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.

A Signal Processing Technique for Detecting Local Defects in a Gear from the Signal Average of the Vibration

1985 ◽  
Vol 199 (4) ◽  
pp. 287-292 ◽  
Author(s):  
P D McFadden ◽  
J D Smith
Keyword(s):  
Signal Processing ◽  
Fatigue Crack ◽  
Early Detection ◽  
Phase Modulation ◽  
Fatigue Cracks ◽  
Digital Signal ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Signal Average ◽  
Local Defects

An approximate digital signal processing technique is presented which can assist in the early detection of local defects such as fatigue cracks in gears by enhancing the changes which these defects produce in the signal average of the vibration of the gear. The technique is demonstrated by the identification of an early fatigue crack in a helicopter gear. The importance of phase modulation in the detection of defects is indicated.

scholarly journals Global crack detection using bispectral analysis

2006 ◽  
Vol 462 (2069) ◽  
pp. 1515-1530 ◽  
Author(s):  
Andrew J Hillis ◽  
Simon A Neild ◽  
Bruce W Drinkwater ◽  
Paul D Wilcox
Keyword(s):  
Crack Detection ◽  
Fatigue Cracks ◽  
Fatigue Cracking ◽  
Processing Technique ◽  
Test Structure ◽  
Strong Mixing ◽  
Steel Beams ◽  
Ultrasonic Frequency ◽  
Destructive Testing ◽  
Signal Processing Technique

This paper describes a global non-destructive testing technique for detecting fatigue cracking in engineering components. The technique measures the mixing of two ultrasonic sinusoidal waves which are excited by a small piezoceramic disc bonded to the test structure. This input signal excites very high-order modes of vibration of the test structure within the ultrasonic frequency range. The response of the structure is measured by a second piezoceramic disc and the received waveform is analysed using the bispectrum signal processing technique. Frequency mixing occurs as a result of nonlinearities within the test structure and fatigue cracking is shown to produce a strong mixing effect. The bispectrum is shown to be particularly suitable for this application due to its known insensitivity to noise. Experimental results on steel beams are used to show that fatigue cracks, corresponding to a reduction in the beam section of 8%, can be detected. It is also shown that the bispectrum can be used to quantify the extent of the cracking. A simple nonlinear spring model is used to interpret the results and demonstrate the robustness of the bispectrum for this application.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

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.

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.

Experimental Study of Cyclic Synchronous Vibration of an Integrally Geared Centrifugal Compressor for Air Separation Application

2020 ◽  
Author(s):  
Zhusan Luo ◽  
Carl L. Schwarz ◽  
Fabio Martins ◽  
Anthony Rosati
Keyword(s):  
Experimental Study ◽  
Centrifugal Compressor ◽  
Processing Technique ◽  
Air Separation ◽  
Signal Processing Technique ◽  
Frequency Spectra ◽  
Vibration Data ◽  
Morton Effect ◽  
Operation Test ◽  
Effective Remedy

Abstract This paper presents an experimental study of high cyclic synchronous vibration observed on the top pinion of an integrally geared centrifugal compressor. This cyclic synchronous vibration was different from the previously reported Morton effect. In a typical cycle, the vibration began with a long quiet period, then took off and followed by settle-down. Sometimes, vibration peaked above a shutdown limit, which subsequently tripped the compressor and then the air separation plant. Frequency spectra showed this new cyclic vibration was dominated by synchronous vibration. To obtain reliable and meaningful phase information for the diagnosis, a new signal processing technique was developed to analyze the historic vibration data captured without a key phasor. An experimental study of this new rotordynamic phenomenon was conducted on the machine in operation. Test data showed the high cyclic synchronous vibration was closely related to Morton effect though it does not have a significant phase shift. An effective remedy measure was therefore taken, and the cyclic synchronous vibration was eliminated. Since then, this compressor has been running smoothly for 17 months. A possible mechanism of the cyclic vibration is discussed in this paper.

scholarly journals Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1062 ◽  
Author(s):  
Venkata ◽  
Rao
Keyword(s):  
Support Vector Machine ◽  
High Pressures ◽  
Control Valve ◽  
Processing Technique ◽  
Support Vector ◽  
Flow Control Valve ◽  
Control Loop ◽  
Signal Processing Technique ◽  
Flow Process ◽  
Vibration Data

A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%.

Numerical Investigation of Nonlinear Lamb Wave Time Reversing for Fatigue Crack Detection

2019 ◽  
Author(s):  
Junzhen Wang ◽  
Yanfeng Shen
Keyword(s):  
Finite Element ◽  
Fatigue Crack ◽  
Time Reversal ◽  
Lamb Wave ◽  
Crack Detection ◽  
Guided Waves ◽  
Fatigue Cracks ◽  
Contact Dynamics ◽  
Virtual Time ◽  
Fatigue Crack Detection

Abstract This paper presents a numerical study on nonlinear Lamb wave time reversing for fatigue crack detection. An analytical framework is initially presented, modeling Lamb wave generation, propagation, wave crack linear and nonlinear interaction, and reception. Subsequently, a 3D transient dynamic coupled-field finite element model is constructed to simulate the pitch-catch procedure in an aluminum plate using the commercial finite element software (ANSYS). The excitation frequency is carefully selected, where only single Lamb wave mode will be generated by the Piezoelectric Wafer Active Sensor (PWAS). The fatigue cracks are modelled nucleating from both sides of a rivet hole. In addition, contact dynamics are considered to capture the nonlinear interactions between guided waves and the fatigue cracks, which would induce Contact Acoustic Nonlinearity (CAN) into the guided waves. Then the conventional and virtual time reversal methods are realized by finite element simulation. Advanced signal processing techniques are used to extract the distinctive nonlinear features. Via the Fast Fourier Transform (FFT) and time-frequency spectral analysis, nonlinear superharmonic components are observed. The reconstructed signals attained from the conventional and virtual time reversal methods are compared and analyzed. Finally, various Damage Indices (DIs), based on the difference between the reconstructed signal and the excitation waveform as well as the amplitude ratio between the superharmonic and the fundamental frequency components are adopted to evaluate the fatigue crack severity. The DIs could provide quantitative diagnostic information for fatigue crack detection. This paper finishes with summary, concluding remarks, and suggestions for future work.

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.

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