Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration s...

In order to study the on-line fault monitoring and diagnosing for the rolling bearing this paper proposes a resonant demodulation measurement with an adaptive frequency selection based on LabVIEW. The wavelet packet function is used to decompose and reconstruct the measured vibration signal to extract the fault information accurately under the noise background. The kurtosis value of the signal ...

The rolling element bearing is a key part of machines. The accurate and timely diagnosis of its faults is critical for predictive maintenance. Most researches have focused on the fault location identification. To estimate the fault severity accurately, this paper focuses on the study of roller bearing vibration amplitude under increasing radial clearances due to inevitable wear using the modula...

Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer rac...

Considering backlash, time-varying mesh stiffness and radial clearance of bearing, nonlinear dynamic model of gear bearing flexible shaft system is established taking into account breathing crack in shaft and tooth wear. Nonlinear dynamic equations are solved by Runge-Kutta method. Effect of backlash, crack in shaft and tooth wear faults on the nonlinear dynamic behavior of gear-shaft-bearing s...

This paper presents an intelligent diagnosis method for a rolling element bearing; the method is constructed on the basis of possibility theory and a fuzzy neural network with frequency-domain features of vibration signals. A sequential diagnosis technique is also proposed through which the fuzzy neural network realized by the partially-linearized neural network (PNN) can sequentially identify ...

Bearings are widely used in rotating machines. Its health status is a significant index to indicate whether machines run continually or not. Detecting the bearing faults timely is very important for the maintenance decision making. In this paper, a new fault diagnosis method based on envelope analysis and Euclidean Distance is developed. Envelope analysis is used to enable the fault frequencies...

This paper proposes a reduct construction method based on discernibility matrix simplification. The method works with genetic algorithm. To identify potential problems and prevent complete failure of bearings, a new method based on rule-based classifier ensemble is presented. Genetic algorithm is used for feature reduction. The generated rules of the reducts are used to build the candidate base...

Rolling bearing is an important part in mechanical system and faults occur frequently with vibration noise. Empirical mode decomposition (EMD) is a tool for nonlinear and non-stationary signals analysis. However, the major drawbacks of EMD are mode mixing problem, ensemble empirical mode decomposition (EEMD) provides a new tool for signal analysis, and it is an improved technique of EMD. In ord...

This study is primary to develop relevant techniques for the bearing of wind turbine, such as the intelligent monitoring system, the performance assessment, future trend prediction and possible fault classification etc. The main technique of system monitoring and diagnosis is divided into three algorithms, such as the performance assessment, performance prediction and fault diagnosis, respectiv...