scholarly journals Health Indicators Construction for Damage Level Assessment in Bearing Diagnostics: A Proposal of an Energetic Approach Based on Envelope Analysis

2020 ◽  
Vol 10 (22) ◽  
pp. 8131
Author(s):  
Eugenio Brusa ◽  
Fabio Bruzzone ◽  
Cristiana Delprete ◽  
Luigi Gianpio Di Maggio ◽  
Carlo Rosso
Keyword(s):  
Contact Pressure ◽  
Dynamic Equilibrium ◽  
Health Indicators ◽  
Vibration Signal ◽  
Diagnostic Features ◽  
Envelope Analysis ◽  
Damage Level ◽  
Spectral Kurtosis ◽  
Vibration Data ◽  
Reading Key

Predictive maintenance strategies are established in the industrial context on account of their benefits in terms of costs abatement and machine failures reduction. Among the available techniques, vibration-based condition monitoring (VBCM) has notably been applied in many bearing fault detection problems. The health indicators construction is a central issue for VBCM, since these features provide the necessary information to assess the current machine condition. However, the relation between vibration data and its sources intimately related to bearing damage is not effortlessly definable from a diagnostic perspective. This study discloses a diagnostic investigation performed both on the vibration signal and on the contact pressure signal that is supposed to be one of main forcing terms in the dynamic equilibrium of the damaged bearing. Envelope analysis and spectral kurtosis (SK) are applied to extract and compare diagnostic features from both signals, referring to the Case Western Reserve University (CWRU) case-study. Namely, health indicators are constructed by means of physical considerations based on the effect of faults on the signal power contents. These indicators show to be promising not only for damage detection but, also, for damage severity assessment. Moreover, they provide an invaluable reading key of the link occurring between the contact pressure path and the vibration response.

scholarly journals Vibration analysis based on the spectrum kurtosis for adjustment and monitoring of ball bearing radial clearance

2018 ◽  
Vol 211 ◽  
pp. 06006 ◽  
Author(s):  
Anthimos Georgiadis ◽  
Xiaoyun Gong ◽  
Nicolas Meier
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Radial Clearance ◽  
Spectral Kurtosis ◽  
Vibration Data ◽  
Bearing Clearance ◽  
Bearing Condition Monitoring ◽  
Bearing Vibration ◽  
Vibration Signal Analysis

Vibration signal analysis is a common tool to detect bearing condition. Effective methods of vibration signal analysis should extract useful information for bearing condition monitoring and fault diagnosis. Spectral kurtosis (SK) represents one valuable tool for these purposes. The aim of this paper is to study the relationship between bearing clearance and bearing vibration frequencies based on SK method. It also reveals the effect of the bearing clearance on the bearing vibration characteristic frequencies This enables adjustment of bearing clearance in situ, which could significantly affect the performance of the bearings. Furthermore, the application of the proposed method using SK on the measured data offers useful information for predicting bearing clearance change. Bearing vibration data recorded at various clearance settings on a floating and a fixed bearing mounted on a shaft are the basis of this study

Failure Analysis of a High Pressure Descaling Pump Using Envelope Analysis

2011 ◽  
Author(s):  
Yimin Shao ◽  
Wennian Yu ◽  
Qing Chen ◽  
Huifang Xiao ◽  
Xiangzhi Yu
Keyword(s):  
High Pressure ◽  
Vibration Signal ◽  
The Other ◽  
Maintenance Cost ◽  
Pump System ◽  
Envelope Analysis ◽  
Vibration Data ◽  
Alignment Technique ◽  
The Hilbert Transform ◽  
Hot Rolled

A high pressure descaling pump was used to remove scale in a hot rolled band furnace. This pump was the key piece of equipment in this process which maintained the surface quality of the hot rolled band steel. Over a period of three years one pump continued to work normally, but the other pump vibrated vigorously. The motor, pump and the other system equipment were changed repeatedly, but the abnormal vibration was not eliminated. Vibration data from the two pumps was obtained, the modulation phenomenon existed in the vibration signal caused by the gear coupling misalignment was found, thus the envelope analysis based on the Hilbert transform was presented to demodulate the vibration signal. The frequency spectrum of the demodulated signal showed that the second order frequency characteristic was more obvious, which effectively revealed the fault information related with the gear coupling misalignment. It was found that the abnormal vibration was caused by coupling misalignment between the motor and the pump. After applying a more advanced alignment technique to thoroughly eliminate the misalignment in the coupling, the vibration was considerably reduced and the pump could work normally. This convincingly verified our analysis results and would dramatically reduce the ongoing maintenance cost for the descaling pump system.

scholarly journals An Improved Dual-Kurtogram-Based T 2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhiyuan Jiao ◽  
Wei Fan ◽  
Zhenying Xu
Keyword(s):  
Fault Diagnosis ◽  
Control Chart ◽  
Condition Monitoring ◽  
Control Limit ◽  
Normal Operation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Envelope Analysis ◽  
Spectral Kurtosis ◽  
Vibration Data

