References 54 publications

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“…Where, m is the shape parameter,  is the scale parameter, which also represents the characteristic life of the product. In addition, the probability density function under the two parameter Weibull distribution is shown in formula (19), and the mean time to failure (MTTF) is shown in formula (20).…”

Section: Life Prediction Of External Gear Pumpmentioning

confidence: 99%

Remaining Useful Life Prediction Based on the Bayesian Regularized Radial Basis Function Neural Network for an External Gear Pump

Guo

Li-jiang

et al. 2020

IEEE Access

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This paper explores an external gear pump's prediction of the remaining useful life (RUL). Moreover, based on Bayesian regularized radial basis function neural network (Trainbr-RBFNN) it proposes a RUL prediction method. The variational mode decomposition (VMD) algorithm has been used to denoise the vibration data of accelerated degradation test, followed by which, using the Hilbert modulation the reconstructed signal has been demodulated. After which, compared with the ensemble empirical mode decomposition (EEMD) algorithm and the modified ensemble empirical mode decomposition (MEEMD) algorithm. Subsequently, factor analysis (FA) has been selected to realize the fusion of various characteristic parameters, after which, the external gear pump's degradation evaluation index established and analyzed. Finally, the degradation evaluation index has been used to train the Trainbr-RBFNN model, and achieve gear pump degradation evaluation model for RUL prediction. Experiment results evidence that the RUL of the external gear pump can be accurately evaluated with the method used. INDEX TERMS External gear pump, Trainbr-RBFNN, remaining useful life, parameter fusion.

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Section: Life Prediction Of External Gear Pumpmentioning

confidence: 99%

Remaining Useful Life Prediction Based on the Bayesian Regularized Radial Basis Function Neural Network for an External Gear Pump

Guo

Li-jiang

et al. 2020

IEEE Access

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“…For simplicity, taking the nonlinear drift f (t; θ ) T λ in (1) as one-dimensional function for example, we consider the following stochastic degradation model to demonstrate the implementation of the proposed method. where the nonlinear drift f (t; θ) T λ = λt a .…”

Section: An Illustrative Examplementioning

confidence: 99%

“…With the rapid development of information technology, nonlinearity and complexity have already become two important trends contributing to the engineering equipment [1]. However, because of the interaction between internal degradation of the system and external complex environments in practice, the system performance degradation will gradually evolve and finally leads to the system failure, which may eventually cause irreparable losses.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

Wang

et al. 2019

IEEE Access

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As the central component of prognostic and health management (PHM) field, remaining useful life (RUL) estimation approaches based on degradation modeling have played an extremely significant role in recent years. For the newly developed systems working in complex environments, the associated degradation processes not only lack historical data and prior information but also have strong nonlinearity and three-source variability. Therefore, this paper proposes an adaptive RUL estimation approach for the newly developed system based on a nonlinear model. Specifically, a general nonlinear Wiener-process-based degradation model is established to simultaneously characterize three-source variability and nonlinearity, and the associated RUL distribution is derived with an explicit form. In order to utilize the condition monitoring (CM) data of the service system up to date, we present a parameter estimation method based on the expectation maximization algorithm to adaptively estimate and update the model parameters online. As such, the RUL distribution can be updated once the new CM data are available. Finally, the effectiveness and superiority of the proposed method are demonstrated by the numerical example an empirical study for battery data. The results show that the proposed method can provide accurate and robust RUL prediction for the newly developed system. INDEX TERMS Remaining useful life, nonlinear, three-source variability, battery, prognostic and health management.

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“…It ultimately leads to degradation of equipment functions and improves the overall cost of equipment spending. With the increase in the number of equipment repairs, the degree of functional degradation and maintenance costs gradually increased [1]. Therefore, when we make decisions on repairing industrial systems and large equipment, we need to balance the factors like prolonged service life, equipment downturn, increased cost, and other factors.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Complex Life Cycle Prediction Based on Cell Division

Zhang

Wang

2017

Journal of Control Science and Engineering

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Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

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A survey on life prediction of equipment

Cited by 32 publications

References 54 publications

Remaining Useful Life Prediction Based on the Bayesian Regularized Radial Basis Function Neural Network for an External Gear Pump

Remaining Useful Life Prediction Based on the Bayesian Regularized Radial Basis Function Neural Network for an External Gear Pump

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

Modeling of Complex Life Cycle Prediction Based on Cell Division

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