Browsing by Author "Sharma, R.K."

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An experimental investigation of oil film temperatures in elliptical profile journal bearing
(2013) Sehgal, R.; Chauhan, A.; Sharma, R.K.
Hydrodynamic journal bearings experience significant variation in oil film temperature. Reliable data of operating temperatures in these journal bearings are very useful and important for practical bearing designers and mathematical modellers. Here, an elliptical journal bearing has been tested to access the temperature rise at oil-bush interface with three grade oils at loads varying from 100 N to 600 N and speeds = 3000, 3500, 4000 rpm at constant oil supply pressure. The results show that with increase in load at constant speed and pressure, and with increase in speed at constant load and pressure, the oil film temperature increases in the central plane of both the lobes of the bearing for all grade oils under study. Further, it is observed that under the given operating conditions, oil 2 gives the coolest operation of the elliptical bearing under analysis. Copyright © 2013 Japanese Society of Tribologists.
Condition monitoring of roller bearing by K-star classifier and K-nearest neighborhood classifier using sound signal
(2017) Sharma, R.K.; Sugumaran, V.; Kumar, H.; Amarnath, M.
Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared. Copyright 2017 Tech Science Press.
Condition monitoring of roller bearing by K-star classifier and K-nearest neighborhood classifier using sound signal
(Tech Science Press sale@techscience.com, 2017) Sharma, R.K.; Sugumaran, V.; Kumar, H.; Amarnath, M.
Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared. © Copyright 2017 Tech Science Press.
An experimental investigation of oil film temperatures in elliptical profile journal bearing
(2013) Sehgal, R.; Chauhan, A.; Sharma, R.K.
Hydrodynamic journal bearings experience significant variation in oil film temperature. Reliable data of operating temperatures in these journal bearings are very useful and important for practical bearing designers and mathematical modellers. Here, an elliptical journal bearing has been tested to access the temperature rise at oil-bush interface with three grade oils at loads varying from 100 N to 600 N and speeds = 3000, 3500, 4000 rpm at constant oil supply pressure. The results show that with increase in load at constant speed and pressure, and with increase in speed at constant load and pressure, the oil film temperature increases in the central plane of both the lobes of the bearing for all grade oils under study. Further, it is observed that under the given operating conditions, oil 2 gives the coolest operation of the elliptical bearing under analysis. Copyright 2013 Japanese Society of Tribologists.