Surface fatigue failure occurs in geared transmission systems due to factors such as high contact stress, and monitoring its progression is vital if the eventual failure of the tooth flank is to be prevented. Techniqu...
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ISBN:
(纸本)9781457705564
Surface fatigue failure occurs in geared transmission systems due to factors such as high contact stress, and monitoring its progression is vital if the eventual failure of the tooth flank is to be prevented. Techniques involving the analysis of vibration, acoustic emission and oil debris have been used to successfully monitor the progression of the different phases of gear surface fatigue failure. However, during monitoring a suitable assessment method is required to correlate the characteristics of the signal with the condition of the gear surface. The most commonly used such methods are gear flank profile scanning, replica sample analysis and conventional image analysis, with each technique having both advantages and disadvantages. Responding to the demand for effective assessment methods, this study experimentally evaluates the development of micro-pitting in gears using a new image registration technique in an online health monitoring system. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, an image registration approach is proposed to cope with inter-image illumination changes of arbitrary shape. Then correlation between the resulting aligned images is compared to a reference one before testing obtained. The results validate the system's capabilities to detect early gear defects and reliably identify the gradual development of micro-pitting in gears, so that it could be used in predictive health monitoring (PHM) systems.
Power transmissions are one of the most important parts of any mechanical system, and in order to achieve reliable operation robust and effective maintenance strategies must be used to trace the condition of the opera...
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Power transmissions are one of the most important parts of any mechanical system. In order to achieve the reliable operation of these systems, effective maintenance strategies must be used. Condition based maintenance...
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Power transmissions are one of the most important parts of any mechanical system. In order to achieve the reliable operation of these systems, effective maintenance strategies must be used. Condition based maintenance (CBM) strategies are currently gaining in popularity due to their effectiveness in reducing maintenance costs. However, these require reliable monitoring techniques. Currently, three monitoring techniques are in use in CBM: vibration, acoustic emission, and oil debris analysis. Researchers have studied these techniques to discover which can best support the operation of CBM systems in tracing the condition of the operating transmission, classifying faults, and predicting the onset of failure. These studies have shown the need to apply fusion algorithms to take the best advantage of each technique. This paper presents a novel approach in monitoring different gear faults combining vibration, acoustic emission, oil debris analysis and fuzzy logic sensory fusion algorithms. The intelligent health monitoring system (IHMS) has been implemented on a back-to-back gearbox and can be adapted to monitor the behaviour of transmission systems in automotive, aircraft, wind turbine, and industrial machinery. The study describes the operation of the online IHMS under variable conditions and its capability in detecting transmission gear defects and thus preventing sudden unexpected failures. The results support the recent trend in using IHMSs in CBM strategies.
Micro-pitting is a fatigue effect that occurs in gears due to high contact stress and monitoring its progression is vital to prevent the eventual failure of the tooth flank. Parameter signature analysis has been succe...
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Micro-pitting is a fatigue effect that occurs in gears due to high contact stress and monitoring its progression is vital to prevent the eventual failure of the tooth flank. Parameter signature analysis has been successfully used to monitor the advanced phases of gear surface fatigue failures and bending fatigue failures. Due to the improvement in modern steel production, the main cause of gear contact fatigue failures is attributed to surface micro-pitting failures rather than sub-surface failures;this increases the demand for the monitoring of micro-pitting progression. This study will evaluate the detection and progression of micro-pitting in helical gears using acoustic emission, vibration, and oil debris analysis monitoring. The paper will present an automated Health Monitoring System which has recently been developed for use with a 75mm back to back gear test rig. The results will validate the system capabilities in detecting early defects, and can be used in both predictive and proactive maintenance strategies.
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