**6. Conclusion**

The orthotropic formation and the transversely isotropic formation are the typical formations encountered frequently in drilling engineering. Based on rock-bit interaction model, the two parameter equations have been derived for us to calculate the anisotropic drilling characteristics of them as soon as rock drillability anisotropy of the formations is evaluated quantificationally by using the oilfield data.

The correlation between rock drillability anisotropy and acoustic wave anisotropy of the formation can be matched to each other by an exponential function which is of the best extrapolative performance and relativity. Coefficients in the model are various for different formations to be drilled.

To a certain extent, the research results presented here have shown a new way for us to evaluate conveniently rock drillability anisotropy of the formation by using well logging or seismic data. Case study shows that this evaluation method is better for applications of rock drillability anisotropy of the formation in drilling engineering.

**8** 

**Machinery Faults Detection** 

Dong Sik Gu and Byeong Keun Choi

*Gyeongsang National University* 

*Republic of Korea* 

**Using Acoustic Emission Signal** 

Application of the high-frequency acoustic emission (AE) technique in condition monitoring of rotating machinery has been growing over recent years. This is particularly true for bearing defect diagnosis and seal rubbing (Mba et al., 1999, 2003, 2005; Kim et al., 2007; Siores & Negro, 1997). The main drawback with the application of the AE technique is the attenuation of the signal and as such the AE sensor has to be close to its source. However, it is often practical to place the AE sensor on the non-rotating member of the machine, such as the bearing or gear casing. Therefore, the AE signal originating from the defective component will suffer severe attenuation before reaching the sensor. Typical frequencies

While vibration analysis on gear fault diagnosis is well established, the application of AE to this field is still in its infancy. In addition, there are limited publications on application of AE to gear fault diagnosis. Siores explored several AE analysis techniques in an attempt to correlate all possible failure modes of a gearbox during its useful life. Failures such as excessive backlash, shaft misalignment, tooth breakage, scuffing, and a worn tooth were seeded during tests. Siores correlated the various seeded failure modes of the gearbox with the AE amplitude, root mean square, standard deviation and duration. It was concluded that the AE results could be correlated to various defect conditions (Siores et al., 1997). Sentoku correlated tooth surface damage such as pitting to AE activity. An AE sensor was mounted on the gear wheel and the AE signature was transmitted from the sensor to data acquisition card across a mercury slip ring. It was concluded that AE amplitude and energy increased with increased pitting (Sentoku, 1998). In a separated study, Singh studied the feasibility of AE for gear fault diagnosis. In one test, a simulated pit was introduced on the pitch line of a gear tooth using an electrical discharge machining (EDM) process. An AE sensor and an accelerometer for comparative purposes were employed in both test cases. It was important to note that both the accelerometer and AE sensor were placed on the gearbox casing, it was observed that the AE amplitude increased with increased rotational speed and increased AE activity was observed with increased pitting. In a second test, periodically occurring peaks were observed when natural pitting started to appear after half an hour of operation. These AE activities increased as the pitting spread over more teeth. Singh concluded that AE could provide earlier detection over vibration monitoring for pitting of gears, but noted it could not be applicable to extremely high speeds or for

associated with AE activity range from 20 kHz to 1 MHz.

**1. Introduction** 
