Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations

The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%.

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Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

ASME 2009 International Manufacturing Science and Engineering Conference, Volume 2 ◽

10.1115/msec2009-84343 ◽

2009 ◽

Author(s):

Young-Sun Hong ◽

Gil-Yong Lee ◽

Young-Man Cho ◽

Sung-Hoon Ahn ◽

Chul-Ki Song

Keyword(s):

Wavelet Packet ◽

Position Effect ◽

Vibration Signal ◽

Industrial Applications ◽

Wavelet Packet Transform ◽

Bevel Gear ◽

Diagnostic Methods ◽

Vibration Signals ◽

Rotary Machinery ◽

Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

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Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft

10.20855/ijav.2019.24.31190 ◽

2019 ◽

Vol 24 (3) ◽

pp. 418-425

Author(s):

Cristina Cristina Castejon ◽

Marıa Jesus Gomez ◽

Juan Carlos Garcia-Prada ◽

Eduardo Corral

Keyword(s):

Wavelet Transform ◽

Crack Detection ◽

Wavelet Packet ◽

Critical Role ◽

Wavelet Packet Transform ◽

Discrete Wavelet ◽

Distribution Analysis ◽

Rotating Shaft ◽

Vibration Signals ◽

Machine Monitoring

Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several factors must be selected and taken into account in the Wavelet Transform application such as the level of decomposition, the suitable mother wavelet, and the level basis or features. In this work, the dynamic response of a shaft with different levels of crack is studied. The evolution of energy of the vibration signals obtained from the rotating shaft and the frequencies where maximum increments of energy appear with the crack are analyzed. The results allow the conclusion that changes in energies computed by means of the Wavelet Packet Transform can be successfully used for crack detection.

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Feature extraction of vibration signals based on wavelet packet transform

Chinese Journal of Mechanical Engineering ◽

10.3901/cjme.2004.01.025 ◽

2004 ◽

Vol 17 (01) ◽

pp. 25 ◽

Cited By ~ 3

Author(s):

Junpeng Shao

Keyword(s):

Feature Extraction ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Vibration Signals

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Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS

Advances in Materials Science and Engineering ◽

10.1155/2017/2759020 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Adel T. Abbas ◽

Mohanad Alata ◽

Adham E. Ragab ◽

Magdy M. El Rayes ◽

Ehab A. El Danaf

Keyword(s):

Experimental Data ◽

Surface Roughness ◽

Regression Analysis ◽

High Strength Steel ◽

Removal Rate ◽

High Strength ◽

Cutting Parameters ◽

Turning Operations ◽

Cnc Turning ◽

Accuracy And Repeatability

The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (Ra), and material removal rate (MRR). The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected) as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System) estimations.

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A Detection Method for Friction Vibration Based on Harmonic Wavelet Packet Transform and Cross-Correlation Analysis

Journal of Vibration and Acoustics ◽

10.1115/1.4038865 ◽

2018 ◽

Vol 140 (3) ◽

Cited By ~ 3

Author(s):

Pengfei Xing ◽

Guobin Li ◽

Ting Liu ◽

Hongtao Gao ◽

Guoyou Wang

Keyword(s):

Steady State ◽

Correlation Analysis ◽

Cross Correlation ◽

Detection Method ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Mean Square ◽

Vibration Signals ◽

Cross Correlation Analysis ◽

Harmonic Wavelet

Running-in wear experiments were conducted on a spherical-on-disk tester. The vibration signals collected in the experiments were detected by a combination of harmonic wavelet packet transform (HWPT) and cross-correlation analysis (CCA) methods. Experimental results show that the friction vibration signals detected in tangential and normal directions have the characteristics of no time delay and strong correlation. Their root-mean-square (RMS) values gradually reduce and enter a steady-state of fluctuation with the experiments time, which are consistent with the variation of friction coefficient and reflect the change of wear states from the running-in wear to the stable wear. Therefore, the detection of friction vibration can be realized by a combination of HWPT and CCA methods.

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A Novel Feature Extraction Method Using Ant Colony Clustering Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.37-38.32 ◽

2010 ◽

Vol 37-38 ◽

pp. 32-35

Author(s):

De Bin Zhao ◽

Ji Hong Yan

Keyword(s):

Feature Extraction ◽

Clustering Analysis ◽

Extraction Method ◽

Ant Colony Algorithm ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Ant Colony ◽

Vibration Signals ◽

Feature Extraction Method ◽

Ant Colony Clustering

A novel feature extraction method is presented by combining wavelet packet transform with ant colony clustering analysis in this paper. Vibration signals acquired from equipments are decomposed by wavelet packet transform, after which frequency bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed in frequency-domain for extracting intrinsic features reflecting operating condition of machinery. Furthermore, the robust ant colony clustering algorithm is proposed by adjusting comparing probability dynamically. Finally, effectiveness and feasibility of the proposed method are verified by vibration signals acquired from a rotor test bed.

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Detection of unbalance in a wind turbine by using wavelet packet transform and vibration signals

2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) ◽

10.1109/ropec.2017.8261580 ◽

2017 ◽

Author(s):

Salvador Z. Hernandez-Michel ◽

Uriel Hernandez-Osornio ◽

Juan P. Amezquita-Sanchez ◽

Martin Valtierra-Rodriguez ◽

David Granados-Lieberman

Keyword(s):

Wind Turbine ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Vibration Signals

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