Acoustic Emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the Remaining Useful Life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning systems in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; Support Vector Machine Regression (SVMR), Multilayer Artificial Neural Network (ANN) model and Gaussian Process Regression (GPR), to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as Signal Intensity Estimator (SIE) and Root Mean Square (RMS) was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.
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.
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.
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.
Bearings are typically used in wind turbines to support shafts and gears that increase rotational speed from a low speed rotor to a higher speed electrical generator. For various bearing applications, condition monitoring using vibration measurements has remained a subject of intense study to the present day since several decades. Various signal processing techniques are used to analyse vibration signals and extract features related to defects. Statistical indicators such as Crest Factor (CF) and Kurtosis (KU) were reported as very sensitive indicators when the presence of the defects is pronounced, whilst their values may come down to the level of undamaged components when the damage is well advanced. Further, these indicators were applied to an acquired data from proposed diagnostic models, test rigs, and instrumentations that were specifically used for particular research tests, and thus, it is essential to undertake further investigations and anal- Wind power generation is more economical than nuclear and thermal power generation, and it is rapidly growing as a source of clean energy. Natural wind is available free of charge, so the main costs for wind power generation are construction and maintenance. To keep wind turbines function at optimal levels to efficiently harness and convert wind energy to electricity, condition monitoring of wind turbine components (eg, bearings) is important because any wear, if not caught in time, may lead to breakage of other adjacent parts. Vibration is the most regularly measured condition parameter in rotating machinery, and it is continuously monitored in many important applications. The commonly monitored vibration signals are displacement, velocity, and acceleration, and the most basic vibration monitoring technique is to measure the overall vibration level over a broad band of frequencies. Researches on the use of vibration measurements for various applications shafts, bearings, gearboxes, compressors, motors, turbines (gas and steam), and pumps are dated to before the turn of the 21 st century. Vibration waveforms can be analysed using the time and frequency domains. In addition to well-established patents, an enormous amount of work has been performed on developing machine diagnostics and studying failure processes. Many researchers have reported successful results for detecting damaged machine components through spectral analysis. 2Taylor could establish a method for measuring the size of the defects on the raceways and formulate the sequence of appearing and
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.