Neural Network-Based Gear Failure Prediction in a Brushless DC Actuation System

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

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

Yan Chen Christina Lucas Jay Lee Michael Buehner

Abstract

Due to its inherent efficiency and reliability, brushless DC (BLDC) driven actuation systems are widely used in a variety of industries such as aerospace, electric transportation and industrial positioning. However, it is inevitable that various types of faults can develop in the actuator either from the BLDC motor or geared positioning systems. This paper, focusing on actuator load positioning system failures, proposes a data-driven based failure prediction method. Run-to-failure data is first collected from test-beds of specific BLDC actuation systems and then critical features representing system performance are extracted. There are also dynamic behavioral tests used, which are designed to provide discrete measurements reflecting system health conditions. Ultimately, based on optimized mapping between the two groups of information, a general neural network model is developed to establish a nonlinear trajectory model for failure progression. The model also allows for prediction of gear failure without the interruption of performing dynamic behavioral tests during continuous working condition. This approach provides for real time monitoring of system behavior as well as possibility of the predicting the Remaining Useful Life (RUL) of the actuation system. Although many efforts have been done to predict gear wear based on vibration signal, the proposed method is formulated within a "sensor-less" environment and makes full use of existing on-board sensing information, which provides the possibility of a closed-loop control system for life management.

How to Cite

Chen, Y., Lucas, C., Lee, J., & Buehner, M. (2015). Neural Network-Based Gear Failure Prediction in a Brushless DC Actuation System. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2762
Abstract 18 | PDF Downloads 11

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

Keywords

Remaining useful life, prediction, BLDC actuator, gear wear, General Regression Neural Network

References
Satish, R., et al., (2004). Diagnosis of potential rotor faults in brushless DC machines. in Power Electronics, Machines and Drives, Conf. Publ. No. 498.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc
Rajagopalan, S., et al., (2006). Detection of rotor faults in brushless DC motors operating under nonstationary conditions. Industry Applications, IEEE Transactions. 42(6): p. 1464-1477.
Nandi, S. and H.A. Toliyat, (1999). Condition monitoring and fault diagnosis of electrical machines-a review. Industry Applications Conference, Thirty-Fourth IAS Annual Meeting. Conference Record of the 1999 IEEE.
Moseler, O., et al, (1999). Model-based fault diagnosis of an actuator system driven by the brushless DC motor. American Control Conference, 1999. Proceedings of the 1999.
Byington, C.S., M. Watson, and D. Edwards, (2004). Data- driven neural network methodology to remaining life predictions for aircraft actuator components. Aerospace Conference, 2004. Proceedings.
Zaidi, S.S.H., et al., (2011) Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models. Industrial Electronics, IEEE Transactions, 58(5): p. 1695-1706.
Kliman, G. and J. Stein, (1992). Methods of motor current signature analysis. Electric machines and power systems, 20(5): p. 463-474.
Rahimi, A., B. Recht, and T. Darrell, (2007) Learning to transform time series with a few examples. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2007, 29(10): p. 1759-1775.
Kohonen, T., (1990) The self-organizing map. Proceedings of the IEEE, 78(9): p. 1464-1480.
Specht, D.F., (1991). A general regression neural network. Neural Networks, IEEE Transactions on, 1991. 2(6): p. 568-576.
Cigizoglu, H.K. and M. Alp, (2006). Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software, 37(2): p. 63-68.
Section
Technical Papers

Most read articles by the same author(s)