This paper describes the design and performance of external gear pumps with a tooth profile based on the cycloid tooth profile. A method has been developed to calculate accurately the displacement volume of external gear pumps having a non-involute tooth profile and an involute tooth profile. Using this method, the displacement volumes of external gear pumps with an involute tooth profile, a cycloid tooth profile, an involute-cycloid composite tooth profile and a modified cycloid tooth profile are calculated. Then, the merits and the demerits of these tooth profiles are discussed with respect to the displacement volume. In addition, a gear pump with an involutecycloid composite tooth profile was designed and manufactured, and the displacement volume of the pump was measured. Consequently, it was confirmed from both the calculated and the experimental results that the displacement volume of the gear pump with an involute-cycloid composite tooth profile is about 20 per cent larger than that of the conventional gear pump with an involute tooth profile.

The operation status of gear device can directly affect the working conditions of the whole machine system. Thus, it is crucial to detect the gear damage as early as possible to prevent the system from malfunction. This paper proposes an intelligent diagnosis method for gear damage using multiple classifiers of support vector machines with extracted failure feature vector. The vibration signal of gear box is employed as the analytical data in this paper. In order to illustrate the representative characters of gear conditions, statistical parameters and characteristic amplitude ratios of frequency bands are extracted from the vibration signals in time-domain and frequency-domain respectively, which are served as failure feature vector for the following diagnosis. Moreover, to reduce the dimensions of the failure feature vector, the technology of principal component analysis is adopted to transform the original failure feature vector into a new smaller set of variables as inputs to classifiers of support vector machines. In order to classify different types of gears, multiple classifiers of support vector machines based on the binary tree are designed. The validity of this approach is investigated by the experiment. Three kinds of gears, namely normal gear, spot damaged gear and pitted gear, are tested on the power circulating type gear testing machine. The vibration accelerations of gear box are measured as original data. Most of the samples are correctly classified by the provided method, which demonstrates the effectiveness of the proposed method on the application of gear damage diagnosis. IntroductionSince minor gear damage may cause serious failures of the entire equipment, early detection of gear damage is one of the important measures to prevent the machine system from malfunction. Analyzing the vibration signal adopted from the gear or gear box is one of the effective methods to diagnose gear failures. Researchers have done countless studies in this respect and have developed many methods based on the analysis of vibration signal in time domain, frequency domain and time-frequency domain. In frequency domain, it is well known that the fault condition of gears can be observed at the meshing frequency and its harmonics, together with sidebands by spectra analysis of vibration signal (Dalpiaz, et al., 2000). However, the acquired signals are always inevitably interfered by the vibration of other components in the system, or the environment disturbances. Therefore, in order to strengthen the characteristics of the useful signal, researchers have developed many other signal processing techniques for gear fault detection, such as time synchronous average method in time domain, wavelet transform and Hilber transform techniques in time-frequency domain, advanced statistical approaches and so on. These techniques have been satisfactorily applied to both fault detection and identification of the damaged gear (Houjoh, et al., 2007, Tanaka, et al., 2012, Wang, et al., 2010 Fan, Ikejo, Nagamura, Kawada and Has...

To date, elliptical gear has been commonly used in automobile, automatic machinery, pumps, flow meters and printing presses for its particular non-uniform rotation. However, the dynamic characteristics of elliptical gears have not been clarified yet. In this study, The calculation as well as the experiment of two elliptical gears, which are a single elliptical gear and a double elliptical gear, is carried out to analyze the dynamic characteristics of elliptical gears. General factors including the torque, the rotation speed and the tooth root stress of the test gears are investigated. According to the analysis conducted in this study, the dynamic input torque variation of elliptical gear becomes larger along with the increase of operating gear rotation speed and the experimental one increases much faster than the calculated one over the Critical Rotation Speed of Tooth Separation (CRSTS) of elliptical gear. The experimental input rotation speed varies according to the variation of input torque, leading to the difference between the experimental output rotation speed and the desired one. The calculation results of the CRSTS of elliptical gears are almost equal to the experimental ones. The dynamic load variation ratios of elliptical gear at different angular position as well as their changing trends with operating gear rotation speed are quite different from each other. And the experimental dynamic load variation ratios of elliptical gear show difference from the calculated ones because of tooth separation and tooth impact. The agreement of the calculation and experimental results proves the validity of this study.

We propose herein an in situ method to remotely diagnose gear-tooth damage using scattering of a laser beam. The proposed method provides early and accurate diagnosis of the gear-tooth-surface condition. A tooth surface is first irradiated at oblique incidence by a zone-covering laser beam, and the zone is scanned along the surface of the gear tooth by the rotation of the gear. By analyzing variations in laser scattering between benchmark data and the current data, we can estimate the condition of the gear-tooth surface in terms of abnormal abrasion, pitting, spalling, etc. To test the method, we used it to remotely detect gear-teeth pitting in an experiment during which we simultaneously measured the vibration and sound of the gearbox and pedestal. Our analysis shows that the laser-scattering measurements reveal pitting more clearly and at an earlier stage than does the vibration and noise measurements. Therefore, we conclude that the proposed method estimates the tooth-surface condition with sufficient accuracy to assess the lifetime of the gear. Furthermore, we used the proposed method to diagnose real gear-teeth in situ in practical gearboxes to verify that the method yields an accurate diagnosis of the tooth surface of a lubricated gear. We found that by attaching a cover to the laser receiver; the remote measurements were unaffected by the choice or method of lubrication. For force-feed lubrication, pitting was detected for every speed range under 1800 rpm. These results demonstrate that the proposed method can diagnose the tooth surface of lubricated gears in practical gearboxes. Finally, we developed an automatic damage diagnosis method that is capable of detecting pitting by analyzing the laser-scattering signal from damaged teeth combined with that from the same teeth prior to the damage.

Gear is one of the most important and commonly used components in machine system. Some gear failure may lead to fatal damage of the entire system, or even huge losses in industrial production. Early detection of gear damage is crucial to prevent the machine system from malfunction. This paper provides an intelligent diagnosis method for gear damage based on techniques of empirical mode decomposition and support vector machines. By the data processing of empirical mode decomposition, the original signal are decomposed into a finite set of intrinsic mode functions with frequency bands ranging from high to low. The characteristic energy ratios of intrinsic mode functions are acquired as representative parameters of the signal. Furthermore, statistical parameters of standard deviation, root mean square value, kurtosis and skewness are extracted from the original signal. Characteristic energy ratios and statistical parameters are combined as failure feature vectors to be input to the support vector machines classifiers for gear damage diagnosis. The validity of the presented method is confirmed by the application of monitoring gear conditions during the cyclic fatigue test. The vibration accelerations of gear box are acquired to illustrate the progression of pitting damage. Most of the gear conditions are identified, indicating the effectiveness of the proposed method.