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Gear fault diagnosis is important in the vibration monitoring of any rotating machine. When a localized fault occurs in gears, the vibration signals always display non-stationary behavior. In early stage of gear failure, the gear mesh frequency (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. This paper presents the value of optimal wavelet function for early detection of faulty gear. The Envelope Detection (ED) and the Energy Operator are used for gear fault diagnosis as common techniques with and without the proposed optimal wavelet to verify the effectiveness of the optimal wavelet function. Kurtosis values are determined for the previous techniques as an indicator parameter for the ability of early gear fault detection. The comparative study is applied to real vibration signals.

Bearing and gear condition monitoring are important to improve a mechanical system reliability and performance. In the early stage of bearing failures, the Bearing Characteristic Frequencies (BCFs) contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to eliminate such corrupting noise and interference. Referring to the non-stationary characteristics of roller bearing fault vibration signals, a roller bearing condition monitoring method based on Envelope Process to raw time-domain vibration signal and Autocorrelation enhancement to the residual signal is put forward in this paper. The concept of Envelope and Autocorrelation techniques and its implementation for defect identification are discussed. Also, distinction of bearing fault signal as cyclostationary from periodic signal for gear fault.

A vehicle gearbox serves for torque and speed conversion with help of rotating elements. Therefore the gearbox experiences periodic excitation forces with a fundamental frequency following the rotation frequency. These excitation forces give rise to corresponding periodic response signals, i.e. signals having content at the fundamental (rotational) frequency and its harmonics. Order analysis is an analysis technique which is used to extract these harmonic orders from the response signals. This article intends to use the order tracking analysis for gearbox fault diagnosis under variable speed conditions to compare between healthy and faulty cases by using order extraction. Finally, determine maximum Root Mean Square (RMS) as severity index.

When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain and the corresponding Bearing Characteristic Frequencies (BCFs) emerge in frequency domain. The common technique of Fast Fourier Transforms (FFT) and Envelope Detection (ED) are always used to identify faults occurring at the BCFs. In the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations. In order to extract the weak fault information submerged in strong background noise of the gearbox vibration signal, an effective signal processing method would be necessary to remove such corrupting noise and interference. Optimal Morlet Wavelet Filter and Envelope Detection (ED) are applied in this paper.

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.

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.