Condition Monitoring of Helical Gear Transmissions Based on Vibration Modelling and Signal Processing

Condition monitoring (CM) of gear transmission has attracted extensive research in recent years. In particular, the detection and diagnosis of its faults in their early stages to minimise cost by maximising time available for planned maintenance and giving greater opportunity for avoiding a system breakdown. However, the diagnostic results obtained from monitored signals are often unsatisfactory because mainstream technologies using vibration response do not sufficiently account for the effect of friction and lubrication. To develop a more advanced and accurate diagnosis, this research has focused on investigating the nonlinearities of vibration generation and transmission with the viscoelastic properties of lubrication, to provide an in-depth understanding of vibration generating mechanisms and hence develop more effective signal processing methods for early detection and accurate diagnosis of gear incipient faults. A comprehensive dynamic model has been developed to study the dynamic responses of a multistage helical gear transmission system. It includes not only time-varying stiffness but also tooth friction forces based on an elastohydrodynamic lubrication (EHL) model. In addition, the progression of a light wear process is modelled by reducing stiffness function profile, in which the 2nd and 3rd harmonics of the meshing frequency (and their sidebands) show significant alteration that support fault diagnostic at early stages. Numerical and experimental results show that the friction and progressive wear induced vibration excitations will change slightly the amplitudes of the spectral peaks at both the mesh frequency and its sideband components at different orders, which provides theoretical supports for extracting reliable diagnostic signatures. As such changes in vibrations are extremely small and submerged in noise, it is clear that effective techniques for enhancing the signal-to-noise ratio, such as time synchronous averaging (TSA) and modulation signal bispectrum (MSB) are required to reveal such changes. MSB is preferred as it allows small amplitude sidebands to be accurately characterised in a nonlinear way without information loss and does not impose any addition demands regarding angular displacement measurement as does TSA. With the successful diagnosis of slight wear in helical gears, the research progressed to validate the capability of MSB based methods to diagnose four common gear faults relating to gear tribological conditions; lubrication shortfall, changes in lubrication viscosity, water in oil, and increased bearing clearances. The results show that MSB signatures allows accurate differentiation between these small changes, confirming the model and signal processing proposed in this thesi

Investigating the Effect of Water Contamination on Gearbox Lubrication based on Motor Control Data from a Sensorless Drive

Water is one of the most significant destructive contaminations to lubricants which in turn lead to more power consumption and early damage to rotating machines. This study explores the effect of water contents in gearbox lube oil on the responses of electrical supply parameters. A two stage gearbox based mechanical transmission system driven by a sensorless variable speed drive (VSD) is utilised to investigate experimentally any measurable changes in these signals that can be correlated with water contamination levels. Results show that the supply parameters obtained from both external measurements and the VSD control data can be correlated to the contamination levels of oil with water and hence can be based on for an instant diagnosis of water contamination. Particularly, the voltage and hence the power responses are more sensitive to the water contents than that of current because the VSD regulates more the voltage to adapt the small load changes due to the water induced lubrication degradation. Simultaneously, vibration also shows changes which agree with that of power supply parameters

An Investigation of Electrical Motor Parameters in a Sensorless Variable Speed Drive for Machine Fault Diagnosis

Motor current signature analysis (MCSA) is regarded as an effective technique for motor and its downstream equipment fault diagnostics. However, limited work has been carried out for motors based on a sensorless variable speed drive (VSD). This study focuses on investigation of mechanical fault detection and diagnosis using electrical signatures from a VSD system. An analytic analysis was conducted to show that the fault can induce sidebands in instantaneous current, voltage and power signals in the VSD system, rather than just the sideband in a drive without closed loop control. Then different degrees of tooth breakages in an industrial two-stage helical gearbox were experimentally studied. It has found that even though the measured signal is very noisy, common spectrum analysis can discriminate the small sidebands for the fault detection and diagnosis. However, it has found that the power signals resulted from the multiplication of the current and voltage can provide a better diagnostic result

Frictional Effects on the Diagnostics of Helical Gear Tooth Defects

Tooth Defect is a common failure mode that frequently occurs in gears. To develop successful diagnostic techniques, this study examines the capability of helical gear dynamic responses with the inclusion of different time-varying friction models, i.e. friction-free, Coulomb and elasto-hydrodynamic lubrication (EHL) models. The gear system is a 10-DOF (degree-of-freedom) vibration system, which incorporates the effects of gear pair, supporting bearings, driving motor and loading system. Moreover, it couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness excitations and different tooth breakage severities. To explore the vibration response, spectral peaks at characteristic mesh frequency and its harmonics along with their sidebands are considered in the light of the impulsive sources from tooth damages and different frictional excitation models. It has found that the sidebands exhibit significant difference between different friction models and mesh components for tooth defects. It is concluded that the frictional effect should be taken into account if it is to be an accurate method for the detection and diagnostic different tooth surface defects

Helical gear wear monitoring: Modelling and experimental validation

Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and leads to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-to-failure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles. The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis

Influence of Lubricant Starvation on Gearbox Vibration Signatures for Condition Monitoring

Different gear failure modes are strongly correlated with lubricant sta-tus, for example low oil level or starved lubrication leads to significant gear dam-ages. In order to develop an early detection and accurate diagnosis of gearbox lubricant serving conditions based on online vibration measurements, this study will investigate the effect of lubricant starvation on the gearbox vibration re-sponses. A two-stage helical industrial gearbox was tested under different lubri-cant shortage conditions. The results show that the gearbox vibration signature changes significantly with lubricant starvation, which includes more consistent increase in the amplitudes of vibration responses at meshing frequency harmon-ics and their associated sideband components. These changes correspond that vi-bration signal can be considered to normalise condition indicator of gearbox lub-ricant starvations

Investigating the Effect of Water Contamination on Gearbox Lubrication based upon Motor Control Data from a Sensorless Drive

Water is one of the most significant destructive contaminations to lubricants which in turn lead to more power consumption and early damage to rotating machines. This study explores the effect of water contents in gearbox lube oil on the responses of electrical supply parameters. A two stage gearbox based mechanical transmission system driven by a sensorless variable speed drive (VSD) is utilised to investigate experimentally any measurable changes in these signals that can be correlated with water contamination levels. Results show that the supply parameters obtained from both external measurements and the VSD control data can be correlated to the contamination levels of oil with water and hence can be based on for an instant diagnosis of water contamination. Particularly, the voltage and hence the power responses are more sensitive to the water contents than that of current because the VSD regulates more the voltage to adapt the small load changes due to the water induced lubrication degradation. Simultaneously, vibration also shows changes which agree with that of power supply parameters

Damage Detection based on the Natural Frequency shifting of a clamped rectangular plate model

Damage detection of any structure becomes the main concern in a failure analysis. Early failure detection is very important as it can prevent any catastrophic failure by replacing or repairing the damage part at early stage. One of the non-destructive methods of damage detection is using frequency based vibration analysis. Identification and comparison of a set of natural frequencies before and after damage is the main concern of this research. A rectangular plate clamped at all edges represented an initial undamaged structure. Based on Kachanov's definition, damage existence in a structure is introduced in the presence of some circular voids. The voids are generated randomly at different level of damage value. To obtain the Natural Frequencies, a Finite Element Model (FEM) of a clamped plate with the updated value of Young's Modulus is analyzed. From the FEM analysis result, it is found that the Natural Frequencies are shifted as the void existence increase. Using curve fitting, the model of Natural Frequency shifting as a function of damage evolution has been generated. It is found that the shifting of the Natural Frequency is greater at higher frequency value as indicated by the higher absolute gradient