A histogram statistical method for the detection of localized faults in deep groove ball bearing (original) (raw)
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Vibration Based Fault Detection of Deep Groove Ball Bearing Using Data Mining Algorithm
SSRN Electronic Journal, 2017
Deep groove ball bearing is a heart of rotating machinery. So early fault detection of bearing can prevent failures of the machineries. Vibration signals collected from bearing carries useful information about its health. This paper presents a methodology to identify various faults in deep groove ball bearing from vibration signals acquired from different bearing condition. Features such as RMS, Variance, Mean, Crest Factor,Kurtosis and Skewness are calculated from time domain for various bearing conditions such as normal bearing, fault at inner race, fault at outer race, and fault on ball. The dataset of the various bearing condition is applied on five classifiers such as Naive Bayes (NB), Multi-Level Perceptron (MLP), K-Star, J-Rip, and J-48 using data mining algorithm WEKA. The distribution of training and testing dataset is carried out using WEKA. In a result, statistical parameters generated from classification algorithms are compared to determine the correctly classified instances and to find the efficient classification algorithm among five algorithms. Result shows that K-Star gives highest accuracy for training as well as for testing among all classification algorithms.
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Ball Bearing Fault Detection Using Vibration Parameters
International journal of engineering research and technology, 2013
Bearing is an indispensible element of almost any rotating machinery. These bearings in due course of time undergo damage which may be confined to inner race, outer race, ball, cage, or all of these. Using various state of the art technologies like Vibration Analysis, Shock Pulse Method, and Acoustic Emission, these bearing faults can be identified, without dismantling the machine. Among all these vibrational analysis of bearing signature is a classical technique. This paper presents an experimental study of bearing vibration and application of FFT spectra as a smart tool for diagnosis and identification of bearing faults like inner race defect, outer race defect and ball defect. Keywords— Vibrational Signal, FFT spectra, Ball bearing, Ball defect, Inner race defect, Outer race defect.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/detection-of-the-distributed-defects-on-inner-outer-race-of-ball-bearing-using-vibration-analysis https://www.ijert.org/research/detection-of-the-distributed-defects-on-inner-outer-race-of-ball-bearing-using-vibration-analysis-IJERTV3IS110240.pdf Bearings are the very important component of rotating machinery. Bearing failure is one of the most common faults in industrial machines. Proper condition monitoring is therefore of the highest importance. The rolling bearing is a machinery component that plays a very important role, since it dominates the machine performance. Small defect in the bearing results in dangerous failure of machinery. If defect is detected on one of the bearings, not only the machine, but also the assembly line stops and the deriving costs may be extremely high. Therefore it is very important to detect defects of the bearing before they cause upcoming damage and decrease expensive downtime. The vibration analysis is the most frequently used technique for monitoring of the bearings. This technique can give early information about progressing malfunctions and can be used for future monitoring purpose. This paper describes the suitability of vibration analysis technique to detect the distributed defects in bearing. In this paper comparison of vibration spectrum of healthy and defective bearings is carried out to find defect on bearing. Results show that comparison of vibration spectrum of healthy bearing and vibration spectrum of defective bearing is helpful to recognize the defects in the bearing.
Industrial Lubrication and Tribology, 2008
PurposeThis paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and changing the ANN structure.Design/methodology/approachAn algorithm is used to select the best subset of features to boost the success of detecting healthy and faulty ball. Some of the important parameters of the ANN are also optimized to make the classifier achieve the maximum performance.FindingsIt was found that better accuracy can be obtained for ANN with fewer inputs.Research limitations/implicationsThe method can be used for other machinery condition‐monitoring systems which are based on ANN.Practical implicationsThe results are useful for bearing fault detection systems designers and quality check centers in bearing manufacturing companies.Originality/valueThe algorithm used in this research is faster than in previous studies. Changing ANN parameters improved the results. The system was examined using e...
Diagnostics of Bearing Defects Using Vibration Signal
International Journal of Computer and Electrical Engineering, 2012
Current trend toward industrial automation requires the replacement of supervision and monitoring roles traditionally performed by humans with artificial intelligent systems. This paper studies the use of hybrid intelligent system in Diagnosis of rotating machinery bearing defects. Vibration signals were collected for normal and various faulty conditions of the ball/roller bearing of the machinery. The acquired signal was processed with FFT and PSD in MATLAB to obtain the characteristic amplitudes from the frequency domain spectra of the signals. The obtained amplitude vector was used to train an adaptive neurofuzzy inference system (ANFIS) to classify and recognize normal and different faulty states. The system was tested, checked and validated with different sets of signal data. The validation data attests to the structural stability and performance of the system.
Experimental Investigation of Fault Detection in Ball Bearings using Vibration Signature Analysis
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Ball bearings are machine elements used in rotary machines to reduce friction and support radial and axial loads. During operation there are chances that these elements develop localized faults most probably on the races. To avoid catastrophic failure it is required to monitor the condition of the bearings and detect faults in it. Vibration Signature Analysis is widely used for condition monitoring of mechanical elements such as bearings, gears etc. Envelope detection is a signal processing technique for demodulation of vibration signals, it is a useful tool for the identification of faults. Time domain signals and order spectrum of healthy and faulty bearings were investigated. Statistical parameters such as Kurtosis and Root Mean Square of the time domain signals are also used to identify the presence of faults.