Investigation of fault modelling in the identification of bearing wear severity

2021 ◽  
pp. 1-19
Author(s):  
Diogo Alves ◽  
Tiago H Machado ◽  
Felipe Tuckmantel ◽  
Patrick S Keogh ◽  
Katia Lucchesi Cavalca
Keyword(s):  
Fault Diagnosis ◽  
Critical Parameters ◽  
Generation Process ◽  
Substantial Impact ◽  
Time Costs ◽  
Rotating Systems ◽  
Fault Modelling ◽  
Processing Techniques ◽  
Vibration Signatures ◽  
Maintenance Process

Abstract Recent research into machines involved in the power generation process has demanded deep investigation of model-based techniques for fault diagnosis and identification. The improvement of critical fault characterisation is crucial in the maintenance process effectiveness, hence in time/costs saving, increasing performance and productivity of the whole system. Consequently, this paper deals with a common fault in hydrodynamically lubricated bearings assembled in rotating systems, namely, that of abrasive wear. Research on this topic points to an interesting query about the significance of model detail and complexity and the identification of its characteristic parameters for the important stages of fault diagnosis and fault identification. For this purpose, two models are presented and analysed in their completeness concerning the fault signature by vibration measurements, as well as the identification of fault critical parameters which determine the machine lifetime estimation, maintenance procedures and time costs regarding performance and productivity. From this study, the detailing in fault modelling has a substantial impact on fault parameter identification, even if its improvement is not so expressive in fault diagnosis procedures involving standard signal processing techniques of vibration signatures.

Artificial Intelligence Based Gas Turbine Compressor Wash: A Predictive Approach

2019 ◽  
Author(s):  
Shaiju M. Raghavan ◽  
Arun Palatel ◽  
Jayaraj Simon
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Critical Parameters ◽  
Deep Penetration ◽  
Substantial Impact ◽  
Future Performance ◽  
Gas Turbine Compressor

Abstract Deep penetration of renewable energy generation has led to increased periods of operation of industrial gas turbines under part load conditions. The performance fault diagnosis of gas turbine module components such as compressor, turbine, and the combustion chamber is a difficult task for such non-design operating conditions. Hence operational data-based performance health monitoring system is a requirement of gas turbine owners or users. The system must be capable to identify the degradation of gas turbine components, having substantial impact on the performance of the module in any possible operating condition. On time identification of degraded components will reduce the cost of operation, ensure service availability and obtain maximum performance from the turbine. This paper illustrates the application of advanced machine learning techniques to the performance analysis, fault diagnosis and prediction of future performance of gas turbine compressor. Compressor fouling is a primary cause of gas turbine performance deterioration, which accounts for 70% to 85% of the performance loss. The first section of the paper focuses on the residual generation of critical parameters of the compressor. The residual of the critical parameters can be calculated by comparing compressor model output with actual plant parameters. The trained artificial neural network (ANN) classifier uses residual of critical parameters to identify the fast rate of compressor fouling in the early stage. Statistical analysis can estimate the future performance of the compressor from the residual of critical parameters. The predicted values of compressor performance are useful for the planning of offline compressor wash, which in turn improve performance, reliability, and availability of gas turbine module. The residuals can also measure the effectiveness of compressor wash and assess the performance after machine overhauling.

Generating Near-THz Surface Acoustic Waves Using Picosecond Ultrasonics

2009 ◽  
Author(s):  
J. H. Lee ◽  
J. S. Sadhu ◽  
S. Sinha
Keyword(s):  
Rise Time ◽  
Acoustic Waves ◽  
High Frequency ◽  
Short Pulse ◽  
Surface Acoustic Waves ◽  
Grating Period ◽  
Critical Parameters ◽  
Generation Process ◽  
Short Pulse Laser ◽  
Substrate Properties

We present here a technique to generate high frequency SAW in non-piezoelectric substrate with nanostructure grating of period less than 100 nm fabricated on it. A short pulse laser (with rise time less than 100fs) incident on this structure creates a periodic thermal stress due to the differential absorption in the substrate and the grating. We show that this stress sets up a surface acoustic wave on the substrate that can be detected optically. Modeling the generation process and analysis of SAW spectrum reveals the critical parameters to be controlled for obtaining SAW of high frequency. We show that the grating period less than 50 nm, a laser pulse of rise time less than 100fs and substrate properties like high optical absorption and high Rayleigh velocity are necessary for generating surface acoustic waves in near-THz range. This work provides quantitative guidelines on the design of near THz phononics.

