scholarly journals Development of a Novel Sensor for Gear Teeth Wear and Damage Detection

Author(s):  
Vikram Sridhar ◽  
Kam Chana

Health monitoring of mechanical transmission systems is an important area of research. Mechanical transmission systems, especially gear boxes in aircraft, automobiles, and wind turbines etc. account for many of the maintenance costs due to repairs, replacements and downtime. Gear boxes can experience high level of failure due to varied load conditions and harsh environments. Replacing the gear box is quite an expensive process and has significant downtime. Current gear box monitoring involves mainly measuring vibrations, however vibrations occur when the fault in the gear has already progressed significantly. Gear teeth monitoring lacks sensor technology to successfully detect tooth damage and misalignment. This paper presents a new concept gear teeth damage detection using eddy current sensors fitted on to the teeth of an idler gear at various locations. These sensors detect various faults encountered in a gear such as micro and macro pitting of the tooth surface, contact wear etc. Eddy current sensors are already being used to detect turbomachinery blade vibrations and tip clearance as they are robust and immune to contamination. In the present case, we use an idler gear that incorporates miniature eddy current sensors and state of the art electronics with wireless data transmission to enable the device to operate remotely and in harsh environments. A rotating rig with gears (spur and helical) and oil supply was built to test and validate the sensor by seeding various faults on the tooth surface. The results show that the idler sensor gear was able to detect various faults. The new eddy current sensor idler gear concept will enable health monitoring of the gearbox and ensure timely maintenance and reduction in operation costs.

Author(s):  
K. S. Chana ◽  
M. T. Cardwell ◽  
J. S. Sullivan

Mechanical transmission systems require online health monitoring for several reasons. Gearboxes account for many of the maintenance costs due to repairs, replacements and downtime. Transmission systems feature in many applications including rotor aircraft and wind turbines. The wind energy industry since its inception has experienced high levels of failure rates. Principal gearbox design defects and structural problems have been notably significant issues of wind turbines and have had to be addressed by the wind turbine manufacturers. Reliability and safety of conventional wind turbines has improved although rates of failures are still disappointingly high. There still exists a lack of sufficient technology to enable reliable monitoring of the structural and operational conditions of wind turbines and this is currently a significant area of research. Gearbox monitoring in particular lacks sensor technology to successfully detect tooth damage, high speed and low speed shaft faults. Typically, vibration measurement and spectrum analysis are chosen for gearbox monitoring but, these methods are not able to detect the faults until failure is imminent. More recently acoustic emissions sensors are being developed for early detection of stress and surface defects. This paper presents a new concept that employs an eddy current sensor fitted to the teeth of an idler gear to detect early micro and macro pitting of the gear tooth surface. A rotating bench test has been carried out to validate the technique where simple eddy current sensors have been fitted to an idler gear. Seeded faults of three different types on an actual gear have been shown to be detectable using this technique. Eddy current sensors are used for their immunity to dust, dirt and oil contamination. Thus this technique is targeted for in-service operation where sensors have not previously been deployed to access the tooth face, flank and root shank.


Author(s):  
K. S. Chana ◽  
V. Sridhar ◽  
D. Singh

The advent of tip-timing systems makes it possible to assess turbomachinery blade vibration using non-contact systems. The most widely used systems in industry are optical. However, these systems are still only used on developmental gas turbine engines, largely because of contamination problems from dust, dirt, oil, water etc. Further development of these systems for in-service use is problematic because of the difficulty of eliminating contamination of the optics. Eddy current sensors are found to be a good alternative and are already being used for gas turbine health monitoring in power plants. Experimental measurements have been carried out on three different rotors using an eddy current sensor developed in a series of laboratory and engine tests in-house to measure rotor blade arrival times. A new tip-timing algorithm for eddy current sensors based on integration has been developed and is compared with two existing tip-timing algorithms: peak-to-peak and peak-and-trough. Among the three, the integration method provided the most promising results in the presence of electrical noise interference. The main aim of this work is to develop an algorithm that can be used to build a simple, robust, real-time and low cost analogue electronic circuit for use in-service health monitoring of engines.


Author(s):  
Alexi Rakow ◽  
Fu-Kuo Chang

In this study a structural health monitoring (SHM) fastener, with built-in eddy current sensors for in-situ monitoring of fatigue cracks at hole locations in layered metallic joints was developed. This presents an optimal method of sensor integration for early stage detection of these cracks, which are among of the most common forms of damage in airframes. Thin, conformable eddy current sensors optimized for in-hole flaw detection [1] and a method of mechanical integration and complete data acquisition and software system are discussed. Results from fatigue tests of single layer and multi-layer specimens are presented in addition to results from bench-top flaw detection tests as a means of experimental validation of the system.


2001 ◽  
Author(s):  
Neil J. Goldfine ◽  
Vladimir A. Zilberstein ◽  
Darrell E. Schlicker ◽  
Yanko Sheiretov ◽  
Karen Walrath ◽  
...  

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