Fourier Harmonic Regression Method for Bearing Condition Monitoring using Vibration Measurements

Srivani, A.; Arunkumar, T.; DenisAshok, S.

doi:10.1016/j.matpr.2018.02.193

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Vibration measurements and analysis is one of the most effective fault diagnosis techniques especially suitable for rolling element bearings as there is a periodic shock impulse appears every time as one component contacts another if there is a local fault [1,2]. In order to detect and recognize the fault signal, different signal analysis methods were developed in time domain analysis [3,4], frequency domain analysis [5], and time-frequency domain analysis by various researchers. Statistical parameters play also a significant role in the detection of a fault of rotary machines.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

2019

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The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

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Section: Introductionmentioning

confidence: 99%

Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

2019

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“…In 1977, Jong was the first researcher to conduct research related to the Fourier series which discusses the transformation of the Fourier series for smoothing of the density function in the spectral estimator [3] . In the later years, it followed by several researchers, with the form of Fourier series given as of

[4] , [5] , [6] , [7] . However, Bilodeau in 1992 developed the Fourier series function for a smoothing model in nonparametric regression by modifying the function.…”

Section: The Model and Its Estimationmentioning

confidence: 99%

A method for parameter hypothesis testing in nonparametric regression with Fourier series approach

Ramli,

Budiantara,

Ratnasari

2023

MethodsX

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Fourier Harmonic Regression Method for Bearing Condition Monitoring using Vibration Measurements

Cited by 2 publications

References 16 publications

Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

A method for parameter hypothesis testing in nonparametric regression with Fourier series approach

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