A Brief Review on Fault Diagnosis of Rotating Machineries

2014 ◽  
Vol 541-542 ◽  
pp. 635-640 ◽  
Author(s):  
S.P. Mogal ◽  
D.I. Lalwani
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Response Measurement ◽  
Rotor Systems ◽  
Vibration Data ◽  
Systems Model ◽  
Processing Techniques ◽  
Signal Processing Techniques

Vibration in any rotating machines is due to faults like misalignment, unbalance, crack, mechanical looseness etc. Identification of these faults in rotor systems, model and vibration signal based methods are used. Signal processing techniques such as Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD) and Wavelet Transform (WT) are applied to vibration data for faults identification. The intent of the paper is to present a review and summarize the recent research and developments performed in condition monitoring of rotor system with the purpose of rotor faults detection. In present paper discuss the different signal processing techniques applied for fault diagnosis. Vibration response measurement has given information concerning any fault within a rotating machine and many of the methods utilizing this technique are reviewed. A detail review of the subject of fault diagnosis in rotating machinery is presented.

Research on Rolling Bearing Fault Monitoring System Based on LabVIEW

2014 ◽  
Vol 602-605 ◽  
pp. 2370-2374
Author(s):  
Li Juan Shi ◽  
Chun Zhi Zhang ◽  
Jin Ying Chen ◽  
Zhao Kun Li
Keyword(s):  
Fault Diagnosis ◽  
Monitoring System ◽  
Signal Analysis ◽  
Development Process ◽  
Rolling Bearing ◽  
Online Data ◽  
Bearing Fault ◽  
Fault Monitoring ◽  
Machine Interface ◽  
Processing Techniques

This paper mainly introduces monitoring principle, montitoring methods and development process of hardware and software of rolling bearing fault monitoring system based on LabVIEW. The system makes full use of computer technology, data acquisition, signal analysis and processing techniques, and fault diagnosis theory to improve monitoring and fault diagnosis of rolling bearing. The system also integrates the functions of traditional detecting instrument and provides a friendly man-machine interface which can display online data.

scholarly journals Laser and Material Parameter Dependence of the Chemical Modifications in the UV Laser Processing of Model Polymeric Solids

2002 ◽  
Vol 20 (1) ◽  
pp. 1-21 ◽  
Author(s):  
E. Andreou ◽  
A. Athanassiou ◽  
D. Fragouli ◽  
D. Anglos ◽  
S. Georgiou
Keyword(s):  
Laser Processing ◽  
Laser Pulses ◽  
Model Systems ◽  
Critical Parameters ◽  
Major Type ◽  
Uv Laser ◽  
Chemical Modifications ◽  
Good Surface ◽  
Molecular Substrates ◽  
Processing Techniques

Chemical modifications are expected to be the major type of side-effect in the UV laser processing of molecular substrates. For their systematic characterization, studies on polymeric systems consisting of poly(methyl methacrylate) and polystyrene films doped with aromatic dopants exemplifying different degrees of photoreactivity are undertaken. In particular, the dependence of the nature and extent of the modifications on chromophore properties and laser parameters (laser fluence, wavelength, and number of pulses) is examined. The substrate absorptivity and the number of employed laser pulses turn out to be the critical parameters in determining the quantity and nature of photoproducts that remain in the substrate. The implications of these results for the optimisation of laser processing of molecular/organic solids are discussed. It is suggested that the importance of employing relatively strongly absorbed wavelengths in laser processing may relate, besides the efficient etching and good surface morphology, to the minimization of the chemical modifications. In contrast, irradiation with successive laser pulses is indicated to be highly disadvantageous for the chemical integrity of the substrate. In all, the study of such model systems appears to be most appropriate for establishing criteria for the systematic optimisation of laser processing techniques of molecular substrates.

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