Condition monitoring and compound fault diagnosis are crucial key points for ensuring the normal operation of rotating machinery. A novel method for condition monitoring and compound fault diagnosis based on the dual-kurtogram algorithm and multivariate statistical process control is established in this study. The core idea of this method is the capability of the dual-kurtogram in extracting subbands. Vibration data under normal conditions are decomposed by the dual-kurtogram into two subbands. Then, the spectral kurtosis (SK) of Subband I and the envelope spectral kurtosis (ESK) of Subband II are formulated to construct a control limit based on kernel density estimation. Similarly, vibration data that need to be monitored are constructed into two subbands by the dual-kurtogram. The SK of Subband I and the ESK of Subband II are calculated to derive T 2 statistics based on the covariance determinant. An alarm will be triggered when the T 2 statistics exceed the control limit and suitable subbands for square envelope analysis are adopted to obtain the characteristic frequency. Simulation and experimental data are used to verify the feasibility of the proposed method. Results confirm that the proposed method can effectively perform condition monitoring and fault diagnosis. Furthermore, comparison studies show that the proposed method outperforms the traditional T 2 control chart, envelope analysis, and empirical mode decomposition.

Rolling element bearing diagnosis using spectral kurtosis based on optimized impulse response wavelet

2019 ◽  
Vol 26 (3-4) ◽  
pp. 175-185 ◽  
Author(s):  
Abbas Rohani Bastami ◽  
Amir Bashari
Keyword(s):  
Impulse Response ◽  
Great Influence ◽  
Rolling Element Bearing ◽  
Envelope Analysis ◽  
Spectral Kurtosis ◽  
Vibration Data ◽  
Resonance Band ◽  
Rolling Element ◽  
Element Bearing ◽  
Short Time

Envelope analysis is widely used in fault diagnosis of rolling element bearings (REBs). In envelope analysis, it is necessary to select a frequency band which is related to the resonance of the bearing. Spectral kurtosis (SK) is known as a powerful method to find the resonance band in vibration of a defective REB. SK, calculated by short time Fourier transform, suffers from its dependency on the window length. In this article, a special wavelet transform is used to obtain a SK diagram. It is shown that choice of mother wavelet function has great influence on the resulting SK diagram. The proposed wavelet is based on the impulse response of a damped single degree of freedom system. An optimization algorithm is used to optimize the SK diagram for fault detection. The method is tested for both simulated and experimental vibration data.

Research of the Vertical Vibrations of Tandem Mill Based on Wavelet Analysis

2015 ◽  
Vol 724 ◽  
pp. 279-282
Author(s):  
Chun Hua Ren ◽  
Xu Ma ◽  
Ze Ming Li ◽  
Yan Hong Ding
Keyword(s):  
Wavelet Analysis ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Vertical Vibration ◽  
Rolling Speed ◽  
Vibration Data ◽  
Signal Features ◽  
Operating Side ◽  
Vertical Vibrations ◽  
Vibration Parameters

In this paper, the defect sheet was captured coincidentally. According to the defective product’s characteristics, we suspected to be caused by the vertical vibration of the roll. When the rolling speed reached a certain value, the vibration of the fourth stand can be feel. The experiment of the vibration data collection was taken to compare the vibration parameters of rolling operating side with those of drive side by wavelet analysis. The result states that the abnormal vibration signal features can be extracted in a special frequency segment of wavelet decomposition, and the vibration frequency to the roll is confirmed which appeared product defects.

FFT Analysis Using LabView as a Part of Vibration and Preventive Maintenance Class for Senior Level MET students

2005 ◽  
Author(s):  
Aniruddha Mitra ◽  
Sahana Sen
Keyword(s):  
Preventive Maintenance ◽  
Laboratory Experiments ◽  
Vibration Signal ◽  
Engineering Technology ◽  
Technology Program ◽  
Student Activities ◽  
Labview Software ◽  
Vibration Data ◽  
Set Up ◽  
Fft Analysis

An existing senior level elective course on vibration in Mechanical Engineering Technology program at Georgia Southern University has been modified significantly. Two major components have been added to this course. Those are theoretical topics on preventive maintenance and laboratory experiments. As a part of laboratory experiments, Fast Fourier Transform (FFT) was introduced as a possible tool for vibration analysis for the purposes of machine diagnosis. Utilizing the current laboratory set up for the data acquisition systems, LabView software has been used for FFT analysis of signals from various sources. Four different modules were developed and implemented. The modules are as follows: random variation in acceleration of a toy cart due to roughness of the track and pulley, regular uniform wave signal which is generated by the lateral vibration of a cantilever beam at its natural frequency, signal generated by the imported raw data from other sources (e.g. MATLAB) and vibration signal of a shaft mounted on ball bearings in order to detect the defects in the bearing. Each of these modules is illustrated in this paper with suitable examples and suggested student activities and involvements. The results from FFT analysis have been cross checked using other methods and observations. As a follow up, students have been taken to a local industry where significant amount of emphasis is given to preventive maintenance of machineries by vibration data analysis using FFT. Future possible projects include the analysis of vibration data gathered from actual machine shop. This project opens the scope for greater collaborative effort between local industries and classroom activities.

