A Compressed Sensing Feature Extraction Approach for Diagnostics and Prognostics in Electromagnetic Solenoids

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 2, 2017
Christian Knoebel Hanna Wenzl Johannes Reuter Clemens Guehmann

Abstract

One major realm of Condition Based Maintenance is finding features that reflect the current health state of the asset or component under observation. Most of the existing approaches are accompanied with high computational costs during the different feature processing phases making them infeasible in a real-world scenario. In this paper a feature generation method is evaluated compensating for two problems: (1) storing and handling large amounts of data and (2) computational complexity. Both aforementioned problems are existent e.g. when electromagnetic solenoids are artificially aged and health indicators have to be extracted or when multiple identical solenoids have to be monitored. To overcome those problems, Compressed Sensing (CS), a new research field that keeps constantly emerging into new applications, is employed. CS is a data compression technique allowing original signal reconstruction with far fewer samples than Shannon- Nyquist dictates, when some criteria are met. By applying this method to measured solenoid coil current, raw data vectors can be reduced to a way smaller set of samples that yet contain enough information for proper reconstruction. The obtained CS vector is also assumed to contain enough relevant information about solenoid degradation and faults, allowing CS samples to be used as input to fault detection or remaining useful life estimation routines. The paper gives
some results demonstrating compression and reconstruction of coil current measurements and outlines the application of CS samples as condition monitoring data by determining deterioration and fault related features. Nevertheless, some unresolved issues regarding information loss during the compression stage, the design of the compression method itself and its influence on diagnostic/prognostic methods exist.

How to Cite

Knoebel, C., Wenzl, H., Reuter, J., & Guehmann, C. (2017). A Compressed Sensing Feature Extraction Approach for Diagnostics and Prognostics in Electromagnetic Solenoids. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2385
Abstract 195 | PDF Downloads 115

##plugins.themes.bootstrap3.article.details##

Keywords

feature extraction, Actuator Fault Diagnosis, Compressed Sensing, Actuator Remaining Useful Life Estimation

References
Alkhadafe, H., Al-Habaibeh, A., & Lotfi, A. (2016). Condition monitoring of helical gears using automated selection of features and sensors. Measurement, 93, 164–177.
Candes, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.
Candes, E. J., & Tao, T. (2005). Decoding by linear programming. IEEE Transactions on Information Theory, 51(12), 4203–4215. doi: 10.1109/TIT.2005.858979
Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.
Duarte, M. F., & Eldar, Y. C. (2011). Structured compressed sensing: From theory to applications. IEEE Transactions on Signal Processing, 59(9), 4053–4085.
Flandrin, P., & Borgnat, P. (2010). Time-frequency energy distributions meet compressed sensing. IEEE Transactions on Signal Processing, 58(6), 2974–2982.
Haupt, J., Bajwa, W. U., Raz, G., & Nowak, R. (2010). Toeplitz compressed sensing matrices with applications to sparse channel estimation. IEEE Transactions on Information Theory, 56(11), 5862–5875.
Jardine, A., Daming Lin, & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
Jayawardhana, M., Zhu, X., Liyanapathirana, R., & Gunawardana, U. (2017). Compressive sensing for efficient health monitoring and effective damage detection of structures. Mechanical Systems and Signal Processing, 84, Part A, 414–430.
Kn¨obel, C., Marsil, Z., Rekla, M., Reuter, J., & G¨uhmann, C. (2015). Fault detection in linear electromagnetic actuators using time and time-frequency-domain features based on current and voltage measurements. In IEEE (Ed.), Methods and models in automation and robotics (mmar), 2015 20th international conference on methods and models in automation and robotics.
Liu, Y., Zhang, G., & Xu, B. (2017). Compressive sparse principal component analysis for process supervisory monitoring and fault detection. Journal of Process Control, 50, 1–10.
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min- redundancy. IEEE transactions on pattern analysis and machine intelligence, 27(8), 1226–1238.
Wang, Y., Xiang, J., Mo, Q., & He, S. (2015). Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis. Measurement, 68, 70–81.
Section
Technical Research Papers