A Remaining Useful Life Prediction Method in the Early Stage of Stochastic Degradation Process

Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

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Remaining useful life prediction of nonlinear degradation process based on EKF

10.1109/ccdc52312.2021.9602196 ◽

2021 ◽

Author(s):

Yubing Wang ◽

Guo Xie ◽

Jing Yang ◽

Yu Liu ◽

Xinhong Hei ◽

...

Keyword(s):

Life Prediction ◽

Degradation Process ◽

Remaining Useful Life ◽

Useful Life

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Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

Advances in Mechanical Engineering ◽

10.1177/1687814018817184 ◽

2018 ◽

Vol 10 (12) ◽

pp. 168781401881718 ◽

Cited By ~ 16

Author(s):

Wentao Mao ◽

Jianliang He ◽

Jiamei Tang ◽

Yuan Li

Keyword(s):

Neural Network ◽

Life Prediction ◽

Short Term Memory ◽

Degradation Process ◽

Remaining Useful Life ◽

Feature Representation ◽

Short Term ◽

Term Memory ◽

Useful Life ◽

Long Short Term Memory

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

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Diamond-Coated Mechanical Seal Remaining Useful Life Prediction Based on Convolution Neural Network

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420510076 ◽

2019 ◽

Vol 34 (05) ◽

pp. 2051007

Author(s):

Zhibin Lin ◽

Hongli Gao ◽

Erqing Zhang ◽

Weiqing Cao ◽

Kesi Li

Keyword(s):

Nuclear Power ◽

Signal To Noise Ratio ◽

Prediction Method ◽

Degradation Process ◽

Remaining Useful Life ◽

Sensor Data ◽

Mechanical Seal ◽

Process Industries ◽

Petroleum Chemical ◽

Useful Life

Reliable remaining useful life (RUL) prediction of industrial equipment key components is of considerable importance in condition-based maintenance to avoid catastrophic failure, promote reliability and reduce cost during the production. Diamond-coated mechanical seal is one of the most critical wearing components in petroleum chemical, nuclear power and other process industries. Estimating the RUL is of critical importance. We consider the data-driven approaches for diamond-coated mechanical seal RUL estimation based on AE sensor data, since it is difficult to construct an explicit mathematical degradation model of seal. The challenges of this work are dealing with the noisy AE sensor data and modeling the degradation process with fluctuation. Faced with these challenges, we propose a pipeline method CDF-CNN to estimate the RUL for mechanical seal: WPD-KLD to raise the signal-to-noise ratio, novel CDF-based statistics to represent seal degradation process and CNN structure to estimate RUL. To acquire AE sensor data, several diamond-coated seals are tested from new to failure in three working conditions. Experimental results demonstrate that the proposed method can accurately predict the RUL of diamond-coated mechanical seal based on AE signals. The proposed prediction method can be generalized to other various mechanical assets.

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