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The paper will present the design of shift fork for automotive manual gearbox dedicated for the manufacturing with powder metal technology. Based on limitations and requirements of new production technology of shifting sleeve, the new design of shifting sleeve and shifting fork are created. The prototypes of shift fork and sleeve were built, but for cost reasons, were manufactured from steel. The functionality of several forks with different tolerances was tested. Prototypes were put through functional and durability tests on testing bench in real automotive gearbox. The tests were accomplished on the inertia test stand. Drawings and CAD models of tested prototypes, custom tools and designed shift forks are described in detail in this article.

Using the PULSE platform for vibration analysis, which has been developed as an advanced solution for vibration measurements, the Robust Diagnostic Concept (RDC) was elaborated. The PULSE setup is designed to aid in fault diagnosis of a vehicle gearbox - the main part of a vehicle powertrain. Time Domain, Continuous Wavelet Transformation Technique (CWT), FFT and order analysis measurements are used for detection of an artificial pitting defect in a gear by tracking the gearbox response at accelerated speed and different loads. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of the fifth speed gear on the intermediate shaft. The effect of temperature on the vibration measurements was also investigated to study its impact on the fault diagnosis.

An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented.

In this paper, a fault in rolling bearing is diagnosed using time waveform analysis. In order to verify the ability of time waveform analysis in fault diagnosis of rolling bearing, an artificial fault is introduced in vehicle gearbox bearing: an orthogonal placed groove on the inner race with the initial width of 0.6 mm approximately. The faulted bearing is a roller bearing located on the gearbox input shaft - on the clutch side. An optimal Morlet Wavelet Filter and autocorrelation enhancement are applied in this paper. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, autocorrelation enhancement is applied to the filtered signal.