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    The shock pulse measurements were carried out on test bearings to ensure that the bearing fault due to contaminated lubrication condition existed. Surface roughness measurements of a turned workpiece was carried out as an offline monitoring method. Talysurf-6 (Taylor Hobson) was used for this purpose.To minimise the effect of cutting tool wear and breakage on acoustic emission and surface roughness, care was taken to ensure cutting tool sharpness. For each set of experiments a fresh cutting tool insert was used; i.e., the tool was not allowed to wear more than a standard specification.

    4 Conclusion

      In this study, critical subsystems and components have bee identified for lathes using failure data.The application of condition monitoring techniques like vibration, acoustic emission (AE) and surface roughness monitoring have been successfully implemented for diagnosing faulty bearings in a lathe. We have reached the following conclusions:

     ·Headstock subsystem is critical because it faces a longer downtime and frequent failures of components like spindle bearings and gears.

     ·For defective bearing conditions, overall vibration levels at headstock spindle beatings are higher than those in defect free lathes. This increase in vibration level is much greater at higher feed and depth of cut values.

     ·For defective beating conditions, significant peaks at the beating fault frequencies are observed.

     ·Larger sized contamination particles increases surface waviness considerably. As a result, the vibration level increased considerably at larger particle sizes.

     ·AE levels show an increasing trend with an increase in feed rate and depth of cut.

     ·For defective bearing conditions, AE levels are higher than those measured under healthy conditions. The increase in AE levels is much greater for higher values of feed and depth of cut.

     ·For defective bearing condition, surface roughness value increases sharply.

     ·It is observed from the FAAS study that smaller contaminant particles ease the wear out of the bearing elements.

    Reference

    1. Kegg RL (1984) On-line machine and process diagnostics. Ann CIRP 32(2):469-478

    2. Bertele OV (1990) Why condition monitor? In: Condition Monitoring, Proceedings of the 3rd International Conference, Windsor, UK, 15-17 October, pp 3-12

    3. Neale MJ (1985) The benefit of condition monitoring. In: Condition monitoring of machinery and plant, Mechanical engineering publications Ltd., The Institution of Engineers, London, pp 25-30

    4. Martin KF (1994) A review by discussion of condition monitoring and fault diagnosis in machine tools. Int J Mach Tools Manuf 34(4): 527-551

    5. Saravanan S, Yadava GS, Rao PV (2003) Machine tool failure data analysis for condition monitoring application. In: Proceedings of the 1 l th National Conference on Machines and Mechanism, 18-20 December, liT Delhi, New Delhi, Allied Publishers Pvt. Limited, pp 552-558

    6. Harris CG, Williams JH, Davies A (1989) Condition monitoring of machine tools. Int J Prod Res 27 (9): 1945-1964

    7. Wang Y, Jia Y, Yu J, Zheng Y, Yi S (1999) Failure probabilistic model of CNC lathes. Reliab Eng Syst Safety 65(3):307-314

    8. Wang Y, Jia Y, Jiang W (2001) Early failure analysis of machining centres: case study. Reliab Eng Syst Safety 72(1):91-97

    9. Dai Y, Jia Y (2001) Reliability of a VMC and its improvement. Reliab Eng Syst Safety 72(1):99-102

    10. Johansson KE (1981) Field monitoring of NC-machines - a system approach in innovation for maintenance technology improvements. In: Proceedings of the Society for Machinery Failure Prevention Group (MFPG) 33rd Meeting, April

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