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.

Pattern recognition approach for acoustic emission burst detection in a gearbox under different operating conditions

2019 ◽  
Vol 25 (17) ◽  
pp. 2295-2304
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Ralph Baltes ◽  
Elisabeth Clausen
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Rotational Speed ◽  
Operating Conditions ◽  
Burst Detection ◽  
Threshold Method ◽  
Planetary Gearbox ◽  
Industrial Sectors ◽  
Artificial Neural Network Ann

Diverse machines in the mining, energy, and other industrial sectors are subject to variable operating conditions (OCs) such as rotational speed and load. Therefore, the condition monitoring techniques must be adapted to face this scenario. Within these techniques, the acoustic emission (AE) technology has been successfully used as a technique for condition monitoring of components such as gears and bearings. 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 study works under variable rotational speed and load, threshold-based methods could produce inadequate results due to the influence of these OCs on the AE. This paper presents a novel burst detection method based on pattern recognition using an artificial neural network (ANN) for classification. The results of the method were compared to an adaptive threshold method. Experimental data were measured in a planetary gearbox test rig under different OCs. The results showed that both methods perform similarly when signals measured under constant OCs are considered. However, when signals are measured under different OCs, the ANN method performs better. Thus, the comparative analysis showed the good potential of the approach to improve an AE analysis of variable speed and/or load machines.

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.

Locating Faulty Planet Gears Using External Vibration Measurements

2019 ◽  
Vol 9 (1) ◽  
pp. 14-17
Author(s):  
D Peng ◽  
W A Smith ◽  
R B Randall
Keyword(s):  
Vibration Signal ◽  
Processing Technique ◽  
Operating Conditions ◽  
Internal Vibration ◽  
Signal Processing Technique ◽  
Vibration Measurements ◽  
External Vibration ◽  
Gear Mesh ◽  
Mesh Phasing ◽  
Planet Carrier

In this study, a mesh phasing-based approach is developed to locate the positions of faulty planet gears using external vibration measurements. Previous studies have illustrated how this can be achieved using internal vibration measurements recorded from a sensor placed on the planet carrier. It was shown in these studies that the timing of identifiable fault symptoms in the vibration signal relative to the phase of the gear-mesh component depends on which of the planet gears carries a fault. A signal processing technique is then developed to locate the position of a spalled gear using internal vibration measurements. However, internally mounted sensors are not commonly used in planetary gearboxes and it is much more convenient to mount sensors externally, for example on the gearbox casing. Therefore, this study extends the concept of using mesh phasing relationships to locate faulty planet gears, this time using external vibration measurements. The updated procedure is validated using experimental data collected from a test-rig running under a range of operating conditions. The results show that the updated procedure is able to identify the locations of faulty planet gears so long as an absolute phase reference (for example from a tachometer) of the planet carrier is available.

Diagnostics of Fatigue Crack in the Shaft Using Spectral Kurtosis

2019 ◽  
Author(s):  
Sagi Rathna Prasad ◽  
A. S. Sekhar
Keyword(s):  
Fatigue Crack ◽  
Crack Detection ◽  
Fatigue Cracks ◽  
Processing Technique ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Laser Vibrometer ◽  
Signal Processing Technique ◽  
Spectral Kurtosis ◽  
Vibration Data

Abstract Rotating machinery components like shafts subjected to continuous fluctuating loads are prone to fatigue cracks. Fatigue cracks are severe threat to the integrity of rotating machinery. Therefore it is indispensable for early diagnostics of fatigue cracks in shaft to avoid catastrophic failures. From the literature, it is evident that the spectral kurtosis (SK) and fast kurtogram were used to detect the faults in bearings and gears. The present study illustrates the use of SK and fast kurtogram for early fatigue crack detection in the shaft using vibration data. To perform this study, experiments are conducted on a rotor test rig which is designed and developed according to the function specification proposed by ASTM E468-11 standard. Fatigue crack is developed, on three shaft specimens, each seeded with the same circumferential V-Notch configuration, by continuous application of stochastic loads on the shaft using electrodynamic shaker in addition to the unbalance forces that arise in normal operating conditions. Vibration data is acquired from various locations of the rotor, using different sensors like miniature accelerometers, laser vibrometer and wireless telemetry strain gauge, till the shaft specimen develops fatigue crack. The analysis results show that the combination of SK and fast kurtogram is an effective signal processing technique for detecting the fatigue crack in the shaft.

