Abstract Based on the chosen properties of an induction motor, a random forest (RF) classifier, machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct states was used to evaluate suggested methodology. The primary objective research defect detection accuracy RF classifier. First, run loops that cycle over each featur...

To fully utilize the fault information and improve diagnosis accuracy of rolling bearings, a multisensor feature fusion method is proposed. The contains two steps. First, intrinsic mode function (IMF) each sensor vibration signal calculated by variational decomposition (VMD), redundant such as noise eliminated. Then, time-domain, frequency-domain multiscale entropy features are extracted based ...

Abstract Rolling bearing is an indispensable part of the contemporary industrial system, and its working conditions affect state entire system. Therefore, there great engineering value to researching improving fault diagnosis technology rolling bearings. However, with involvement whole mechanical equipment, we need have a large quantity data support accuracy diagnosis, while efficiency classica...

--The Motor Current Signature Analysis (MCSA) is considered the most popular fault detection method now a day because it can easily detect the common machine fault such as turn to turn short ckt, cracked /broken rotor bars, bearing deterioration etc. The present paper discusses the fundamentals of Motor Current Signature Analysis (MCSA) plus condition monitoring of the induction motor using MCS...

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccura...

In recent years, intelligent condition-based monitoring of rotary machinery systems has become a major research focus machine fault diagnosis. monitoring, it is challenging to form large-scale well-annotated dataset due the expense data acquisition and costly annotation. The generated have large number redundant features which degraded performance learning models. To overcome this, we utilized ...

The detection of faults related to the optimal condition induction motors is an important task avoid malfunction or loss motor, thus avoiding high repair replacement costs and in efficiency process which they belong. These are not limited a single area; mechanical electrical problems can cause fault. Specifically, bearing motor subjected several effects that faults, significant breakdowns machi...