Bearing Signal Separation of Commercial Helicopter Main Gearbox

Gears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques. These techniques have been tested experimentally using vibration data collected from the transmission system of a CS-29 ‘Category A’ helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection

Smart Wing Flutter Suppression

In this work, it has been shown the effect of a piezoelectric material on postponing the flutter phenomenon and even removing it completely on a regular wing. First, the system response of a smart wing with only plunge DOF and pitch DOF are presented. Using an efficient piezopatch can effectively decay the oscillations of the smart wing in a very short time. In addition, implementing one piezopatch in the plunge DOF of a regular wing with three DOF can postpone the flutter speed by 81.41%, which is a considerable increase in the flutter speed. We then present the effect of adding one more piezopatch to a smart wing in the pitch DOF to further postpone the flutter phenomenon. The flutter speed in a smart wing can be postponed by 115.96%, which is a very considerable value. Finally, adding one more piezopatch on a smart wing in the control DOF can completely remove the flutter phenomenon from the wing, which represents a great achievement in the dynamic aeroelectic behavior of a wing

Condition Monitoring Philosophy for Tidal Turbines

Renewable energy is currently considered as the main solution to reduce greenhouse gas emission. This has led to great developments in the use of renewable energy for electricity generation. Among many renewable energy resources, tidal energy has the advantage of being predictable, particularly when compared to wind energy. Currently the UK is the world leader in extracting energy from the tide; an estimation shows a potential of 67 TWh per year. In order to ensure safe operation and prolonged life for tidal turbines, condition monitoring is essential. The technology for power generation using tidal turbines is new therefore the condition monitoring concept for these devices is yet to be established. Also, there is a lack of understanding of techniques suitable for health monitoring of the turbine components and support structure given their unique operating environment.In this paper the condition monitoring of a tidal turbine is investigated. The objective is to highlight the need for condition monitoring and establish procedures to decide the condition monitoring techniques required, in addition to highlighting the impact and benefits of applying condition based maintenance. A model for failure analysis is developed to assess the needs for condition monitoring and identify critical components, after which a ‘symptoms analysis’ was performed to decide the appropriate condition monitoring techniques. Finally, the impact of condition monitoring on system reliability is considered

Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox

A study on helicopter main gearbox planetary bearing fault diagnosis

The condition monitoring of helicopter main gearbox (MGB) is crucial for operation safety, flight airworthiness and maintenance scheduling. Currently, the helicopter health and usage monitoring system, HUMS, is installed on helicopters to monitor the health state of their transmission systems and predict remaining useful life of key helicopter components. However, recent helicopter accidents related to MGB failures indicate that HUMS is not sensitive and accurate enough to diagnose MGB planetary bearing defects. To contribute in improving the diagnostic capability of HUMS, diagnosis of a MGB planetary bearing with seeded defect was investigated in this study. A commercial SA330 MGB was adopted for the seeded defect tests. Two test cases are demonstrated in this paper: the MGB at 16,000 rpm input speed with 180 kW load and at 23,000 rpm input speed with 1760 kW load. Vibration data was recorded, and processed using signal processing techniques including self-adaptive noise cancellation (SANC), kurtogram and envelope analysis. Processing results indicate that the seeded planetary bearing defect was successfully detected in both test cases

Detection of Natural Crack in Wind Turbine Gearbox

This document is the Accepted Manuscript version of the following article: Suliman Shanbr, Faris Elasha, Mohamed Elforjani, and Joao Teixeira, ‘Detection of natural crack in wind turbine gearbox’, Renewable Energy, vol. 118: 172-179, October 2017. Under embargo. Embargo end date: 30 October 2018. The final, published version is available online at doi: https://doi.org/10.1016/j.renene.2017.10.104. © 2017 Elsevier Ltd. This manuscript version is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.One of the most challenging scenarios in bearing diagnosis is the extraction of fault signatures from within other strong components which mask the vibration signal. Usually, the bearing vibration signals are dominated by those of other components such as gears and shafts. A good example of this scenario is the wind turbine gearbox which presents one of the most difficult bearing detection tasks. The non-stationary signal analysis is considered one of the main topics in the field of machinery fault diagnosis. In this paper, a set of signal processing techniques has been studied to investigate their feasibility for bearing fault detection in wind turbine gearbox. These techniques include statistical condition indicators, spectral kurtosis, and envelope analysis. The results of vibration analysis showed the possibility of bearing fault detection in wind turbine high-speed shafts using multiple signal processing techniques. However, among these signal processing techniques, spectral kurtosis followed by envelope analysis provides early fault detection compared to the other techniques employed. In addition, outer race bearing fault indicator provides clear indication of the crack severity and progress.Peer reviewe

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig

Helicopter gearbox bearing fault detection using separation techniques and envelope analysis

The main gearbox (MGB) is a crucial part of a helicopter. MGB bearings suffer intensively from stress and friction during flights hence concerns for their health condition and detecting potential defects become critical for the sake of operation safety and system reliability. In this study, bearing defects were seeded in the second epicyclic stage bearing of a commercial Class A helicopter MGB. Vibration and tachometer signals were recorded simultaneously for the purpose of fault diagnosis. The tests were carried out at different power and speed conditions for various seeded bearing defects. This paper presents a comparison of signal processing techniques employed to identify the presence of the defects masked by strong background noise generated from an operation helicopter MGB

Helicopter Main Gearbox Bearing Defect Identification with Acoustic Emission Techniques

Helicopter transmission integrity is critical to the safety operation. Among all mechanical failures in helicopter transmission, the main gearbox (MGB) failures occupy approximately 16%. Great effort has been paid in early prevention and diagnosis of MGB failures. As a commonly mployed monitoring technology, vibration analysis suffers from strong background noise due to variable transmission paths from the bearing to the receiving externally mounted vibration sensor. The background noise can mask the signal signature of interest. This paper reports on an investigation to identify the presence of a bearing defect in a CS29 Category ‘A’ helicopter main gearbox with acoustic emission (AE) technologies. This investigation involved performing the tests for faultfree condition, minor bearing damage and major bearing damage conditions under different power levels. The bearing faults were seeded on one of the planet gears of the second epicyclic stage. To overcome the issue of low signal to noise ratio (SNR), AE sensor was directly attached on the dish of planet carrier. The AE signal was transferred wireless to avoid complex wiring inside MGB. The analysis results proved the feasibility of using AE ensor as in-situ bearing defect identification