352 research outputs found

    Novel technology based on the spectral kurtosis and wavelet transform for rolling bearing diagnosis

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    A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet

    Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution

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    Detecting periodic impulse signal (PIS) is the core of bearing fault diagnosis. Earlier fault detected, earlier maintenance actions can be implemented. On the other hand, remaining useful life (RUL) prediction provides important information when the maintenance should be conducted. However, good degradation features are the prerequisite for effective RUL prediction. Therefore, this paper mainly concerns earlier fault detection and degradation feature extraction for bearing. Maximum correlated kurtosis deconvolution (MCKD) can enhance PIS produced by bearing fault. Whereas, it only achieve good effect when bearing has severe fault. On the contrary, PIS produced by bearing weak fault is always masked by heavy noise and cannot be enhanced by MCKD. In order to resolve this problem, a revised empirical mode decomposition (EMD) algorithm is used to denoise bearing fault signal before MCKD processing. In revised EMD algorithm, a new recovering algorithm is used to resolve mode mixing problem existed in traditional EMD and it is superior to ensemble EMD. For degradation analysis, correlated kurtosis (CK) value is used as degradation indicator to reflect health condition of bearing. Except of theory analysis, simulated bearing fault data, injected bearing fault data, real bearing fault data and bearing degradation data are used to verify the proposed method. Simulated bearing fault data is used to explain the existed problems. Then, injected bearing fault data and real bearing fault data are used to demonstrate the effectiveness of proposed method for fault diagnosis. Finally, bearing degradation data is used to verify the degradation feature CK extracted based on proposed method. All these case studies show the effectiveness of proposed fault diagnosis and degradation tracking method

    A proposal of a technique for correlating defect dimensions to vibration amplitude in bearing monitoring

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    The capability of early stage detection of a defect is gaining more and more importance because it can help the maintenance process, the cost reduction and the reliability of the systems. The increment of vibration amplitude is a well-known method for evaluating the damage of a component, but it is sometimes difficult to understand the exact level of damage. In other words, the amplitude of vibration cannot be directly connected to the dimension of the defect. In the present paper, based on a non-Hertzian contact algorithm, the spectrum of the pressure distribution in the contact surface between the race and the rolling element is evaluated. Such spectrum is then compared with the acquired spectrum of a vibration response of a defected bearing. The bearing vibration pattern was previously analyzed with monitoring techniques to extract all the damage information. The correlation between the spectrum of the pressure distribution in the defected contact surface and the analyzed spectrum of the damaged bearing highlights a strict relationship. By using that analysis, a precise correlation between defect aspect and dimension and vibration level can be addressed to estimate the level of damaging

