Application of Mathematical Morphology in Diesel Vibration Signals

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-26535-3_57 ◽

2015 ◽

pp. 500-509

Author(s):

Hongxia Pan ◽

An Dong ◽

Manliang Cao

Keyword(s):

Mathematical Morphology ◽

Vibration Signals

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DE-NOISING TECHNIQUE FOR VIBRATION SIGNALS OF ROTATING MACHINERY BASED ON MATHEMATICAL MORPHOLOGY FILTER

Journal of Mechanical Engineering ◽

10.3901/jme.2006.04.127 ◽

2006 ◽

Vol 42 (04) ◽

pp. 127 ◽

Cited By ~ 20

Author(s):

Aijun HU

Keyword(s):

Mathematical Morphology ◽

Rotating Machinery ◽

Vibration Signals ◽

Morphology Filter

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Mathematical morphology for shape description

Journal de Physique IV (Proceedings) ◽

10.1051/jp42002004 ◽

2002 ◽

Vol 12 (1) ◽

pp. 87-116 ◽

Cited By ~ 1

Author(s):

M. Schmitt

Keyword(s):

Mathematical Morphology ◽

Shape Description

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Diagnostic analysis of vibration signals using adaptive digital filtering techniques

10.2514/6.1983-883 ◽

1983 ◽

Author(s):

R. JEWELL ◽

J. JONES ◽

J. PAUL

Keyword(s):

Digital Filtering ◽

Diagnostic Analysis ◽

Vibration Signals ◽

Filtering Techniques

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Olive Tree Identification Method by Spatial Statistics Analysis and Mathematical Morphology

i-manager’s Journal on Future Engineering and Technology ◽

10.26634/jfet.3.3.638 ◽

2008 ◽

Vol 3 (3) ◽

pp. 5-13

Author(s):

Mounir Salhi ◽

◽

A. Kallel ◽

N. Derbel ◽

◽

...

Keyword(s):

Spatial Statistics ◽

Mathematical Morphology ◽

Olive Tree ◽

Statistics Analysis ◽

Identification Method

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COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

June-2020 - International Journal of Engineering Sciences & Research Technology ◽

10.29121/ijesrt.v9.i8.2020.8 ◽

2020 ◽

Vol 9 (8) ◽

pp. 66-72

Keyword(s):

Fault Diagnosis ◽

Resonance Frequency ◽

Background Noise ◽

Maximum Correlation ◽

Vibration Signals ◽

Filter Length ◽

Frequency Bands ◽

Noise Interference ◽

Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

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