Wavelets and principal component analysis method for vibration monitoring of rotating machinery

Bendjama, Hocine; Boucherit, Mohamad S.

doi:10.15632/jtam-pl.54.2.659

Cited by 8 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These determine the ability of wavelet decomposition to capture transient properties in a signal. This research uses db4 wavelet that is suitable for vibration signals analysis [7,12].…”

Section: Wavelet Transformation-multiresolution Analysismentioning

confidence: 99%

See 1 more Smart Citation

Detection of gear defects using method of wavelet decomposition and cross-correlation - Experiment to develop a feasible detection method

Hidayat

Santoso

2018

MATEC Web of Conferences

View full text Add to dashboard Cite

Detection of machine component failure is very important to be properly applied in a maintenance program in industries. The objective of this research is to detect gear fault using wavelet transforms. The vibration signal is acquired with accelerometer mounted at bearing houses of 2 parallel shafts with 2 spur gears (28 tooth). The gears are rotated at 1200 RPM and the spectrum is displayed. The spectrum cannot indicate gear mesh frequencies (GMF), because they are covered with frequencies such as natural and harmonic frequencies of rotating shaft. This research have developed a method to obtain GMF using wavelet decomposition and cross correlation. The results showed that with FFT applied to cross-correlation of wavelet detil components, spectrum of visible and distinctable GMF has been obtained.

show abstract

“…These determine the ability of wavelet decomposition to capture transient properties in a signal. This research uses db4 wavelet that is suitable for vibration signals analysis [7,12].…”

Section: Wavelet Transformation-multiresolution Analysismentioning

confidence: 99%

“…Wavelet-based signal processing for gear fault detection had been investigated by [4] and [5]. Further studies of wavelet transform applications on vibration signals for gear fault detection have been also performed by [6] and [7] that discuss of wavelet decomposition and feature extraction using PCA.…”

Section: Introductionmentioning

confidence: 99%

Detection of gear defects using method of wavelet decomposition and cross-correlation - Experiment to develop a feasible detection method

Hidayat

Santoso

2018

MATEC Web of Conferences

View full text Add to dashboard Cite

show abstract

“…The decomposition of the signals produces approximations and detail levels with different frequency bands by using a successive low-pass and high-pass filtering. These detail levels will not lose their information in the time domain (Bendjama et al 2015). However, useful information can be obtained from the subbands of the dominant frequencies, so statistical measurements of the subbands are representative of these detail levels.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

Abbagoni¹,

Yeung²

2016

Meas. Sci. Technol.

View full text Add to dashboard Cite

The identification of flow pattern is key issue in multiphase flow which encountered in the petrochemical industry. Gas-liquid two-phase flow is difficult to identify the gas-liquid flow regimes objectively. This paper presents a feasibility of a clamp-on instrument for objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and artificial neural network. It is on recording and processing of the ultrasonic signals reflected from the two-phase flow. Experimental data obtained on a horizontal test rig with total pipe length of 21 m long and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer Perceptron Neural Networks (MLPNNs) used for developing the classification model. The classifier requires features as input which is representative of the signals. Ultrasound signal features extracted by applying both power spectral density (PSD) and discrete wavelet transforms (DWT) methods to the flow signals. A classification scheme of "1-of-C coding method for classification" was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using output of a first level networks features as input features. Addition of the two network models provided a combined neural network models which has achieved higher accuracy than single neural network models. Classification accuracies evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed three datasets out of 24 test datasets of the classification and scored 87.5% accuracy. (2) With the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated success of a clamp-on ultrasound sensor for flow regime classification and it would be possible in industry practice. It is considerably more promising than other techniques as it uses of non-invasive and nonradioactive sensor.

show abstract

“…Visibly noticed are revolution methods based on mechanical signal processing, which are divided into two main categories, detection and diagnosis, and are based on time-frequency methods and temporal methods or a combination of both. Thus, many methods are born, the scalar indicators such as kurtosis, skew, crest factor (Dron et al, 2004;Pachaud et al, 1997), demodulation and detection of the envelope (Sheen, 2004(Sheen, , 2008, amplitude modulation (Stack et al, 2004), detection of vibration modes (Rizos et al, 1990), de-noising vibratory signals , the spectral density analysis (Krejcar and Frischer, 2011), the Fast Fourier Transform (Lenort, 1995) (Bendjama and Boucherit, 2016), blind source separation (Wang et al, 2014), fuzzy logic (Liu et al, 1996). El-Thalji and Jantunen (2015) and Rai and Upadhyay (2016) reviewed almost all the techniques used in the domain predicting defects.…”

Section: Introductionmentioning

confidence: 99%

A new method for automatic defects detection and diagnosis in rolling element bearings using Wald test

Chiter

Zegadi

Bekka

et al. 2018

jtam

View full text Add to dashboard Cite

To detect and to diagnose, the localized defect in rolling bearings, a statistical model based on the sequential Wald test is established to generate a "hypothetical" signal which takes the state zero in absence of the defect, and the state one if a peak of resonance caused by the defect in the bearing is present. The autocorrelation of this signal allows one to reveal the periodicity of the defect and, consequently, one can establish the diagnosis by comparing the frequency of the defect with the characteristic frequencies of the bearing. The originality of this work is the use of the Wald test in the signal processing domain. Secondly, this method permits the detection without considering the level of noise and the number of observations, it can be used as a support for the Fast Fourier Transform. Finally, the simulated and experimental signals are used to show the efficiency of this method based on the Wald test.

show abstract

Wavelets and principal component analysis method for vibration monitoring of rotating machinery

Cited by 8 publications

References 30 publications

Detection of gear defects using method of wavelet decomposition and cross-correlation - Experiment to develop a feasible detection method

Detection of gear defects using method of wavelet decomposition and cross-correlation - Experiment to develop a feasible detection method

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

A new method for automatic defects detection and diagnosis in rolling element bearings using Wald test

Contact Info

Product

Resources

About