Feature Extraction Based on Teager-Kaiser Energy Operation and Envelope Spectra for Fault Detection of a Reciprocating Compressor

2020 ◽  
Author(s):  
Chin-Che Hou ◽  
Min-Chun Pan
Keyword(s):  
Fault Detection ◽  
Energy Operator ◽  
Vibration Signal ◽  
Experiment Design ◽  
Processing Technique ◽  
Operating Conditions ◽  
Reciprocating Compressor ◽  
Signal Processing Technique ◽  
Two Phase ◽  
Synthetic Signal

Abstract In this paper, signal analysis techniques based on Teager-Kaiser energy operation and envelope spectra for fault detection of the discharge valve of a reciprocating compressor is proposed. The method can accurately identify the existing fault of vibration signal features that it simulated by the synthetic signals. A two-phase study was designed to explore the signals simulation and the experimental validation. Signals simulation, which is based on the operation of a reciprocating compressor, and experiment design, which uses three conditions. The first stage is to simulate the operation of the reciprocating compressor, which is to simulate a synthetic signal for the cycle and impact. The synthetic signal is composed of a noise, square wave, and pulse wave. In this study, the synthetic signal is signal-processed by the Teager-Kaiser energy operator and the envelope spectrum that they can effectively extract feature signal and the noise almost is eliminated. The second stage is applied to the signal processing technique proposed in the first stage. Experimental verification of experiment design by the different operating conditions of reciprocating compressor valves. Through the above analysis technology, it is proved that the synthetic signal can be eliminated the background noise to obtain the feature signal. The feasibility of the proposed approach is verified by simulation results, the experiment is to validate with the measurement signals from a six-cylinder reciprocating compressor under different valve conditions. Simulations and experimental results support the proposed technology positively.

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.

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.

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.

scholarly journals A Remote Sensor System Based on TDLAS Technique for Ammonia Leakage Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2448
Author(s):  
Hongbin Lu ◽  
Chuantao Zheng ◽  
Lei Zhang ◽  
Zhiwei Liu ◽  
Fang Song ◽  
...  
Keyword(s):  
Signal Processing ◽  
Limit Of Detection ◽  
Processing Technique ◽  
Tunable Diode Laser ◽  
Sensor System ◽  
Signal Processing Technique ◽  
Suppression Effect ◽  
Output Wavelength ◽  
Deviation Analysis ◽  
Remote Sensor

The development of an efficient, portable, real-time, and high-precision ammonia (NH3) remote sensor system is of great significance for environmental protection and citizens’ health. We developed a NH3 remote sensor system based on tunable diode laser absorption spectroscopy (TDLAS) technique to measure the NH3 leakage. In order to eliminate the interference of water vapor on NH3 detection, the wavelength-locked wavelength modulation spectroscopy technique was adopted to stabilize the output wavelength of the laser at 6612.7 cm−1, which significantly increased the sampling frequency of the sensor system. To solve the problem in that the light intensity received by the detector keeps changing, the 2f/1f signal processing technique was adopted. The practical application results proved that the 2f/1f signal processing technique had a satisfactory suppression effect on the signal fluctuation caused by distance changing. Using Allan deviation analysis, we determined the stability and limit of detection (LoD). The system could reach a LoD of 16.6 ppm·m at an average time of 2.8 s, and a LoD of 0.5 ppm·m at an optimum averaging time of 778.4 s. Finally, the measurement result of simulated ammonia leakage verified that the ammonia remote sensor system could meet the need for ammonia leakage detection in the industrial production process.

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