A data-driven prognostics approach for RUL based on principle component and instance learning

The research of Remaining Useful Life (RUL) estimation is one of the most common tasks of Prognostics and Health Management (PHM). This paper presents a data-driven approach for estimating RUL using principle component and instance learning. The approach is especially suitable for situations in which abundant run-to-failure (RtF) data are available. Firstly, the principal component analysis (PCA) is used to find the low-dimensional principal components (PCs) from the statistical features of the measured signals. Then, the health indicators (HI) can be obtained by using weighted Euclid distance (WED), and regressed by the data-driven methods or model-based methods. Finally, the method based on instance learning is employed to estimate the RUL of the machine under operation. The performance of the prognostics approach introduced in this paper is demonstrated by using turbofan engine degradation simulation data set, which is supplied by NASA Ames.

[1]  Noureddine Zerhouni,et al.  A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering , 2015, IEEE Transactions on Cybernetics.

[2]  Jianbo Yu,et al.  A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.

[3]  N. Zerhouni,et al.  Component based data-driven prognostics for complex systems: Methodology and applications , 2015, 2015 First International Conference on Reliability Systems Engineering (ICRSE).

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[6]  Ismail Mohamad,et al.  Standardization and Its Effects on K-Means Clustering Algorithm , 2013 .

[7]  Michael G. Pecht,et al.  Multivariate State Estimation Technique for Remaining Useful Life Prediction of Electronic Products , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[8]  Jay Lee,et al.  Similarity based method for manufacturing process performance prediction and diagnosis , 2007, Comput. Ind..

[9]  Peter Craven,et al.  Smoothing noisy data with spline functions , 1978 .

[10]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[11]  S. Marble,et al.  Predicting the remaining life of propulsion system bearings , 2006, 2006 IEEE Aerospace Conference.

[12]  Asok Ray,et al.  Stochastic modeling of fatigue crack dynamics for on-line failure prognostics , 1996, IEEE Trans. Control. Syst. Technol..

[13]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .