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

Investigation of Bearing Fatigue Damage Life Prediction Using Oil Debris Monitoring

Research was performed to determine if a diagnostic tool for detecting fatigue damage of helicopter tapered roller bearings can be used to determine remaining useful life (RUL). The taper roller bearings under study were installed on the tail gearbox (TGB) output shaft of UH- 60M helicopters, removed from the helicopters and subsequently installed in a bearing spall propagation test rig. The diagnostic tool was developed and evaluated experimentally by collecting oil debris data during spall progression tests on four bearings. During each test, data from an on-line, in-line, inductance type oil debris sensor was monitored and recorded for the occurrence of pitting damage. Results from the four bearings tested indicate that measuring the debris generated when a bearing outer race begins to spall can be used to indicate bearing damage progression and remaining bearing life

Development of Prognostics Techniques for Surface Defect Growth in Railroad Bearing Rolling Elements

One of the major causes of failure in railroad bearings used in freight service is rolling contact fatigue (RCF). RCF is due to subsurface inclusions which are a result from impurities in the steel that is used to fabricate the bearings. Once the bearings initiate subsurface fatigue cracks, they will then propagate upward and initiate spalling of the rolling surfaces. These spalls will begin small and continuously propagate with operation as this induces additional crack forming and spalling. Studies have indicated that bearing temperature is not a good indicator of spall initiation. In many cases, the temperature of the bearing increases markedly once the spall has propagated across major portions of the raceway. However, vibration signatures can be used to detect spall initiation and can track spall deterioration. No monitoring system technique can indicate the growth rate of a spall nor can it determine the bearing residual useful life. Therefore, the principle objective of this study is to develop reliable prognostic models for spall growth within railroad bearings that are based on actual service life testing rather than theoretical simulations. The data used to develop the models presented in this study have been acquired from laboratory and field testing that initiated in 2010. The growth models in this study are for spalls that initiated on the bearing inner (cone) and outer (cup) rings. Coupling these prognostic models with a vibration-based bearing condition-monitoring algorithm previously developed, will provide the rail industry with an efficient tool that can be used to propose proactive maintenance schedules that will reduce unnecessary and costly train stoppages and delays and will prevent catastrophic derailments

Prognostics Models for Railroad Tapered Roller Bearings with Spall Defects on Inner or Outer Rings

Rolling contact fatigue (RCF) is one of the major causes of failure in railroad bearings used in freight service. Subsurface inclusions resulting from impurities in the steel used to fabricate the bearings initiate subsurface fatigue cracks, which propagate upwards and cause spalling of the rolling surfaces. These spalls start small and propagate as continued operation induces additional crack formation and spalling. Studies have shown that the bearing temperature is not a good indicator of spall initiation. In many instances, the temperature of the bearing increases markedly only when the spall has spread across major portions of the raceway. In contrast, vibration signatures can be used to accurately detect spall initiation within a bearing and can track spall deterioration. No monitoring technique can indicate the growth rate of a spall or determine residual useful life. Hence, the main objective of this study is to develop reliable prognostic models for spall growth within railroad bearings that are based on actual service life testing rather than theoretical simulations. The data used to devise the models presented here were acquired from laboratory and field testing that started in 2010. Growth models are provided for spalls initiating on the bearing inner (cone) and outer (cup) rings. Coupling these prognostic models with a previously developed vibration-based bearing condition monitoring algorithm will provide the rail industry with an efficient tool that can be used to plan proactive maintenance schedules that will mitigate unnecessary and costly train stoppages and delays and will prevent catastrophic derailments