    ์ง„๋™ ์ƒ์„ฑ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ณ ๋ คํ•œ ์ดˆ๊ธฐ ๊ฒฐํ•จ ๋‹จ๊ณ„์˜ ๋ฒ ์–ด๋ง ์ง„๋‹จ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2023. 2. ์œค๋ณ‘๋™.The failure of rolling element bearings is a common fault in rotating machines. These failures can have catastrophic consequences, including fatal injuries and significant financial losses. To mitigate these risks, researchers have explored various ways to detect and prevent bearing failures as early as possible. One promising approach is the use of condition monitoring data; in this approach, vibration data has been found to be particularly effective for identifying and preventing potential failures. However, the use of vibration signals to diagnose bearings at the incipient fault stage is a challenging task, in part due to the gap between the controlled conditions under which research data is often generated and the actual field conditions in which these bearings operate. In particular, fault-related signals are weak and nonstationary; further, they are usually obscured by noise that arises from environmental factors. Additionally, these signals may be complicated or modulated, making them difficult to discern. To properly address these research issues, this dissertation aims at advancing two research thrusts focused on developing techniques for modeling and analyzing vibration signals based on physical phenomena. In Research Thrust 1, a quasi-periodic impulse train model with an impact force function is suggested to brtidge the gap between theory and reality. In this research, a pseudo second-order cyclostationary signal is modeled using the quasi-periodic impulse train model. In order to simulate the dynamic response of a system, considering the physical behaviors in bearings, the impact force function that reflects the change in contact stress is used. Finally, the proposed model is validated by performing signal processing on the synthesized signal, including simulation of the proposed model. The result confirm that an appropriate preprocessing process is essential to diagnose bearing failure at the incipient failure stage, further, that finding the frequency band that contains the failure information is essential for performance improvement. In Research Thrust 2, a new feature extraction method is proposed for bearing diagnosis using vibration signals, namely the linear power normalized cepstral coefficients (LPNCC). The proposed approach is designed to enhance the bearing signal, which is buried in noise that arises from environmental effects, and which contains mechanical phenomena. The proposed method consists of two sequentially executed steps: 1) extraction of the LPNCC and 2) demodulation analysis that is performed by examining the squared envelope spectra (SES). Combined, this approach is called LPNCC-SES. The performance of the proposed method is examined by applying it to both simulation data and experimental cases. The results show a high level of accuracy and robustness in the diagnostic capabilities of the method, making it suitable for use in maintenance and diagnostic routines.๊ตฌ๋ฆ„ ๋ฒ ์–ด๋ง์€ ํšŒ์ „ ๊ธฐ๊ณ„ ๋ฐ ์™•๋ณต๋™ ๊ธฐ๊ณ„์˜ ํ•ต์‹ฌ์ ์ธ ์š”์†Œ๋ถ€ํ’ˆ์œผ๋กœ ํšŒ์ „ํ•˜๊ฑฐ๋‚˜ ์ง„๋™ํ•˜๋Š” ๊ตฌ์กฐ๋ฅผ ์ง€์ง€ํ•˜๋ฉฐ ๊ตฌ์„ฑํ’ˆ ๊ฐ„์˜ ํ•˜์ค‘์„ ์ „๋‹ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ตฌ๋ฆ„ ๋ฒ ์–ด๋ง์˜ ๊ณ ์žฅ์€ ์‹œ์Šคํ…œ ์ „์ฒด์˜ ๊ณ ์žฅ์œผ๋กœ ์ด์–ด์ ธ ์น˜๋ช…์ ์ธ ์ธ๋ช… ํ”ผํ•ด๋Š” ๋ฌผ๋ก  ๋ง‰๋Œ€ํ•œ ์žฌ์ •์  ์†์‹ค์„ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์กฐ๊ธฐ์— ๋ฒ ์–ด๋ง์˜ ๊ณ ์žฅ์„ ๊ด€์ธกํ•˜๊ณ  ์ง„๋‹จํ•˜๊ธฐ ์œ„ํ•ด ์ƒํƒœ๊ด€์ธก ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด ์™”์œผ๋ฉฐ ํŠนํžˆ ์ง„๋™์‹ ํ˜ธ๋ฅผ ํ™œ์šฉํ•œ ์ง„๋‹จ์ด ๋„๋ฆฌ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋ฒ ์–ด๋ง ์ดˆ๊ธฐ ๊ฒฐํ•จ์„ ์ง„๋‹จ์„ ํ•˜๋Š”๋ฐ ์žˆ์–ด ์–ด๋ ค์›€์„ ๊ฒช๊ฒŒ ํ•˜๋Š” ์ด์œ ๋กœ ํ™˜๊ฒฝ ์˜ํ–ฅ์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ์žก์Œ์— ๋ฌปํ˜€ ์žˆ๋Š” ์•ฝํ•œ ๊ฒฐํ•จ ์‹ ํ˜ธ ๋ฐ ๋ฒ ์–ด๋ง์˜ ๊ฒฐํ•จ ๊ด€๋ จ ์‹ ํ˜ธ์˜ ๋ณต์žกํ•œ ๋ณ€์กฐ๋ฅผ ๋“ค ์ˆ˜์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฒ ์–ด๋ง ๊ฒฐํ•จ์‹ ํ˜ธ์˜ ์ƒ์„ฑ์›๋ฆฌ์—์— ๊ธฐ๋ฐ˜ํ•œ ์‹ ํ˜ธ ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ฒ ์–ด๋ง ์‹ ํ˜ธ๋Š” ๋ณธ์งˆ์ ์œผ๋กœ ๋น„์ •์ƒ์„ฑ์„ ๋„๋ฉฐ ๋˜ํ•œ ์‹ค์ œ ํ˜„์žฅ์—์„œ ํš๋“ํ•œ ์‹ ํ˜ธ๋Š” ๋ณต์žกํ•˜๊ณ  ๋‹ค์–‘ํ•œ ์†Œ์Šค์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์‹ ํ˜ธ๊ฐ€ ์กฐํ•ฉ๋œ๋‹ค. ์ด๋ก ๊ณผ ํ˜„์‹ค ์‚ฌ์ด์˜ ๊ฒฉ์ฐจ๋ฅผ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด ํ•ด์„์  ์‹ ํ˜ธ ๋ชจ๋ธ์— ํ—ค๋ฅด์ธ  ์ ‘์ด‰ ์ด๋ก ์— ๊ธฐ๋ฐ˜ํ•œ ์ถฉ๊ฒฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜๋œ ๋ฒ ์–ด๋ง ์‹ ํ˜ธ์— ๊ธฐ์–ด์˜ ๊ฒฐ์ •๋ก ์  ์‹ ํ˜ธ, ํšŒ์ „์ถ•์˜ ์‚ฌ์ธํŒŒ ์‹ ํ˜ธ ๋ฐ ๊ฐ€์šฐ์‹œ์•ˆ ๋…ธ์ด์ฆˆ์™€ ํ•ฉ์„ฑ๋œ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ์ „์ฒ˜๋ฆฌ ๋ถ„์„์„ ํ†ตํ•ด ์ œ์•ˆ ๋ชจ๋ธ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ด ํ›„, ๋‹ค์–‘ํ•œ ์žก์Œ ํ™˜๊ฒฝ์—์„œ ์—ฌ๋Ÿฌ ๋ณ€์กฐ๋œ ์Œ์„ฑ ์‹ ํ˜ธ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํŒ๋ณ„ํ•˜๋Š” ์Œ์„ฑ์ธ์‹ ๋ฐฉ๋ฒ•์„ ๊ธฐ๊ณ„์‹œ์Šคํ…œ์— ์ ์šฉํ•œ ๊ณ ์žฅํŠน์ง• ์ถ”์ถœ ๋ฐฉ๋ฒ•์„ ์ƒˆ๋กœ์ด ์ œ์•ˆํ•˜์—ฌ ์บก์ŠคํŠธ๋Ÿฝ์— ๊ธฐ๋ฐ˜ํ•œ ํŠน์ง•์ธ์ž๋ฅผ ์ถ”์ถœํ•˜์˜€๋‹ค. ์ถ”์ถœ๋œ ์ธ์ž๋กœ๋ถ€ํ„ฐ ์‹œ๊ฐ„-์ฃผํŒŒ์ˆ˜ ์˜์—ญ์—์„œ ์ŠคํŽ™ํŠธ๋Ÿผ์„ ๊ณ„์‚ฐํ•˜์—ฌ ํšจ๊ณผ์ ์œผ๋กœ ๋ฒ ์–ด๋ง์˜ ํŠน์„ฑ ์ฃผํŒŒ์ˆ˜๋ฅผ ๊ฒ€์ถœํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์˜ ๊ฒ€์ฆ์„ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์žก์Œ ํ™˜๊ฒฝ์—์„œ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ์ดํ„ฐ์™€ ์‹คํ—˜๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ฐ€์†์ˆ˜๋ช…์‹œํ—˜์„ ํ†ตํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•˜์—ฌ ์กฐ๊ธฐ์ง„๋‹จ์˜ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 5 Chapter 2. Technical Background and Literature Review 6 2.1 Vibration Signals of Bearing Faults 6 2.1.1 Rolling Element Bearings 6 2.1.2 Failure of Rolling Element Bearings 7 2.1.3 Bearing Fault Signature and Its Frequencies 8 2.2 Vibration Techniques for Bearing Incipient Fault Diagnosis 10 2.2.1 Overview of Vibration Techniques for Bearings 10 2.2.2 Cepstrum-Based Fault Diagnosis Techniques 13 Chapter 3. Quasi Periodic Impulse Train Model with Impact Force Function 20 3.1 Vibration Modelling of Bearing Fault 21 3.1.1 General Mathematical Model 21 3.1.2 Quasi-periodic Model with Cyclostationary 22 3.1.3 Excitation Force Function in Dynamic Models 23 3.2 Quasi Period Impulse Model with Impact Function 26 3.2.1 Overall Process of Proposed Model 26 3.2.2 Modeling the Excitation Force 27 3.3 Numerical Results and Discussion 32 3.3.1 Necessity of Choosing an Appropriate Preprocessing Method 34 Chapter 4. Speech Recognition-Inspired Feature Engineering for Bearing Fault Diagnosis 48 4.1 Review of Power-Normalized Cepstral Coefficients (PNCC) 49 4.1.1 Basic Definition of Cepstrum 49 4.1.2 Characteristics of cepstrum in mechanical vibrations 50 4.1.3 Power-Normalized Cepstral Coefficients (PNCC) 52 4.2 Proposed Feature Extraction Method: Linear Power-Normalized Cepstral Coefficients (LPNCC) 55 4.3 Fault Diagnosis by Implementing LPNCC 57 4.3.1 Fault Diagnosis Method using LPNCC and Squared Envelope Spectrum (LPNCC-SES) 57 4.3.2 Effect of Linear Filter and Power-normalization 59 4.4 Experimental Application and Results 60 4.4.1 Case Study with Simulation Model 61 4.4.1.1. Simulation Data with White Gaussian Noise 61 4.4.1.2. Denoising Under Gaussian Noise 62 4.4.1.3. Reseults Under Non-gaussian Noise 66 4.4.2 Case Study with Experiment Data 67 4.4.2.1. Experimental Data: Case Western Reserve University Dataset 67 4.4.2.1.1. Compared Methods 67 4.4.2.1.2. Case 1: Impusive Noise 68 4.4.2.1.3. Case 2: Low Signal-to-noise Ratio (SNR) 69 4.4.2.1.4. Case 3: Multiple Defective Signals 71 4.4.2.2. Experimental Data: Naturally Degradation Data 72 Chapter 5. Conclusions 108 5.1 Summary of Dissertation 108 5.2 Contributions and Significance 110 5.3 Suggestions for Future Research 113 References 116 Abstract (Korean) 130๋ฐ•

