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Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failure can be catastrophic and can result in costly downtime. Bearing condition monitoring has thus played an important role in machine maintenance. In condition monitoring, the observed signal at a measurement point is often corrupted by extraneous noise during the transmission process. It is important to detect incipient faults in advance before catastrophic failure occurs. In condition monitoring, the early detection of incipient bearing signal is often made difficult due to its corruption by background vibration (noise). Numerous advanced signal processing techniques have been developed to detect defective bearing signals but with varying degree of success because they require a high Signal to Noise Ratio (SNR), and the fault components need to be larger than the background noise. Vibration analyses in the time and frequency domains are commonly used to detect machinery failure, but these methods require a relatively high SNR. Hence, it is essential to minimize the noise component in the observed signal before post processing is conducted. In this research, detection of failure in rolling element bearing faults by vibration analysis is investigated. The expected time intervals between the impacts of faulty bearing components signals are analysed using the blind deconvolution technique as a feature extraction technique to recover the source signal. Blind deconvolution refers to the process of learning the inverse of an unknown channel and applying it to the observed signal to recover the source signal of a damaged bearing. The estimation time period between the impacts is improved by using the technique and consequently provides a better approach to identify a damaged bearing. The procedure to obtain the optimum inverse equalizer filter is addressed to provide the filter parameters for the blind deconvolution process. The efficiency and robustness of the proposed algorithm is assessed initially using different kinds of corrupting noises. The result show that the proposed algorithm works well with simulated corrupting periodic noises. This research also shows that blind deconvolution behaves as a notch filter to remove the noise components. This research involves the application of blind deconvolution technique with optimum equalizer design for improving the SNR for the detection of damaged rolling element bearings. The filter length of the blind equalizer needs to be adjusted continuously due to different operating conditions, size and structure of the machines. To determine the optimum filter length a simulation test was conducted with a pre-recorded bearing signal (source) and corrupted with varying magnitude noise. From the output, the modified Crest Factor (CF) and Arithmetic Mean (AM) of the recovered signal can be plotted versus the filter length. The optimum filter length can be selected by observation when the plot converges close to the pre-determined source feature value. The filter length is selected based on the CF and AM plots, and these values are stored in a data training set for optimum determination of filter length using neural network. A pre-trained neural network is designed to train the behaviour of the system to target the optimum filter length. The performance of the blind deconvolution technique was assessed based on kurtosis values. The capability of blind deconvolution with optimum filter length developed from the simulation studies was further applied in a life bearing test rig. In this research, life time testing is also conducted to gauge the performance of the blind deconvolution technique in detecting a growing potential failure of a new bearing which is eventually run to failure. Results from unseeded new bearing tests are different, because seeded defects have certain defect characteristic frequencies which can be used to track a specific damaged frequency component. In this test, the test bearing was set to operate continuously until failures occurred. The proposed technique was then applied to monitor the condition of the test bearing and a trend of the bearing life was established. The results revealed the superiority of the technique in identifying the periodic components of the bearing before final break-down of the test bearing. The results show that the proposed technique with optimum filter length does improve the SNR of the deconvolved signal and can be used for automatic feature extraction and fault classification. This technique has potential for use in machine diagnostics.

No description available.

Rolling element bearings are the one of the most widely used machine elements in the industry. The most important criterion in bearing selection is the endurance life. The first attempts on the prediction of the endurance life of rolling elements bearings are done by Lundberg and Palmgren in 1950s (Harris, 1999). Their work adopted as an ANSI, ABMA and ISO standard which is widely used in industry today. The basic assumption of Lundberg-Palmgren formulation is that no matter how small the load applied on rolling element bearing, all material in the stressed volume is subject to fatigue failure. In this study, four main life theories / Weibull, Lundberg-Palmgren, Ioannides-Harris, and Zaretsky on rolling element bearings have been investigated. Three-dimensional finite element models of a bearing&rsquo / s inner ring and rolling element have been prepared. The stress fields within the inner ring and the ball with respect to the applied load are obtained numerically. The fatigue life of the inner ring has been predicted by two methods that are widely used for fatigue analysis / Total Life Analysis (S-N method) and Crack Initiation Analysis (&amp / #56256 / &amp / #56624 / -N method). Obtained results are compared with ISO formulation. As a result of the investigation, S-N and &amp / #56256 / &amp / #56624 / -N methods are determined to give more conservative results than ISO method for higher loads that cause stresses above the fatigue limit of the material. The used methods for bearing life prediction recognize the existence of the fatigue limit stress. Hence as the stresses within an operating bearing do not exceed the limit stress, the bearing can achieve infinite life. It is also observed that load variation has a direct influence on the bearing life. When the load significantly changes from the levels which create stress above the fatigue limit to the levels that result stress is below the fatigue limit, the bearing would have higher endurance life than predicted by ISO method.

