A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. The application of acoustic emission (AE) for bearing diagnosis is gaining ground as a complementary diagnostic tool, however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. Furthermore, the extent of bearing damage has eluded the diagnostician. The experimental investigation reported in this paper was centred on the application of the AE technique for identifying the presence and size of a defect on a radially loaded bearing. An experimental test rig was designed such that defects of varying sizes could be seeded onto the outer race of a test bearing. Comparisons between AE and vibration analysis over a range of speed and load conditions are presented. In addition, the primary source of AE activity from seeded defects is investigated. It is concluded that AE offers earlier fault detection and improved identification capabilities than vibration analysis. Furthermore, the AE technique also provided an indication of the defect size, allowing the user to monitor the rate of degradation on the bearing; unachievable with vibration analysis.     

Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

With a view to detecting incipient failures in large-size low-speed rolling bearings and ensuring minimal effect of subjectivity on the process, a new data-driven multivariate and multiscale statistical monitoring method is proposed. The proposed method which combines the Principal Component Analysis (PCA) multivariate monitoring approach and the Ensemble Empirical Mode Decomposition (EEMD) method, which adaptively decomposes signals into various time scales, was called the EEMD-based multiscale PCA (EEMD–MSPCA). The method is very general in nature, which is why it could also be used in different areas and for various tasks. It can be used for controlling each time scale of decomposition or only the selected ones, for multivariate and multiscale filtering or for monitoring system operation on the basis of reconstructed i.e. filtered signals. The efficiency of the proposed EEMD–MSPCA method for the task of bearing condition monitoring and signal filtering was evaluated on simulated as well as on actual vibration and Acoustic Emission (AE) signals measured on a purpose built test stand. The fact that the proposed method is able to identify the local bearing defect of a very small size indicates that AE and vibration signals carry sufficient information on the bearing condition and that the proposed EEMD–MSPCA method ensures high-reliability bearing fault detection.     

Identification of the acoustic emission source during a comparative study on diagnosis of a spur gearbox

Condition monitoring of gears with vibration analysis is well established whilst the application of acoustic emission (AE) to gear defect diagnosis and monitoring is still in its infancy. This paper details results of an experimental programme to ascertain and validate the applicability of AE to seeded gear defect identification. Furthermore, comparisons are made to vibration diagnosis. As a direct consequence of the experimental programme, the relationship between temperature, oil film thickness and AE activity were investigated. It is shown that similar to the lubricant film thickness between non-conforming surfaces under isothermal conditions, AE activity is not influenced by load. Limitations of applying AE to seeded defect identification are presented and it is concluded that the source of AE activity is attributed to asperity contact.     

A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission,vibration and spectrometric oil analysis for spur gears

Prognosis of gear life using the acoustic emission (AE) technique is relatively new in condition monitoring of rotating machinery. This paper describes an experimental investigation on spur gears in which natural pitting was allowed to occur. Throughout the test period, AE, vibration and spectrometric oil samples were monitored continuously in order to correlate and compare these techniques to natural life degradation of the gears. It was observed that based on the analysis of root mean square (rms) levels only the AE technique was more sensitive in detecting and monitoring pitting than either the vibration or spectrometric oil analysis (SOA) techniques. It is concluded that as AE exhibited a direct relationship with pitting progression, it offers the opportunity for prognosis.     

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signals requires expert prior information and human labour.Recently,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data.Given its robust per-formance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions.This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis.The first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration signals.To begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration Images.Once the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration images.Finally,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture features.In the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions.Two cases of fault classification of rolling element bearings are used to validate the proposed method.Experimental results of this investigation demonstrate that RGBVI-CNN can generate advan-tageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy.Moreover,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy,precision,recall,and F-score.     

The use of acoustic emission information to distinguish between dry and lubricated rolling element bearings in low-speed rotating machines

Bearings are known as the vital parts of machines, and their condition is often critical to the success of an operation or process. Presence of a film of lubricant such as grease between the bearing surfaces minimizes the friction and surface wear. Contaminated grease or lack of lubricant may lead to an ineffective bearing performance or malfunction of the machinery parts. Therefore, in order to avoid unexpected breakdowns, reliable and robust bearing condition monitoring techniques are demanded. According to previous studies, acoustic emission (AE) signals contain valuable information that can be used for bearing condition monitoring and fault detection. The main objective of this study is to evaluate the effectiveness of AE signal parameters to distinguish between lubricated and dry bearings under similar operating conditions. To this end, a low-speed rotating test rig is manufactured and used. Eight levels of rotational speeds and four levels of radial loads were applied to the test rig shaft end, which is connected to the testing bearing. In each test, seven time domain AE parameters were computed. The statistical tools were also used to present the dominant experimental variables on AE signal parameters. According to experimental results, it was found that four AE parameters can be used to distinguish between dry and lubricated bearings.     

Evaluation of tribological properties of bearing materials for marine diesel engines utilizing acoustic emission technique

Acoustic Emission (AE) technique, which has detection capability for minute failures, has been tried to monitor the condition of a plain bearing under the laboratory conditions. In this paper, the bearing materials for marine diesel engines - tin alloy as known as “white metal”, aluminum alloy of 40% tin mass and aluminum alloy 40% tin mass with resin overlay - were tested using a sleeve-to-plate tribo-tester. The frictional force and back temperature were measured as well as the AE signals. The possibility of AE technique to monitor the bearing condition was also assessed by evaluating tribological properties under different operating conditions such as start-stop simulating the crankshaft turning during engine assembly and seizure tests. These results indicate that AE is useful for monitoring the lubricated condition of the sliding surfaces and evaluating tribological properties of the bearing.     

Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method

The article presents a novel non-linear multivariate and multiscale statistical process monitoring and signal denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) non-linear multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) to handle multiscale system dynamics. The proposed method which enables us to cope with complex even severe non-linear systems with a wide dynamic range was named the EEMD-based multiscale KPCA (EEMD-MSKPCA). The method is quite general in nature and could be used in different areas for various tasks even without any really deep understanding of the nature of the system under consideration. Its efficiency was first demonstrated by an illustrative example, after which the applicability for the task of bearing fault detection, diagnosis and signal denosing was tested on simulated as well as actual vibration and acoustic emission (AE) signals measured on purpose-built large-size low-speed bearing test stand. The positive results obtained indicate that the proposed EEMD-MSKPCA method provides a promising tool for tackling non-linear multiscale data which present a convolved picture of many events occupying different regions in the time-frequency plane.     

Isolation and identification of dry bearing faults in induction machine using wavelet transform

Any vibration signal obtained from electromechanical systems contains a level of random changes. These random changes in the measured signal may be due to the random vibrations that can be related to the health of the machine for some faults such as dry bearing fault or bearing ageing. The presence of dry bearing fault, which is caused by the lack of lubricant, increases the level of random vibrations as compared to those obtained in healthy bearing machine. If these random vibrations could be isolated from the measured signal, useful information about bearing health may be obtained. Therefore, in this paper, signals (three line to line voltages, three currents, two vibration signals, four temperatures and one speed signal) obtained from the monitoring system are treated and analyzed using wavelet transform to correlate it to the dry bearing faults in induction machine. In this study, on-line analysis of the acquired signals has been performed using C⁺⁺, while MATLAB has been used to perform the off-line analysis.     

RETRACTED ARTICLE: Development and application of intensified envelope analysis for the condition monitoring system using acoustic emission signal

The application of the high-frequency acoustic-emission (AE) technique in the condition monitoring of rotating machinery has been increasing of late. It has a major drawback, though, the attenuation of the signal, and as such, the AE sensor has to be close to its source. Two signal-processing methods, envelope analysis and wavelet transform, were found to be useful for detecting faults in the rolling element bearing and gearboxes. These methods have a disadvantage, though: their application is focused only on a component of the assembled machine. For example, envelope analysis is a powerful method for detecting faults in the bearing system, but it is not proper for use in the gear system. Thus, these methods could not be used to detect combined faults in the common assembled machines. Therefore, we propose a signal-processing method consisting of envelope analysis and DWT (discrete wavelet transform). In addition, a novel mother function optimized for the AE signal for DWT was extracted through a fatigue crack growth test, and is also proposed herein. Then the proposed method, called intensified envelope analysis (IEA), was used to detect the faults in the rolling element bearing and rotating shaft. According to the results, IEA can be a better signal processing method for the condition monitoring system using AE technique.