Gears are the one of the most important machine components and widely used in transmission design of automobiles and other rotating machinery. In industry, breakdown of such crucial components causes heavy losses. This paper present two signal processing techniques used for the fault detection of a gear used in an internal combustion engine, they are conventional vibration spectrum analysis and continuous wavelet transform. A fault diagnosis engine test setup is built for experimental studies to acquire vibration signal ...

Detection of machine component failure is very important to be properly applied in a maintenance program in industries. The objective of this research is to detect gear fault using wavelet transforms. The vibration signal is acquired with accelerometer mounted at bearing houses of 2 parallel shafts with 2 spur gears (28 tooth). The gears are rotated at 1200 RPM and the spectrum is displayed. The spectrum cannot indicate gear mesh frequencies (GMF), because they are covered with frequencies such as natural and harmonic frequencies of rotating shaft. This research have developed a method to obtain GMF using wavelet decomposition and cross correlation. The results showed that with FFT applied to cross-correlation of wavelet detil components,, spectrum of visible and distinctable GMF has been obtained. 1. Background Gear units are parts of machines serve to transmit speed and torque between two shafts. The units are used in wind turbine [1], automotive and helicopter engines [2]. During using, the gear defects like wear, broken, cracked usually occure. The damage should be detected in a maintenance program. Gear condition monitoring is therefore required in order to improve safety and reduce operating costs. In this research, a rig consists of 2 shafts with 2 gear is constructed. One gear is normal gear while the other has a partially/full toothcut. Electric motor is used to rotate one shaft to 1200 RPM. Vibration signal is then obtained using accelerometer mounted on the bearing house. The signal processing method uses only FFT has disadvantages, i.e. the frequency associated with fault condition must be known first and if the signal has a low signal to noise ratio or there are many other vibration components then peaks correspond to failure frequency cannot be clearly indicated [3, 4]. This research uses wavelet transformation to indicate GMF features so that gear failure i.e. full / partial toothcut gear can be detected. Wavelet-based signal processing for gear fault detection had been investigated by [4] and [5]. Further studies of wavelet transform applications on vibration signals for gear fault detection have been also performed by [6] and [7] that discuss of wavelet decomposition and feature extraction using PCA.

To achieve reliable and cost effective diagnosis, Motor current signature analysis is used to investigate the use of an induction motor as a transducer to indicate the faults in multistage gearbox via analyzing supply parameters such as phase current and instantaneous power. In gearboxes, load fluctuations on the gearbox and gear defects are two major sources of vibration. Further at times, measurement of vibration in the gearbox is not easy because of the inaccessibility in mounting the vibration transducers. This analysis system can be used for measuring the characteristics for a perfectly working gearbox and use the data as a standard for measuring faults and defects in other gearboxes. The objective of this paper is to design and diagnose fault in the gearbox using motor current analysis system at different gear operations on different loads. Steady load conditions on the gearbox are tested for current signatures during different gear operations. Also found the minimum power required to run on different gears and gear ratio. The motor current analysis system can be used further to specify mainly faults in the gear, misalignment of meshed gears, loss of contact of the gears and bearing wear.

This paper employs the application of the wavelet transform (WT) to non-stationary gear vibration signals to investigate its sensitivity and limitations in detecting different gear faults. The detection and diagnostic capability of the wavelet analysis is compared to the Fourier-based analysis on the basis of experimental results. The artificially created defects included chipped teeth, and broken teeth as examples of local gear faults. In addition, gear distributed faults are also investigated. The sensitivity to fault severity, and to transducer orientation will be investigated. Adaptive Morlet wavelet filters are developed based upon the kurtosis maximization principle. The parameters in the Morlet wavelet function are optimized to match with the nature of the fault feature to be extracted from the different signals. The experimental results considered in this paper, show that the adaptive WT is very effective in the detection of both local and distributed gear faults. The WT is found to be very sensitive to fault severity. In addition the radial signals show more informative results than the axial signals when using either FFT or WT.

Vibration-based schemes are founded on the assumption that vibration signals from gearboxes measured using accelerometers reflect their condition accurately. A large number of vibration based techniques are used to make this reflection. They include various spectral analyses such as traditional Fourier transform, short-time Fourier transform, amplitude phase modulation and time synchronous averaging and non-parametric special estimation. Recently, Wavelet Transform (WT) has been proven to be more suitable for analysis of vibration signals, since most of the time-vibration signals have instantaneous impulse trains and exhibit a transient (non-stationary) nature. This paper uses an adaptive wavelet filter, based on the Morlet wavelet, applied on the torsional vibration data measured from a single-stage gearbox with artificially induced cracks in the gear. This is done to extract some parameters and check their diagnostic behavior in an effort to search for those with the most potential and appropriateness for future health monitoring schemes. The results demonstrate that the adaptive wavelet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early tooth cracks. Moreover the influence of crack depth, speed, and load on the wavelet entropy are interduced. Multi-hour tests were conducted and recordings were acquired using torsional vibration monitoring. The transitions in the wavelet entropy values with the recording time were highlighted suggesting critical changes in the operation of the gearbox.

Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. In this paper, a review is made of some current vibration analysis techniques used for condition monitoring in gear fault.

— Gear box plays vital role in automobile industry. Therefore, there is a strong demand for their reliable and safe operation. If any fault and failures occur in Gear box it can lead to excessive downtimes and generate great losses in terms of revenue and maintenance. Therefore, early fault detection needed for the protection of the Gear box. In the current scenario, the health monitoring of the Gear box are increasing due to its potential to reduce operating costs, enhance the reliability of operation and improve service to the customers. The on-line health monitoring involves taking measurements on a machine while it is in operating conditions in order to detect faults with the aim of reducing both unexpected failure and maintenance costs. In the present paper, a comprehensive survey of Gear box faults, Motor current signature analysis (MCSA) to monitor the gearbox away from its actual location has been discussed.