Automated fault diagnosis of rolling element bearings based on morphological operators and M-ANFIS

Rajabi, Saeed; Samanazari, Mehdi; Momeni, Hamid Reza; Ramezani, Amin

doi:10.1109/iraniancee.2016.7585805

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The AMDF method is susceptible to random noise [14,15], resulting in the notch not being obvious enough, so it needs a filtering method to denoise the signal in advance. Among the many filtering methods, mathematical morphology filtering is a kind of time-domain nonlinear filtering method with clear physical meaning, practicality and high efficiency, which is widely employed in some fields, such as power system signal processing [16], mechanical fault diagnosis [17][18][19], electrocardiogram (ECG) measurement [20] and image processing [21][22][23]. Self-complementation Top-Hat (STH) transform is a denoising method based on some operations of that, which is good at suppressing the background noise to a great extent and retaining the details of the original signal [24].…”

Section: Introductionmentioning

confidence: 99%

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

Zhi-gang

Wang

2019

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Period detection technology for weak characteristic signals is very important in the fields of speech signal processing, mechanical engineering, etc. Average magnitude difference function (AMDF) is a widely used method to extract the period of periodic signal for its low computational complexity and high accuracy. However, this method has low detection accuracy when the background noise is strong. In order to improve this method, this paper proposes a new method of period detection of the signal with single period based on the morphological self-complementary Top-Hat (STH) transform and AMDF. Firstly, the signal is de-noised by the morphological self-complementary Top-Hat transform. Secondly, the average magnitude difference function of the noise reduction sequence is calculated, and the falling trend is suppressed. Finally, a calculating adaptive threshold is used to extract the peaks at the position equal to the period of periodic signal. The experimental results show that the accuracy of periodic extraction of AMDF after Top-Hat filtering is better than that of AMDF directly. In summary, the proposed method is reliable and stable for detecting the periodic signal with weak characteristics.

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Section: Introductionmentioning

confidence: 99%

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

Zhi-gang

Wang

2019

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“…Dong et al [13] effectively identified the rotating machinery fault by using a morphological filter, which is optimized by the particle swarm optimization algorithm and nonlinear manifold learning algorithm local tangent space alignment. Rajabi et al [14] proposed a novel approach by combining mathematical morphology and multioutput adaptive neurofuzzy inference system classifier. Li et al [15] established a multiscale morphological filtering of the vehicle system model, which displays considerable noise reduction performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

Jun

Shen

Zhu

2017

International Journal of Rotating Machinery

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Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.

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MANFIS based SMART home energy management system to support SMART grid

G.K¹,

Jasper

2020

Peer-to-Peer Netw. Appl.

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Automated fault diagnosis of rolling element bearings based on morphological operators and M-ANFIS

Cited by 4 publications

References 17 publications

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

MANFIS based SMART home energy management system to support SMART grid

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