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.

The main purpose of this diploma thesis is to design turbocharger rotor using rolling element bearings. The diploma thesis is compiled from two specialized search parts dealing with turbocharger rotor and rolling element bearing computation, respectively. The application of the particular rotor using the multi body system Adams – View is described in the following chapter. In the last chapter, one can find the comparison between rotor using bearing with steal and hybrid ceramic rolling element. Also, there is compariosn between rolling element bearing and journal bearing. In the maximum rotor speed, the decrease of more than 50 % in power loss, due to usage of rolling element bearing, resulted from this comparison.

Symbolic dynamics is a current interest in the area of anomaly detection, especially in mechanical systems.  Symbolic dynamics reduces the overall dimensionality of system responses while maintaining a high level of robustness to noise.  Rolling element bearings are particularly common mechanical components where anomaly detection is of high importance.  Harsh operating conditions and manufacturing imperfections increase vibration innately reducing component life and increasing downtime and costly repairs.  This thesis presents a novel way to detect bearing vibrational anomalies through Symbolic Aggregate Approximation (SAX) in the two-dimensional time-frequency domain.  SAX reduces computational requirements by partitioning high-dimensional sensor data into discrete states.  This analysis specifically suits bearing vibration data in the time-frequency domain, as the distribution of data does not greatly change between normal and faulty conditions. Under ground truth synthetically-generated experiments, two-dimensional SAX in conjunction with Markov model feature extraction is successful in detecting anomalies (> 99%) using short time spans (< 0.1 seconds) of data in the time-frequency domain with low false alarms (< 8%).  Analysis of real-world datasets validates the performance over the commonly used one-dimensional symbolic analysis by detecting 100% of experimental anomalous vibration with 0 false alarms in all fault types using less than 1 second of data for the basis of 'normality'. Two-dimensional SAX also demonstrates the ability to detect anomalies in predicative monitoring environments earlier than previous methods, even in low Signal-to-Noise ratios. / Master of Science

<p>Lubrication and friction of modern rolling element bearings were investigated to develop a physics-based bearing friction model. A test rig was designed and developed to measure the frictional torque of radially loaded rolling element bearings with oil bath lubrication. Deep groove ball bearings and radial needle roller bearings were studied at various loads, speeds and lubrication conditions. Experimental results indicate that bearing friction models currently used in industry can be inaccurate, especially when predicting bearing fluid drag losses. A separate test rig was designed and developed to investigate the lubrication and friction of rolling element bearing cage pockets, as new cage pocket designs could improve bearing efficiency. Cage pocket oil starvation was observed for certain operating conditions, and the starvation was found to correlate strongly with cage pocket friction. In order to better understand friction and lubrication characteristics of bearings, computational fluid dynamics (CFD) models were developed to compare with the experimental results. Fluid motion inside the rolling element bearings was investigated using CFD to determine fluid drag torque of bearing components. Fluid drag torque obtained from CFD and experimental measurements are in good agreement. Results from the CFD models also included pressure distributions over bearing surfaces and fluid velocity near rolling elements, but were limited to global length scales. At the micro-scale, rolling element bearing lubrication and friction is dictated by elastohydrodynamic lubrication (EHL). The radial needle roller bearings and deep groove ball bearings used in this investigation are characterized by line and elliptical contacts, respectively. EHL modeling was therefore developed for line contacts with a strongly coupled fluid solid interaction (FSI) solver. Solid bodies were modeled with finite element (FE) software to incorporate inhomogeneities such as inclusions and surface features which affect EHL pressure, film thickness and friction. Results were used to investigate lubricant film thickness at lubricated line contacts under various operating conditions. This work was further extended to model EHL circular contacts with an FSI approach, combining CFD and FE software. The newly developed FSI EHL model provided critical insights regarding fluid behavior in and around EHL point contacts and fluid properties within the lubricant film. Given the modeling results at the micro and macro scale within the rolling element bearings, a better understanding of bearing friction and lubrication is developed, and supported by experimental data.</p>

