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...

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This p...

Bearing faults represent the most frequent mechanical faults in rotational machines. They are characterized by repetitive impacts between the rolling elements and the damaged surface. The time intervals between two impacts are directly related with the type and location of the surface fault. These time intervals can be elegantly analyzed within the framework of renewal point processes. With suc...

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...

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In...

This paper introduces a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. First features are extracted from amplitude demodulated vibration signals obtained from both normal and faulty bearings. The features are based on the reflection coefficients of the polynomial transfer function of the autoregressive model of the vibration signal. ...

Received Oct 30, 2014 Revised Dec 29, 2014 Accepted Jan 15, 2015 A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on...