Degradation Prediction Model Based on a Neural Network with Dynamic Windows

Zhang, Xinghui; Xiao, Lei; Kang, Jianshe

doi:10.3390/s150306996

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…Based on the learned state transition function and real-time information of the fault indicator, the PF algorithm was used for RUL prediction of the gear subjected to wear mode of failure. Similar study is reported by Zhang et al 7 who proposed an artificial neural network (ANN) model for predicting the trend of the sideband based HI.…”

Section: Introductionsupporting

confidence: 82%

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Kundu

Darpe

Kulkarni

2019

Structural Health Monitoring

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This article presents an ensemble decision tree–based random forest regression methodology for remaining useful life prediction of spur gears subjected to pitting failure mode. The random forest regression methodology does not require an elaborate statistics background knowledge and has an inbuilt health indicator selection capability compared to other existing data-driven remaining useful life prediction approaches. A correlation coefficient parameter based on the residual vibration signal is used for monitoring and detecting the pitting progression in spur gears. The effectiveness of the correlation coefficient of the residual vibration signal is assessed over the other existing health indicators for pitting fault progression. To show the inbuilt best health indicator selection capability of the random forest regression model, initially, eight indicators (existing seven and the correlation coefficient of the residual vibration signal) were used for model training. In addition, the effect of fusing the vibration sensor data from multiple positions on the gearbox on prediction accuracy of the random forest regression model is also evaluated. The accuracy in the remaining useful life prediction is found to increase after fusing the correlation coefficient of the residual vibration signal based health indicator derived from the accelerometers located at multiple positions on the gearbox in comparison to data from a single accelerometer. Furthermore, the accuracy of the proposed methodology is tested and proven using five accelerated run-to-failure experimental data collected from the specially built test rig.

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Section: Introductionsupporting

confidence: 82%

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Kundu

Darpe

Kulkarni

2019

Structural Health Monitoring

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“…There are mainly artificial intelligence methods and statistical data-driven methods. Among them, artificial intelligence methods include support vector machine regression [9], correlation vector machine [10], neural network [11,12], ant colony algorithm [13], etc. This type of method is based on a large amount of degradation data.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

et al. 2020

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The remaining useful life (RUL) prediction is the core of equipment maintenance and decisionmaking. Accurate RUL prediction can make effective maintenance before the failure occurs to reduce the probability of equipment failure. However, in industrial practice, we often face the situation that prior information is insufficient or inaccurate, which could influence our prediction of RUL and reduce the prediction accuracy severely. To solve this problem, we study the issue of RUL prediction under imperfect prior degradation data by reasonably fusing the prior information and the field degradation data. Firstly, based on the linear Wiener degradation process, we prove a conclusion that the random parameter estimation results by the joint Bayesian algorithm and the expectation-maximization (EM) algorithm are the same as that by the maximum likelihood estimation (MLE) method. It shows that the joint estimation method completely overcomes the influence of prior information, and the more iterations, the smaller proportion of prior information. Secondly, we also prove that the random parameter joint updating method for the nonlinear Wiener degradation process has the same characteristics. Then, a heuristic algorithm that reasonably fuses the prior information and the field degradation data is proposed, which controls the number of iterations of the joint updating algorithm based on the credibility of the prior information. It is also applied to the nonlinear degradation process by not iterating the nonlinear coefficients. Finally, the correctness of the conclusion of this paper is verified by the simulated degradation data, and the effectiveness of the proposed heuristic updating method is verified by the actual lithium battery degradation data.

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“…During design, each train-track component is associated with one or more physics comprehension which favors the stability of forecasting the growing deterioration rate using model oriented methods even though in most cases majority of component attitudes are nonlinear. To counter the nonlinearity issues, many authors have valoralised the use of AI ideas center on neural network, fuzzy logic, Markov Models, particle filtering and other genetic algorithms [3] [4]. Excessive usage of time during execution and as such lack of human comprehension due to their hidden attitude are their negative motivation when relying on them.…”

Section: Introductionmentioning

confidence: 99%

A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

Sparthan¹,

Nzie²,

Sohfotsing³

et al. 2020

OJAppS

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In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.

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Degradation Prediction Model Based on a Neural Network with Dynamic Windows

Cited by 28 publications

References 28 publications

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

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