Improving condition indicators for helicopter health and usage monitoring systems

Purpose – The purpose of this paper is to suggest new method for improving the condition indicators (CIs) used in health and usage monitoring system based on signal separation of gears. Design/methodology/approach – The research method is based on employing signal separation techniques to improve gears signal and fault signature. The signal separation is based on adaptive filters concept. Findings – CIs estimated for the deterministic part of vibration signal show higher sensitivity to gears faults in comparison to indicators estimated based on the original signal. This method proposed could enhance early fault detection in gears, particularly for those applications where strong background noise from other sources in the machine masks the characteristics fault components. Originality/value – The contribution of this research is improving the CIs currently used for helicopter gearboxes. As consequence the safe operation and availability will be improved. </jats:sec

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

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

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

There is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. This can be achieved through improving diagnosis and prognosis of bearing faults to better determine bearing remaining useful life (RUL). Until now there has been limited research into the prognosis of bearing life in rotating machines. Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new prognosis techniques. Different approaches make use of the advantages of each technique while overcoming the disadvantages towards the development of intelligent systems to determine the RUL of bearings. The review shows that while there are numerous approaches to diagnosis and prognosis, they are suitable for certain cases or are domain specific and cannot be generalised

A hybrid prognostic methodology for tidal turbine gearboxes

Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data

Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Helicopter gearboxes significantly differ from other transmission types and exhibit unique behaviors that reduce the effectiveness of traditional fault diagnostics methods. In addition, due to lack of redundancy, helicopter transmission failure can lead to catastrophic accidents. Bearing faults in helicopter gearboxes are difficult to discriminate due to the low signal to noise ratio (SNR) in the presence of gear vibration. In addition, the vibration response from the planet gear bearings must be transmitted via a time-varying path through the ring gear to externally mounted accelerometers, which cause yet further bearing vibration signal suppression. This research programme has resulted in the successful proof of concept of a broadband wireless transmission sensor that incorporates power scavenging whilst operating within a helicopter gearbox. In addition, this paper investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using vibration and Acoustic Emissions (AE). It compares their effectiveness for various operating conditions. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were combined for this investigation. In addition, this research discusses the feasibility of using AE for helicopter gearbox monitoring

A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emission (AE) to this field is still in its infancy. For planetary-type gearboxes it is more challenging to diagnose bearing faults due to the dynamically changing transmission paths which contribute to masking the vibration signature of interest. The present study is aimed to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. This has been achieved through developing of internal AE sensor for helicopter transmission system. In addition, series of signal processing procedure has been developed to improved detection of incipient damage. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were applied to AE and vibration data acquired from a simplified planetary gearbox test rig with a seeded bearing defect. The results show that AE identified the defect earlier than vibration analysis irrespective of the tortuous transmission pat