Detection of bearing faults from raw time domain or frequency domain data is extremely difficult in view of the fact that there are a very large number of frequency components present in these signals. For this reason, other specialized signal processing techniques have been tried out. Experiments were carried out on a specially fabricated bearing test rig with variable speed drive and hydrauli...

Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the U.S. into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to moni...

Symmetrical nature of mean of electrical signals during normal operating conditions is used in the fault detection task for dependable, robust, and simple fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A fault detection task is complete...

With the developments in improved computation power and vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes machinery require different modeling methods focusing on automated learning deployment. In this context, deep provides possibilities industrial diagnostics to achieve performance efficiency. applications c...

Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals....