Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition

2017 ◽  
Vol 17 (3) ◽  
pp. 532-548 ◽  
Author(s):  
Mohamed Elforjani
Keyword(s):  
Acoustic Emission ◽  
Health Management ◽  
Processing Technique ◽  
Remaining Useful Life ◽  
Slow Speed ◽  
Signal Processing Technique ◽  
Monitoring And Diagnosis ◽  
Diagnosis And Prognosis ◽  
Recent Developments ◽  
Machine Health

The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

Evaluation of acoustic emission burst detection methods in a gearbox under different operating conditions

2018 ◽  
Vol 25 (4) ◽  
pp. 895-906 ◽  
Author(s):  
F. Leaman ◽  
C. Niedringhaus ◽  
S. Hinderer ◽  
K. Nienhaus
Keyword(s):  
Acoustic Emission ◽  
Processing Technique ◽  
Operating Conditions ◽  
Detection Methods ◽  
Threshold Values ◽  
Burst Detection ◽  
Signal Processing Technique ◽  
Planetary Gearbox ◽  
Machine Condition Monitoring ◽  
Definition Of

In account of its abilities to follow the damage progression, also at early stages, the acoustic emission (AE) analysis has become an attractive technique for machine condition monitoring. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under analysis works under variable operating conditions, threshold-based methods could lead to poor results due to the influence of these conditions on the AE generation. The present work compares the ability of three AE burst detection methods in a planetary gearbox working under different rotational speeds and loads. The results showed that performance could be significantly improved by using factors of the root mean square value as threshold values instead of fixed values. Among the evaluated methods, the method that includes demodulation and differentiation as a signal processing technique had the best performance overall.

Acoustic Emission Signal Analysis by Wavelet Method to Investigate Damage Mechanisms During Drilling of Composite Materials

2010 ◽  
Author(s):  
Hossein Heidary ◽  
Amir Refahi Oskouei ◽  
Milad Hajikhani ◽  
Behrooz Moosaloo ◽  
Mehdi Ahmadi Najafabadi
Keyword(s):  
Acoustic Emission ◽  
Cutting Speed ◽  
Damage Mechanism ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Emission Analysis ◽  
Wavelet Method ◽  
Emission Signal ◽  
Composite Damage ◽  
Acoustic Emission Sensing

Structural parts made of composites have frequently to be drilled in the industry. However, little is now about the interacting conditions between the drill tool and material, which may be multi-type and multi-size. Delamination free in drilling different fiber reinforced composites is the main objective of present paper. Therefore the influence of drilling and materials variables thrust force and delamination of GFRP composite was investigated experimentally. Drilling variables are cutting speed and feed; material variable is fiber orientation. Acoustic Emission sensing was employed for online detection of composite damage induced by drilling. This paper addresses an application of wavelet-based signal processing technique on a composite during drilling. The wavelet methodology is introduced and procedure of wavelet-based acoustic emission (AE) analysis methods is demonstrated. Result shows Acoustic Emission analysis by wavelet method can monitor damage mechanism in drilling of composites.

A recursive Bayesian approach to small fatigue crack propagation and detection modeling

2018 ◽  
Vol 232 (6) ◽  
pp. 738-753 ◽  
Author(s):  
Reuel Smith ◽  
Mohammad Modarres ◽  
Enrique López Droguett
Keyword(s):  
Acoustic Emission ◽  
Fatigue Crack ◽  
Measurement Errors ◽  
Crack Detection ◽  
Life Support ◽  
Health Management ◽  
Fatigue Crack Initiation ◽  
Fatigue Cracking ◽  
Remaining Useful Life ◽  
Dog Bone

Engineers have witnessed much advancement in the study of fatigue crack detection and propagation modeling. More recently, the use of certain damage precursors such as acoustic emission signals to assess the integrity of structures has been proposed for application to prognosis and health management of structures. However, due to uncertainties associated with small crack detection of damage precursors and crack size measurement errors of the detection technology used, applications of prognosis and health management assessments have been limited. In this article, a methodology is developed for the purpose of assessment of crack detection and propagation parameters and the minimization of uncertainties including detection and sizing errors associated with a series of known crack detection and propagation models that use acoustic emission as the precursor to fatigue cracking. The methodology is facilitated by the Bayesian inference of a joint-likelihood model which includes sizing and detection models. Examples where several dog-bone Al 7075T6 specimens are tested to produce fatigue crack initiation and propagation data and estimates based on remaining useful life support the effectiveness and usefulness of the proposed methodology.

scholarly journals Prognostic and Health Management of Active Assets in Nuclear Power Plants

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Vivek Agarwal ◽  
Nancy Lybeck ◽  
Binh T. Pham ◽  
Richard Rusaw ◽  
Randall Bickford
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Health Management ◽  
National Laboratory ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Idaho National Laboratory ◽  
Diagnosis And Prognosis ◽  
Prognostic And Health Management

This paper presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and two wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.

scholarly journals Rotordynamic Faults: Recent Advances in Diagnosis and Prognosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ryan Walker ◽  
Sureshkumar Perinpanayagam ◽  
Ian K. Jennions
Keyword(s):  
Condition Monitoring ◽  
Health Management ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Bearing Failure ◽  
Rotating Machines ◽  
Diagnosis And Prognosis ◽  
Recent Developments ◽  
Identification Techniques ◽  
Intense Research

Diagnosis and condition monitoring in rotating machinery has been a subject of intense research for the last century. Recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated vehicle health management (IVHM) systems. With this in mind, this paper concentrates on highlighting some of the latest research on common faults in rotating machines. Eight key faults have been described; the selected faults include unbalance, misalignment, rub/looseness, fluid-induced instability, bearing failure, shaft cracks, blade cracks, and shaft bow. Each of these faults has been detailed with regard to sensors, fault identification techniques, localization, prognosis, and modeling. The intent of the paper is to highlight the latest technologies pioneering the drive towards next-generation IVHM systems for rotating machinery.

Monitoring the Lubrication Condition of Rolling Element Bearings Using the Acoustic Emission Technique

2006 ◽  
Author(s):  
Yibo Edward Fan ◽  
Zhanqun Shi ◽  
Georgina Harris ◽  
Fengshou Gu ◽  
Andrew Ball
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Frequency Domain Analysis ◽  
Processing Technique ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Signal Processing Technique ◽  
Operational Life ◽  
Rolling Element ◽  
Lubrication Condition

Lubrication condition strongly influences the behaviour and operational life of a rolling element bearing. This paper presented an experimental investigation of rolling element bearings with no lubricant and with grease-lubricant containing contaminants using the acoustic emission (AE) technique. High frequency sampling and data streaming technology were applied in the measurement of AE, instead of traditionally measured AE parameters such as the counts, events, and peak amplitude of the signal etc. By processing the AE signals with frequency domain analysis technology, the no lubricant and containing contaminants conditions can be clearly discriminated. This result proved that the frequency domain AE signal processing technique is a suitable method for monitoring the lubrication condition in rolling element bearings.

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