Today's industry uses increasingly complex machines, some with extremely demanding performance criteria. Failed machines can lead to economic loss and safety problems due to unexpected production stoppages. Fault diagnosis in the condition monitoring of these machines is crucial for increasing machinery availability and reliability. Fault diagnosis of machinery is often a difficult and daunting task. To be truly effective, the process needs to be automated to reduce the reliance on manual data interpretation. It is the aim of this research to automate this process using data from machinery vibrations. This thesis focuses on the design, development, and application of an automatic diagnosis procedure for rolling element bearing faults. Rolling element bearings are representative elements in most industrial rotating machinery. Besides, these elements can also be tested economically in the laboratory using relatively simple test rigs. Novel modern signal processing methods were applied to vibration signals collected from rolling element tests to destruction. These included three advanced timefrequency signal processing techniques, best basis Discrete Wavelet Packet Analysis (DWPA), Matching Pursuit (MP), and Basis Pursuit (BP). This research presents the first application of the Basis Pursuit to successfully diagnosing rolling element faults. Meanwhile, Best basis DWPA and Matching Pursuit were also benchmarked with the Basis Pursuit, and further extended using some novel ideas particularly on the extraction of defect related features. The DWPA was researched in two aspects: i) selecting a suitable wavelet, and ii) choosing a best basis. To choose the most appropriate wavelet function and decomposition tree of best basis in bearing fault diagnostics, several different wavelets and decomposition trees for best basis determination were applied and comparisons made. The Matching Pursuit and Basis Pursuit techniques were effected by choosing a powerful wavelet packet dictionary. These algorithms were also studied in their ability to extract precise features as well as their speed in achieving a result. The advantage and disadvantage of these techniques for feature extraction of bearing faults were further evaluated. An additional contribution of this thesis is the automation of fault diagnosis by using Artificial Neural Networks (ANNs). Most of work presented in the current literature has been concerned with the use of a standard pre-processing technique - the spectrum. This research employed additional pre-processing techniques such as the spectrogram and DWPA based Kurtosis, as well as the MP and BP features that were subsequently incorporated into ANN classifiers. Discrete Wavelet Packets and Spectra, were derived to extract features by calculating RMS (root mean square), Crest Factor, Variance, Skewness, Kurtosis, and Matched Filter. Certain spikes in Matching Pursuit analysis and Basis Pursuit analysis were also used as features. These various alternative methods of pre-processing for feature extraction were tested, and evaluated with the criteria of the classification performance of Neural Networks. Numerous experimental tests were conducted to simulate the real world environment. The data were obtained from a variety of bearings with a series of fault severities. The mechanism of bearing fault development was analysed and further modelled to evaluate the performance of this research methodology. The results of the researched methodology are presented, discussed, and evaluated in the results and discussion chapter of this thesis. The Basis Pursuit technique proved to be effective in diagnostic tasks. The applied Neural Network classifiers were designed as multi layer Feed Forward Neural Networks. Using these Neural Networks, automatic diagnosis methods based on spectrum analysis, DWPA, Matching Pursuit, and Basis Pursuit proved to be effective in diagnosing different conditions such as normal bearings, bearings with inner race and outer race faults, and rolling element faults, with high accuracy. Future research topics are proposed in the final chapter of the thesis to provide perspectives and suggestions for advancing research into fault diagnosis and condition monitoring.