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The piston ring-cylinder liner friction pair is one of the most important friction pair in IC engines. In previous modelling studies, most researches were carried out based on a hypothesis that the inner surface of liner is an ideal cylindrical shell without considering the effects of dynamic deformation on oil film distribution. To investigate the potential influences of structural deformations on tribological behaviors of cylinder assemblies, the dynamic deformation of liner surface was obtained by FEM-based dynamic simulation, and then introduced into the lubrication model. Different from the traditional lubrication model where the pressure stress factor and shear stress factor are regarded as constant, this paper calculated these factors in real time by numerical integration to achieve more realistic simulation results. This study shows that the friction force obtained from the improved model manifests obvious fluctuations, and shows a significant reduction compared to original model.

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme.

For accurate fault detection and diagnosis, this paper focuses on the study of bearing vibration responses under increasing radial clearances due to investable wear and different bearing grades. A nonlinear dynamic model incorporating with local defects and clearance increments is developed for a deep groove ball bearing. The model treats the inner race-shaft and outer race-housing as two lumped masses which are coupled by a nonlinear spring formalized by the Hertzian contact deformation between the balls and races. The solution of the nonlinear equation is obtained by a Runge-Kutta method in Matlab. The results show that the vibrations at fault characteristic frequencies exhibit significant changes with increasing clearances. However, an increased vibration is found for the outer race fault whereas a decreased vibration is found for inner race fault. Therefore, it is necessary to take into account these changes in determining the size of faults.

The trend to use inverter drives in industry is well established. It is desirable to monitor the condition of the motor/drive combination with the minimum of system intervention and at the same time retaining compatibility with the latest generation of AC PWM vector drives. This paper studies the effect of stator resistance asymmetry on the performance of the motor driven by a latest-generation unmodified AC PWM drive under varying speed conditions. The asymmetry of increased resistance in one phase is intended to simulate the onset of a failing connection between drive and motor but one that is non-critical and will remain undetected in use because the resistance increase is small and does not appear to affect the motor operation significantly. The performance is compared against baseline motor data for the resistance increase. Moreover, it is also examined following an auto-tune on the drive with the asymmetric motor in order to observe if any effects of resistance imbalance can be shown on the sensorless vector control algorithms. Initial results from the motor tests clearly show a difference in values measured from the motor current and voltage signals, which can be a useful indication of the asymmetry of the drive system.

This paper develops a new scheme for the\ud lightweight design of heavy dump truck frames based on the\ud characteristics of dynamic responses. The dynamic response\ud is predicted using a finite element (FE) model which is\ud verified by an experimental mode analysis. The FE model is\ud then used to investigate the characteristics of dynamic\ud responses and frame weight changes with respect to the\ud mass changes of each frame component for selecting\ud significant components. An optimization is finally developed\ud for the lightweight design under constrains that maintain\ud required dynamic responses and static strength. The\ud optimization results show that the weight of frame can be\ud reduced by 8%, showing that the scheme is an effective way\ud to achieve automotive lightweight design.

Vibrations of a defective rolling bearing often\ud exhibit nonstationary and nonlinear characteristics which are\ud submerged in strong noise and interference components. Thus,\ud diagnostic feature extraction is always a challenge and has\ud aroused wide concerns for a long time. In this paper, the\ud multifractal detrended fluctuation analysis (MF-DFA) is\ud applied to uncover the multifractality buried in nonstationary\ud time series for exploring rolling bearing fault data.\ud Subsequently, a new approach for fault diagnosis is proposed\ud based on MF-DFA and Mahalanobis distance criterion. The\ud multifractality of bearing data is estimated with the\ud generalized Hurst exponent and the multifractal spectrum.\ud Five characteristic parameters which are sensitive to changes\ud of bearing fault conditions are extracted from the spectrum\ud for diagnosis of fault sizes. For benchmarking this new\ud method, the empirical mode decomposition (EMD) method is\ud also employed to analyze the same dataset. The results show\ud that MF-DFA outperforms EMD in revealing the nature of\ud rolling bearing fault data.

Condition monitoring of a gearbox is a very\ud important activity because of the importance of gearboxes in\ud power transmission in many industrial processes. Thus there\ud has always been a constant pressure to improve measuring\ud techniques and analytical tools for early detection of faults in\ud gearboxes. This study forces on developing gearbox monitoring\ud methods based on operating parameters which are available in\ud machine control processes rather than using additional\ud measurements such as vibration and acoustics used in many\ud studies. To utilise these parameters for gearbox monitoring,\ud this paper examines a model based approach in which a data\ud model has been developed using a General Regression Neural\ud Network (GRNN) to captures the nonlinear connections\ud between the electrical current of driving motor and control\ud parameters such as load settings and temperatures based on a\ud two stage helical gearbox power transmission system. Using the\ud model a direct comparison can be made between the measured\ud and predicted values to find abnormal gearbox conditions of\ud different gear tooth breakages based on a threshold setup in\ud developing the model.