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In this paper, we present an image registration approach to cope with inter-image illumination changes of arbitrary shape in order to monitor the development of micro-pitting in transmission gears. Traditional image registration approaches do not typically account for inter-image illumination variations that negatively affect the geometric registration precision. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, the correlation between the resulting aligned images is compared to a reference one. The presented image registration approach is used with an online health monitoring system involving the analysis of vibration, acoustic emission and oil debris to follow the development of micro-pitting in transmission gears. The proposed monitoring system achieves more registration precision compared to competing systems.

Bearings are reliable mechanical components which are designed according to known factors and should operate for their designed life. Nevertheless, in practice, various factors such as environmental and operating conditions may significantly reduce their service life, hence application of condition monitoring to bearings could achieve greater reliability. Condition based maintenance (CBM) systems are currently the main maintenance strategy for many applications due to their effectiveness in reducing maintenance costs and increasing reliability. However, trustworthy monitoring techniques are required to support the operation of the CBM system in tracking the condition of the bearing system; two methods are widely used: acoustic emission (AE), and vibration analysis. This work presents a novel AE wireless monitoring system (AEWMS) for rotating machinery which is able to provide a decision support feature using an intelligent approach to overcome any false alarms that may occur.

Gears are one of the most important parts of any mechanical transmission system, and in order to achieve reliable operation effective monitoring techniques must be employed. Predictive health monitoring (PHM) systems are currently gaining in popularity due to their effectiveness in providing robust information about the system condition and reducing maintenance costs. However, PHM systems require reliable monitoring techniques, such as vibration, acoustic emission, and oil debris analysis. These techniques have been studied in recent years to discover which can best support the operation of PHM systems in tracing the condition of the operating transmission. These studies have shown the need to apply intelligent algorithms in order to benefit from the advantage of each technique in classifying faults and predicting the onset of failure. This paper presents a new online PHM system for monitoring different gear faults using vibration analysis and autoregressive (AR) algorithms.

Electro-Pneumatic systems exhibit highly nonlinear characteristics due to air compressibility, the presence of friction and the nonlinearities of control valves. Monitoring by acquiring the system's transfer function accurately can be difficult for nonlinear systems. This paper outlines a new idea that one can deal with the electro-pneumatic system as a black box, and using a multivariate technique called principal component analysis (PCA) and projection to latent structure discriminant analysis (PLSDA) to provide robust information about the system's condition. The monitoring system has been experimentally validated for an electro-pneumatic printing machine system using vibration, pressure and displacement sensory data integration using PCA-PLSDA algorithm. Experiments were conducted under two pressures for three artificially conditions: normal, throttled, and leaking system.

Micro-pitting is a fatigue effect that occurs in geared transmission systems 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 bending fatigue failure and advanced phases of gear surface fatigue failure such as macro-pitting and scuffing. However, due to modern improvements in steel production the main cause of gear contact fatigue failure can be attributed to surface micro-pitting rather than sub-surface phenomena. Responding to the consequent demand to detect and monitor the progression of micro-pitting, this study experimentally evaluated the development of micro-pitting in spur gears using vibration and oil debris analysis. The paper presents the development of an online health monitoring system for use with back-to-back gear test rigs.