Health Assessment of Electronic Products using Mahalanobis Distance and Projection Pursuit Analysis

Authors: Sachin Kumar, Vasilis Sotiris, Michael Pecht

Abstract:

With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysis. These methods have been used to detect deviations in system performance from normal operation and for critical parameter isolation in multivariate environments. The study evaluates the detection capability of each approach on a set of test data with known faults against a baseline set of data representative of such “healthy" systems.

Keywords: Mahalanobis distance, Principle components, Projection pursuit, Health assessment, Anomaly.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071083

References:

[1] N. Vichare, P. Rodgers; V. Eveloy; and M. Pecht; "Environment and Usage Monitoring of Electronic Products for Health Assessment and Product Design," International Journal of Quality Technology and Quantitative Management. vol. 2, no. 4, 2007, pp. 235-250.
[2] J. Gu; N. Vichare; T. Tracy; and M. Pecht; "Prognostics Implementation Methods for Electronics," 53rd Annual Reliability and Maintainability Symposium (RAMS), Florida, 2007.
[3] G. Zhang, C. Kwan; R. Xu; N. Vichare; and M. Pecht; "An Enhanced Prognostic Model for Intermittent Failures in Digital Electronics," IEEE Aerospace Conference, Big Sky, MT, March 2007.
[4] N. Vichare; and M. Pecht; "Enabling Electronic Prognostics Using Thermal Data," Proceedings of the 12th International Workshop on Thermal Investigation of ICs and Products, Nice, C├┤te d'Azur, France, 27-29 September 2006.
[5] N. Vichare, P. Rodgers; and M. Pecht; "Methods for Binning and Density Estimation of Load Parameters for Prognostics and Health Management, International Journal of Performability Engineering, vol. 2, no. 2, April 2006.
[6] A. Fraser; N. Hengartner; K. Vixie; and B. Wohlberg; "Incorporating Invariants in Mahalanobis Distance based Classifiers: Application to Face Recognition," in International Joint Conference on Neural Networks (IJCNN), (Portland, OR, USA), Jul 2003.
[7] J. E. Jackson; and G. S. Mudholkar; "Control Procedures for Residuals Associated With Principal Component Analysis," Technometrics, vol. 21, no. 3, 1979.
[8] J. Liu, K. Lim; R. Srinivasan; and X. Doan; "On-Line Process Monitoring and Fault Isolation Using PCA," Proceedings of the 2005 IEEE International Symposium on, Mediterranean Conference on Control and Automation, 2005, pp. 658 - 66.
[9] G. Taguchi, and R. Jugulum; The Mahalanobis-Taguchi Strategy: A Pattern Technology System, Wiley, 2002.
[10] G. Taguchi, S. Chowdhury; and Y. Wu; The Mahalanobis-Taguchi System, New York: McGraw-Hill. 2001.
[11] E. B. Martin, A. J. Morris; and J. Zhang; "Process Performance Monitoring Using Multivariate Statistical Process Control," IEEE Proceeding of Control Theory Application, vol. 143, no.2, March 1996.
[12] H. Chen, G. Jiang; C. Ungureanu; and K. Yoshihira; "Failure Detection and Localization in Component Based Products by Online Tracking," KDD, 2005.
[13] H. Wang, Z. Song; and P. Li; "Fault Detection Behavior and Performance Analysis of Principal Component Analysis Based Process Monitoring Methods," American Chemical Society, vol. 41, 2002, pp. 2455 - 2464.
[14] H. Yue, and S. J. Qin; "Reconstruction-Based Fault Identification Using a Combined Index," American Chemical Society, vol. 40, 2001, pp. 4403- 4414.