Design of Embedded Wireless Sensor and its Soft Encapsulation for Embedded Monitoring of Helicopter Planetary Gear Set

Planetary gear set, as an important part of helicopter, is with the characteristics of multi-point and time-varying position engagement. For the revolution of planetary gears round sun gear, directions of vibration and pulse created by tooth damage change continuously. If an accelerometer fixed on the surface of gearbox, the angle between the directions of pulse force and accelerometer sensitivity will change continuously, which will causes that the components of pulse force on the sensitivity direction vary with time and the features of damage are very difficult to extract from the signals. Aiming at this problem, a type of embedded wireless sensor node was designed firstly, which can be fixed on the carrier of planetary gear, and acquires the damage-related vibration signals in a fixed direction of pulse force. Then, to avoid the corrosion of electronic components by the lubrication oil in gearbox, the protect restrictions of the sensor node was investigated and a kind of soft encapsulation method is applied. Finally, real vibration signal is measured and transmitted by the designed and/or encapsulated sensor node. The experiments show that the sensor can measure vibration effectively.

[1]  P.W. Kalgren,et al.  Defining PHM, A Lexical Evolution of Maintenance and Logistics , 2006, 2006 IEEE Autotestcon.

[2]  Robert X. Gao,et al.  Structural design and analysis for a sensor-integrated ball bearing , 2000 .

[3]  H. Powrie,et al.  PHM Sensor Implementation in the Real World - a Status Report , 2006, 2006 IEEE Aerospace Conference.

[4]  Donald L. Simon,et al.  A Systematic Approach to Sensor Selection for Aircraft Engine Health Estimation , 2013 .

[5]  Darryll J. Pines,et al.  Fiber optic sensors monitoring transmission ring gears , 2007, SPIE Optics East.

[6]  L. Michael Santi,et al.  Optimal Sensor Selection for Health Monitoring Systems , 2013 .

[7]  Donald L. Simon,et al.  Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics , 2008 .

[8]  William A. Maul,et al.  Sensor Selection and Optimization for Health Assessment of Aerospace Systems , 2007, J. Aerosp. Comput. Inf. Commun..

[9]  Byeng D. Youn,et al.  A Generic Sensor Network Design Framework Based on a Detectability Measure , 2010 .

[10]  Guangfan Zhang,et al.  Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture , 2005 .

[11]  George Vachtsevanos,et al.  Methodologies for uncertainty management in prognostics , 2009, 2009 IEEE Aerospace conference.

[12]  Zeng Jingcheng.  Relation of the Property of a Low Viscosity Epoxy Resin Casting Matrix to Its Degree of Cure , 2009 .

[13]  W. C. Baer,et al.  An open-standard smart sensor architecture and system for industrial automation , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[14]  Darryll J. Pines,et al.  Fiber Optic Strain Sensor Vibration Separation for Detection of Seeded Faults in Rotorcraft Transmissions , 2008 .

[15]  Niaoqing Hu,et al.  An Approach to Detect Damage Quantitatively for Planetary Gear Sets Based on Physical Models , 2011 .

[16]  Kourosh Danai,et al.  Model-Based Selection of Accelerometer Locations for Helicopter Gearbox Monitoring , 1998, Dynamic Systems and Control.

[17]  Frank L. Lewis,et al.  Wireless sensor network for machine condition based maintenance , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..