    Fast Computation of the Autogram for the Detection of Transient Faults

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    Structures and machines maintenance is a hot topic, as their failure can be both expensive and dangerous. Condition-based maintenance regimes are ever more desired so that cost-effective, reliable, and damage-responsive diagnostics techniques are needed. Among the others, Vibration Monitoring using accelerometers is a very little invasive technique that can in principle detect also small, incipient damages. Focusing on transient faults, one reliable processing to highlight their presence is the Envelope analysis of the vibration signal filtered in a band of interest. The challenge of selecting an appropriate band for the demodulation is an optimization problem requiring two ingredients: a utility function to evaluate the performance in a particular band, and a scheme to move within the search space of all the possible center frequencies and band sizes (the dyad {f, ฮ”f}) toward the optimal. These problems were effectively tackled by the Kurtogram, a brute-force computation of the kurtosis of the envelope of the filtered signal (the utility function) of every possible {f, ฮ”f} combination. The complete exploration of the whole plane (f, ฮ”f) is a heavy task which compromises the computational efficiency of the algorithm so that the analysis on a discrete (f, ฮ”f) paving was implemented (Fast Kurtogram). To overcome the lack of robustness to non-Gaussian noise, different utility functions were proposed. One is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the Autogram. To spread this improved algorithm in on-line industrial applications, a fast implementation of the Autogram is proposed in this pape

    Diagnosis of Bearing Fault Using Morphological Features Extraction and Entropy Deconvolution Method

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    It is observed that the bearing failure of rotating machinery is a pulse in the vibration signal, but it is mostly immersed in noise. In order to effectively eliminate this noise and detect pulses, a novel an image fusion technology based on morphological operators inference is proposed. The correctness of morphological operators lies in the correct selection of structural elements (SE). This report presents an effective algorithm for SE selection based on kurtosis, which makes the analysis free empirical method. When analyzing three different groups of faults, the results show that this method effectively and robustly generates impulse. It enables the algorithm to detect early faults too. Recently, minimum entropy deconvolution (MED) was introduced to the machine in the field of condition monitoring, to enhance the detection of rolling bearing and gear failures. MED analysis helps to extract these pulses and diagnose their source, namely defects bearing components. In this research, MED will be reviewed and reintroduced, Application in fault detection and diagnosis of rolling bearings. MED parameters are selected and its combination with pre-whitening. Test cases are presented to illustrate benefits of MED technology. The simulation has been done on MATLAB and a graphical user interface has been created for analysis of bearing and detection of bearing faults using morphological features

    Rolling element bearings localized fault diagnosis using signal differencing and median filtration

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    With the increase complexity of bearingsโ€™ processing algorithms and the growing trend of using computationally demanding algorithms, it is advantageous to provide analysts with a simple to use and implement algorithm. In this spirit, this paper combines simple functions to provide machine condition analysts with the capacity to diagnose bearing faults without all the complexity and jargon that comes with existing methods. The paper proposes a simplified surveillance and diagnostic algorithm for diagnosing localized faults in rolling element bearings using measured raw vibration signals. The proposed algorithm is based on analyzing the frequency content obtained from applying a median filter on the squared derivative signal (first or higher derivatives) of the vibration signal. The combination of signal differencing and median filters provides a squared envelope signal, which can be used directly to diagnose faults. Signal differencing gives a measure of jerk forces and lifts the high frequency content of the signal. To select the optimum order of differentiation, Kurtosis and maximum correlated kurtosis (MCK) are proposed. Median filter usage represents a better alternative of normal low pass filtration. This completely suppresses impulses with large magnitudes, which may interfere with the diagnosis. The length of the median filter (odd number 3, 5, 7 etc.) is selected as such to include the first 10 harmonics of the defect frequency. Simulated signals are used to demonstrate the efficiency of the proposed algorithm and give insights into the choices of the differentiation and smoothening orders. The proposed processing algorithm gives a first measure (surveillance) for detecting localized faults in rolling element bearings in a very simple way and can be employed in online learning and diagnosis systems. Results obtained from applying the algorithm on complex vibration signals from two types of gearboxes are compared with a well-established semi-automated technique with good correspondence
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