scholarly journals Fault Diagnosis of Industrial Gear Hobbing Machine

2020 ◽  
Vol 9 (3) ◽  
pp. 2039-2043
Keyword(s):  
Fault Diagnosis ◽  
Low Cost ◽  
Market Competition ◽  
Vibrational Analysis ◽  
High Quality ◽  
Mems Accelerometer ◽  
Spectral Kurtosis ◽  
Vibration Data ◽  
Gear Hobbing ◽  
Dynamic Time

The aim of this paper is to develop a fault diagnosis algorithm by vibrational analysis for an industrial gear hobbing machine. Gear Hobbing is the most dominant and profitable process for manufacturing high quality gears. In order to sustain the market competition gear manufacturers, need to produce high quality gears with minimum possible cost. However, catastrophic failures do occur in gear hobbing process which causes unexpected machine down time and revenue loss. These failures can be avoided by using condition monitoring approaches. In the proposed approach vibration data during different faults such as lubrication error, excessive feed rate, loose bearing error is collected from an industrial gear hobbing machine using three axis MEMS accelerometer. The collected data is analyzed and classified with spectral kurtosis and Dynamic Time Warping algorithm. The efficiency of the proposed approach is 90 percent as determined by experimental results. The proposed approach can provide a low-cost solution for predictive maintenance for gear hobbing industries..

scholarly journals Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

2018 ◽  
Vol 17 (5) ◽  
pp. 1192-1212 ◽  
Author(s):  
Faris Elasha ◽  
Matthew Greaves ◽  
David Mba
Keyword(s):  
Acoustic Emission ◽  
Signal To Noise Ratio ◽  
Acoustic Emissions ◽  
Research Programme ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Broadband Wireless ◽  
Bearing Faults ◽  
Spectral Kurtosis ◽  
Signal Suppression

Helicopter gearboxes significantly differ from other transmission types and exhibit unique behaviours that reduce the effectiveness of traditional fault diagnostics methods. In addition, due to lack of redundancy, helicopter transmission failure can lead to catastrophic accidents. Bearing faults in helicopter gearboxes are difficult to discriminate due to the low signal-to-noise ratio in the presence of gear vibration. In addition, the vibration response from the planet gear bearings must be transmitted via a time-varying path through the ring gear to externally mounted accelerometers, which cause yet further bearing vibration signal suppression. This research programme has resulted in the successful proof of concept of a broadband wireless transmission sensor that incorporates power scavenging while operating within a helicopter gearbox. In addition, this article investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using vibration and acoustic emissions. It compares their effectiveness for various operating conditions. Three signal processing techniques, including an adaptive filter, spectral kurtosis and envelope analysis, were combined for this investigation. In addition, this research discusses the feasibility of using acoustic emission for helicopter gearbox monitoring.

Detection of Transients in Vibration Signals using Reverse Dispersion Entropy

2021 ◽  
pp. 1-33
Author(s):  
Jaafar Alsalaet
Keyword(s):  
Shannon Entropy ◽  
Vibration Signal ◽  
Fir Filter ◽  
Frequency Resolution ◽  
Frequency Bandwidth ◽  
Vibration Signals ◽  
Spectral Kurtosis ◽  
Ideal Filter ◽  
Envelope Signal ◽  
The Ideal

Abstract In this work, the reverse dispersion entropy (RDE) is used to process the squared envelope signal in order to detect nonstationarites. Based on the idea of spectral kurtosis (SK) and kurtogram, the squared envelope signal is first extracted by applying STFT to vibration signal. Then, as an alternative to negative Shannon entropy, the RDE is used to process the squared envelope to detect the range of frequencies at which the transients occur. The RDEgram color-coded map is used to represent the RDE values as a function of frequency and frequency resolution from which the ideal filter parameters can be inferred. Once, the best frequency and frequency bandwidth pair are found, an optimum FIR filter can be designed to filter the original vibration signal. The proposed method is tested against simulated and actual vibration signals and proved to be superior to existing methods.

scholarly journals Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB

2020 ◽  
Vol 10 (20) ◽  
pp. 7302
Author(s):  
Seokgoo Kim ◽  
Dawn An ◽  
Joo-Ho Choi
Keyword(s):  
Process Simulation ◽  
Rolling Element Bearing ◽  
Fault Identification ◽  
New Developments ◽  
Envelope Analysis ◽  
Practical Applications ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal

This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept and implement by their own effort. The three most common techniques—the autoregressive model, spectral kurtosis, and envelope analysis—are selected to demonstrate the bearing diagnosis process. Simulation signal is introduced to help understand the characteristics of fault signal and carry out the process toward the fault identification. The techniques are then applied to the two real datasets to demonstrate the practical applications, one made by the authors and the other by the Case Western Reserve University, which is known as a standard reference in testing the diagnostic algorithms.

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