An integrated bearing prognostics method for remaining useful life prediction

Abstract An integrated bearing prognostics method for remaining useful life prediction Nowadays, in order to improve the productivity and quality, more and more resources are invested in maintenance. In order to improve the reliability of an engineering system, accurate predictions of the remaining useful lifetime of the equipment and its key parts are required. Bearing plays an important role in the rotating machines. The purpose of using a bearing is to reduce rotational friction and support the load imposed on it in radial and axial directions. The common types of bearing defects include damage in rolling elements, inner and outer races, etc. In this thesis, we focus on the spall propagation caused by rolling contact fatigue. The existing bearing prognosis methods are either model-based or data driven. In this thesis, we develop an integrated bearing prognostics method, which utilizes both physical models and condition monitoring data. In the physical model part, a Hertz contact model is used to analyze the stress developed from the contact point between two curved surfaces which are pressed together, the ball and the deep groove. Based on Paris’ law, a damage propagation model is used to describe the spall propagation process. It is difficult to measure a defect size when the machines are running. Therefore, online data is obtained and processed to transform raw signals into useful information. In this thesis, the uncertainty factors are considered, including material uncertainty, model uncertainty and measurement error. A Bayesian method is used to update the distribution of this uncertainty factor by fusing the condition monitoring data, to achieve updated predictions of remaining useful life. Finally, two sets of data are used to verify and validate the proposed integrated bearing prognostics method. The first set of data includes a group of simulated bearing degradation histories. The second set of data were collected from lab experiments conducted using the Bearing Prognostics Simulator. These examples demonstrated the effectiveness of the proposed method. The key contribution of this thesis is the development of an integrated bearing prognostics method, where the uncertain model parameters are updated using the collected condition monitoring data, while the existing bearing prognostics methods are either model-based or data driven. Both the development of the method and the experimental validation are significant contributions to the field of bearing prognostics

IJTC2010-41127 SURFACE DAMAGE UNDER EXTREME CONDITIONS EXISTED IN AIRCRAFT BEARINGS

ABSTRACT The extreme conditions of aircraft bearing steel M50 have been simulated by a two-disk test rig for investigating the surface damage of the ball/raceway contact surfaces. The slide/roll ratio are 0.12 and 0.15, correspondingly, the rolling speed are 43.2m/s and 49.5m/s. Aircraft engine oil 4050 as the supplied oil has been maintained at approximately 80℃ in the tests. The ultimate Hertzian contact stresses of the surface damage obtained from the experiments are 3.8GPa in 0.12 slide/roll ratio and 3.5GPa in 0.15 slide/roll ratio. The damage mode is scuffing in 0.12 slide/roll ratio and it is oxidation, thermal fatigue and scuffing in 0.15 slide/roll ratio. Cracks in the contact areas originate from surface layer in the two slide/roll ratios. INTRODUCTION The bearings used in aircraft engines operate in extreme conditions such as high speed, heavy load and high temperature. The combination of speed, load, and temperature which exist in aircraft bearings will exceed the capability of conventional synthetic lubricants and materials. The contact surfaces of the parts always occur fatigue, scuffing and many other damage modes, these damages would result in severe wear and even catastrophe M50 steel is a main material used in aircraft bearings, its tribological behavior in extreme conditions is related to the reliability and life of bearings. Rolling contact fatigue experiments of M50 steel have shown that the orientation of surface micro-crack is related to the friction direction and asperity-scale micro-cracks as well as micro-spalls may evolve into macroscopic spalling under heavy load and rolling/sliding speed conditions The study simulated the extreme conditions of aircraft bearings, the ultimate parameters and the damage modes o

Endurance and failure characteristics of main-shaft jet engine bearings at 3x10 to the 6th power DN

Groups of thirty 120-mm bore angular contact ball bearings were endurance tested at a speed of 12,000 and 25,000 rpm and a thrust load of 66 721 N. The bearings were manufactured from a single heat of VIM-VAR AISI M-50 steel. At 1.44X1 million and 3.0x1 million DN, 84 483 and 74 800 bearing test hours were accumulated, respectively. Test results were compared with similar bearings made from CVM AISI M-50 steel run under the same conditions. Bearing lives at speeds of 3x1 million DN with the VIM-VAR AISI M-50 steel were nearly equivalent to those obtained at lower speeds. A combined processing and material life factor of 44 was found for VIM-VAR AISI M-50 steel. Continuous running after a spall has occurred at 3.0x1 million DN can result in a destructive fracture of the bearing inner race

