bearing fault detection based on maximum likelihood estimation and optimized ann using the bees algorithm

Authors

behrooz attaran

afshin ghanbarzadeh

abstract

rotating machinery is the most common machinery in industry. the root of the faults in rotating machinery is often faulty rolling element bearings. this paper presents a technique using optimized artificial neural network by the bees algorithm for automated diagnosis of localized faults in rolling element bearings. the inputs of this technique are a number of features (maximum likelihood estimation values), which are derived from the vibration signals of test data. the results shows that the performance of the proposed optimized system is better than most previous studies, even though it uses only two features. effectiveness of the above method is illustrated using obtained bearing vibration data.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm

Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...

full text

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA)...

full text

GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation

Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result w...

full text

Joint Angle-delay Estimation Based on Smoothed Maximum-likelihood Algorithm

In this paper, a novel maximum likelihood algorithm for joint angle and delay estimation is developed to identify the specular components of channel fading for uniform linear array based on the physical propagation channel model. Frequency domain presmoothing is applied to the structured frequency transfer matrix before the estimation procedure in order to utilize substantial observations. Iter...

full text

Maximum likelihood estimation from fuzzy data using the EM algorithm

A method is proposed for estimating the parameters in a parametric statistical model when the observations are fuzzy and are assumed to be related to underlying crisp realizations of a random sample. This method is based on maximizing the observeddata likelihood defined as the probability of the fuzzy data. It is shown that the EM algorithm may be used for that purpose, which makes it possible ...

full text

Parameter Estimation of Loranz Chaotic Dynamic System Using Bees Algorithm

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...

full text

My Resources

Save resource for easier access later


Journal title:
journal of applied and computational mechanics

Publisher: shahid chamran university of ahvaz

ISSN 2383-4536

volume 1

issue 1 2014

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023