Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition

This document is the Accepted Manuscript version. The final, definitive version of this paper has been published in Structural Health Monitoring, April 2017, DOI: https://doi.org/10.1177/1475921717704620, published by SAGE Publishing, All rights reserved.The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.Peer reviewedFinal Accepted Versio

Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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 techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, 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 and root mean square 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.Peer reviewe

Condition Monitoring of Slow Speed Rotating Machinery Using Acoustic Emission Technology

Slow speed rotating machines are the mainstay of several industrial applications worldwide. They can be found in paper and steel mills, rotating biological contractors, wind turbines etc. Operational experience of such machinery has not only revealed the early design problems but has also presented opportunities for further significant improvements in the technology and economics of the machines. Slow speed rotating machinery maintenance, mostly related to bearings, shafts and gearbox problems, represents the cause of extended outages. Rotating machinery components such as gearboxes, shafts and bearings degrade slowly with operating time. Such a slow degradation process can be identified if a robust on-line monitoring and predictive maintenance technology is used to detect impending problems and allow repairs to be scheduled. To keep machines functioning at optimal levels, failure detection of such vital components is important as any mechanical degradation or wear, if is not impeded in time, will often progress to more serious damage affecting the operational performance of the machine. This requires far more costly repairs than simply replacing a part. Over the last few years there have been many developments in the use of Acoustic Emission (AE) technology and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on slow speed rotating machinery. Unlike conventional technologies such as thermography, oil analysis, strain measurements and vibration, AE has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This programme of research involves laboratory tests for monitoring slow speed rotating machinery components (shafts and bearings) using AE technology. To implement this objective, two test rigs have been designed to assess the capability of AE as an effective tool for detection of incipient defects within low speed machine components (e.g. shafts and bearings). The focus of the experimental work will be on the initiation and growth of natural defects. Further, this research work investigates the source characterizations of AE signals associated with such bearings whilst in operation. It is also hoped that at the end of this research program, a reliable on-line monitoring scheme used for slow speed rotating machinery components can be developed

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

Experimental Monitoring of Eccentric Gears with different Mechanical Conditions

The use of Condition Monitoring (CM) for engineering design, and industrial gears, has dramatically changed the gear design and manufacturing process in a short space of time. As CM deployment grows rapidly, the feasibility of CM for the diagnosis and prognosis of most common gear failure modes has well been investigated and documented. However, this is not the case for the CM of industrial gear eccentricity. Previous published work, in particular, focused only on the use of simulation models as a basis to investigate the gear eccentricity. Simulations cannot always ease the detection of complex problems and complications that are experienced in real operations. For instance, excessive eccentricity can be considered as one of manufacturing errors that may severely lead to direct effect on the overall dynamic performance of gears. Yet, it produces very high modulated mesh frequency rates; thus, making the detection of faulty machine components very difficult or even impossible. With this in mind, this paper presents the first known attempt at the diagnosis and prognosis of experimental vibration datasets from five different eccentric gear conditions. Datasets were first analysed using Signal Intensity Estimator (SIE) method in time and frequency domains. Then, the data was subjected to additional processing for the classification of gearbox status. Observations from the results showed that the proposed techniques could successfully discriminate the “good” and “bad” gears

Detecting natural crack initiation and growth in slow speed shafts with the Acoustic Emission technology

This paper presents results of an experimental investigation to assess the potential of the Acoustic Emission (AE) technology for detecting natural cracks in operational slow speed shafts. A special purpose built test rig was employed for generating natural degradation on a shaft. It was concluded that AE technology successfully detected natural cracks induced on slow speed shafts

Natural mechanical degradation measurements in slow speed bearings

Acoustic emission (AE) technology applied to condition monitoring is gaining acceptance as a useful complimentary tool. This paper demonstrates the use of AE measurements to detect, monitor the growth and locate natural defect initiation and propagation in a conventional rolling element bearing. To undertake this task a special purpose test-rig was built to allow for accelerated natural degradation of a bearing race. It is concluded that crack initiation and its subsequent propagation is detectable with AE technology. The paper also investigates the source characterisation of E signals associated with a defective bearing whilst in operation

Assessment of natural crack initiation and its propagation in slow speed bearings

Monitoring of bearings is an essential part of most condition monitoring programmes in rotating machinery. This paper demonstrates the use of acoustic emission (AE) measurements to detect, monitor and locate natural defect initiation and propagation in a thrust rolling element bearing. To undertake this task a special purpose test-rig was built that allowed for accelerated natural degradation of a bearing race. It is concluded that sub-surface initiation and subsequent crack propagation can be detected using a range of time and frequency domain analysis techniques on AE's generated from natural degrading bearings. The paper also investigates the source characterisation of AE signals associated with a defective bearing whilst in operation