Effects of Ultra-Clean and centrifugal filtration on rolling-element bearing life

Fatigue tests were conducted on groups of 65-millimeter bore diameter deep-groove ball bearings in a MIL-L-23699 lubricant under two levels of filtration. In one test series, the oil cleanliness was maintained at an exceptionally high level (better than a class "000" per NAS 1638) with a 3 micron absolute barrier filter. These tests were intended to determine the "upper limit" in bearing life under the strictest possible lubricant cleanliness conditions. In the tests using a centrifugal oil filter, contaminants of the type found in aircraft engine filters were injected into the filters' supply line at 125 milligrams per bearing-hour. "Ultra-clean" lubrication produced bearing fatigue lives that were approximately twice that obtained in previous tests with contaminated oil using 3 micron absolute filtration and approximately three times that obtained with 49 micron filtration. It was also observed that the centrifugal oil filter had approximately the same effectiveness as a 30 micron absolute filter in preventing bearing surface damage

Frequency Domain Feature Extraction and Long Short-Term Memory for Rolling Bearing Fault Diagnosis

Conference paper, accepted versionWith the rapid development of the high-speed rail-ways, the speed of trains is getting faster and faster, and the dynamic load between the wheels and rails of the vehicle increases accordingly. The rolling bearing is a key part of the high-speed train transmission system. The train is subjected to high-frequency vibration for a long time during operation, and the bearing is prone to fatigue damage, which affects the safe operation of the train. Nowadays, many methods have been applied in fault diagnosis like reinforcement learning, convolutional neural networks and autoencoders. One of the typical methods is the reinforcement neural architecture research method. It makes neural network design automatic and eliminates the bottleneck associated with choosing network architectural parameters. However, this method focuses on the time domain signal, and a time domain signal cannot capture the particular properties of a frequency domain signal. In order to solve these problems, we propose a new method containing two Steps: Use FFT to convert the time domain signal to the frequency domain and use Bi-LSTM neural network model to recognize different faults. For each fault, the time series signal has some correlation with some specific frequencies. The frequency domain is more intuitive than the time domain and describes different states of faulty types. For recognition, LSTM is better at classifying sequence data than other methods, and Bi-LSTM can predict the sequence from both directions, achieving higher accuracy. Experiments on public data sets demonstrate the efficiency of the proposed method.10.13039/501100001809-Natural Science Foundation of China (Grant Number: 6196020601

An investigation into improving the spall resistance of polycrystalline diamond compacts

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Engineering, School of Mechanical, Industrial & Aeronautical Engineering, 2017An investigation of polycrystalline diamond compact (PDC) cutter failures, which are industrially known as spalling, was conducted by exploring changes in the diamond layer architecture and edge geometry of the cutter. Layer architecture was investigated through the use of layered functionally graded (FG) structures. Twenty one FG variations were prepared by the tape casting method and sintered using a high-pressure, high-temperature press. The vertical borer test (VBX), a laboratory test method, was used to gauge the improvement in spall resistance of the FG specimens against the benchmarks. Due to cost constraints associated with VBX testing, of the 21 available specimens, only four variations were tested for spalling. Contrary to expectation, it was found that all four specimens spalled during VBX testing despite showing a slight improvement in the spall area. For this reason, this route was abandoned. It was concluded that the use of layered structures is not effective in resolving the spalling problem. The use of novel edge geometry was investigated by taking three standard products and creating new geometric profiles on the specimens using a spark erosion machine. Each profile comprised a depression on the front face of the cutter. The specimens with novel geometry were also tested on the VBX. The spall was found to be confined between the chamfer breach and the depression feature. The depression appeared to have stopped the spall from propagating beyond the allowable spall limit of 1.2mm. On the basis of this 3 finding, it was concluded that spalling was successfully resolved. It is recommended that further optimization of this solution should be explored in field testing. In addition, a cost-effective way to fabricate the geometric profiles on the cutters should be further investigated because creating specimens using the spark erosion machine was quite expensive. Therefore, it is not viable for fabrication of large production volumesCK201