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Long-range correlation degradation process remaining life prediction method depending on time and states

15 Dec 2017-
TL;DR: In this paper, a long-range correlation degradation process remaining life prediction method is proposed, which consists of the following steps that firstly, sensor data sampled at equal intervals is acquired; a degeneration model is established on the basis of the fractional Brownian motion according to degradation data characteristics; a Hurst index in the model was established by utilizing a quadratic-variation-based method; drift term unknown parameters were estimated by utilizing the likelihood ratio function to the largest extent.
Abstract: The invention discloses a long-range correlation degradation process remaining life prediction method depending on time and states, and belongs to the technical field of health management. The method comprises the following steps that firstly, sensor data sampled at equal intervals is acquired; a degeneration model is established on the basis of the fractional Brownian motion according to degradation data characteristics; a Hurst index in the model is established by utilizing a quadratic-variation-based method; drift term unknown parameters are estimated by utilizing a likelihood ratio function to the largest extent, wherein the likelihood ratio function is constructed through a Radon-Nikodym derivative; diffusion term unknown parameters are estimated through the maximum likelihood method; then by means of the weak convergence theory, an original degradation process is approximated to a random process which has a time-varying diffusion term coefficient and is based on the Brownian motion; the degradation process is further simplified through a group of transformation; finally, analyzed remaining life distribution is obtained. By means of the method, the remaining life distribution can be accurately predicted.
Citations
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Patent
09 Oct 2018
TL;DR: In this article, a multi-mode degradation process modeling and residual service life prediction method is proposed for the technical field of health management, and the method comprises the following steps that: firstly, collecting degradation data which is sampled at equal intervals; carrying out change point detection on the degradation data; clustering degradation segments obtained by changepoint segmentation by taking a degradation rate as a characteristic; establishing a degradation model which contains mode switching, and describing the mode switching through one continuous time Markov chain; adopting a method based on quadratic variation to estimate the Hurst index
Abstract: The invention discloses a multi-mode degradation process modeling and residual service life prediction method, and belongs to the technical field of health management. The method comprises the following steps that: firstly, collecting degradation data which is sampled at equal intervals; carrying out change point detection on the degradation data; clustering degradation segments obtained by changepoint segmentation by taking a degradation rate as a characteristic; establishing a degradation model which contains mode switching, and describing the mode switching through one continuous time Markov chain; adopting a method based on quadratic variation to estimate the Hurst index of a degradation process; utilizing a maximum likelihood method to independently estimate the state transition ratematrix of the Markov chain and a drift item coefficient and a diffusion item coefficient under each mode; utilizing a Monte Carlo algorithm to obtain distribution obeyed by the drift item under the influence of state switching in a further period of time; and under a given threshold value, obtaining the distribution of residual service life. By use of the method, the residual service life distribution of systems or equipment which contains various degradation modes can be accurately predicted.

3 citations

Patent
19 Sep 2019
TL;DR: In this paper, a multi-mode degradation process modelling and remaining service life prediction method was proposed for the technical field of health management, consisting of collecting degradation data sampled at equal intervals, performing change point detection on the degradation data, using a degradation rate as a feature to cluster degraded segments obtained by means of change point segmentation, building a degradation model containing mode switching, wherein the model switching is described by a continuous-time Markov chain, estimating a Hurst index of a degradation process by using a quadratic variation based approach, and then obtaining the distribution of the
Abstract: A multi-mode degradation process modelling and remaining service life prediction method, belonging to the technical field of health management. The method comprises the following steps: firstly, collecting degradation data sampled at equal intervals; performing change point detection on the degradation data; using a degradation rate as a feature to cluster degraded segments obtained by means of change point segmentation; building a degradation model containing mode switching, wherein the model switching is described by a continuous-time Markov chain; estimating a Hurst index of a degradation process by using a quadratic variation based approach; using a maximum likelihood method to respectively estimate a state transition rate matrix of the Markov chain and a drift term coefficient in each mode, and a diffusion term coefficient; using a Monte Carlo algorithm to obtain the distribution obeyed by the drift term under the influence of state switching in a future period of time; and then obtaining the distribution of the remaining service life under a given threshold value.
References
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Patent
01 Jan 2014
TL;DR: In this article, an equipment residual service life prediction method under the condition of uncertain degradation measured data is proposed, where degradation state uncertainty and measurement uncertainty are considered, characteristic quantity of individual service life and overall reliability service life of equipment are predicated and analyzed.
Abstract: The invention belongs to the technical field of reliability engineering, and relates to an equipment residual service life prediction method under the condition of uncertain degradation measured data. The method includes the following steps: creating an equipment performance degradation model under the condition of the uncertain degradation measured data, estimating model parameters, estimating an equipment potential performance degradation state and predicating residual service life. The equipment residual service life prediction method under the condition of the uncertain degradation measured data is provided, degradation state uncertainty and measurement uncertainty are considered, characteristic quantity of individual service life and overall reliability service life of equipment are predicated and analyzed, and the method can serve as an effective analysis tool for predicating the residual service life of the equipment so as to provide powerful theoretical basis and technical support to state-based maintenance of the equipment, so that expenditure is reduced, unnecessary economic loss is avoided, and good engineering application value is realized.

20 citations

Journal ArticleDOI
TL;DR: In this article, an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter, and the Bayesian method was used to update the prior estimation of failure threshold.
Abstract: Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold (RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization (EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

15 citations

Patent
17 Aug 2016
TL;DR: In this paper, the authors proposed an online prediction method for the remaining life of electromechanical equipment under the situation of two-stage degradation. And the model proposed in the method better conforms to the general degradation rules, more accurate online remaining life prediction results can be obtained, and great significance is achieved on fault prediction and health management in engineering.
Abstract: The invention discloses an online prediction method for the remaining life of electromechanical equipment under the situation of two-stage degradation. The online prediction method can be applied to online service life prediction and health management of mechanical equipment and electric and electronic devices. The method comprises the steps that a Wiener process model serves as a basic degradation model of an object, and a degradation drifting coefficient is expanded into a state and described with a closed oblique Wiener process. A new algorithm is proposed to overcome prediction deviation caused by the Markovian feature of a common Wiener process. For state estimation in the online prediction stage, an iteration filter algorithm is proposed to obtain an analytical expression of updated states. On parameter estimation, a two-stage parameter estimation algorithm is proposed. An analytical expression related to remaining life prediction results is obtained by using updated states and parameters. The model proposed in the method better conforms to the general degradation rules, more accurate online remaining life prediction results can be obtained, and great significance is achieved on fault prediction and health management in engineering.

8 citations