Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators: Preprint PDF Download
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Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. The underlying idea behind traditional indicators often revolves around tracking both cyclostationarity and abnormal impulses in the vibration signals without distinguishing the two. A recently proposed method to capture the rolling element bearing degradation lays out the groundwork for new indicator families utilizing generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the NREL Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox, hence the complexity of the vibration signals is similar to a real case. Furthermore, the new indicators are also tested with signals that carry multiple fault signatures. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.
Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. The underlying idea behind traditional indicators often revolves around tracking both cyclostationarity and abnormal impulses in the vibration signals without distinguishing the two. A recently proposed method to capture the rolling element bearing degradation lays out the groundwork for new indicator families utilizing generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the NREL Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox, hence the complexity of the vibration signals is similar to a real case. Furthermore, the new indicators are also tested with signals that carry multiple fault signatures. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.
Author: Qingkai Han Publisher: Springer ISBN: 9811027471 Category : Technology & Engineering Languages : en Pages : 168
Book Description
This book explores the dynamics and vibration properties of gearboxes, with a focus on geared rotor systems. It discusses mechanical theories, finite-element based simulations, experimental measurements and vibration signal processing techniques. It introduces the vibration-resonance calculation method for the geared rotor system in wind turbines and load sharing of the planetary gear train, and offers a method for calculating the vibrations of geared rotor systems under either internal excitations from gear sets or external loads transferred from wind loads. It also defines and elaborates on parameter optimization for planetary gear systems based on the torsional dynamics of wind-turbine geared rotor systems. Moreover, it describes experimental measurements of vibrations on the wind-turbine gearbox performed on the test rig and on site, and analyzes the vibration signals of different testing points, showing them in both time and frequency domains. Lastly, it lists the gear coupling frequencies and fault characteristic frequencies from the vibrations of the gearbox housing. The technologies and results presented are valuable resources for use in dynamic design, vibration prediction and analysis of gearboxes and geared rotor systems in wind turbines as well as many other machines.
Author: Juanjuan SHI Publisher: ISBN: Category : University of Ottawa theses Languages : en Pages :
Book Description
As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below: (1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment. (2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures. (3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause zsmearingy of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially. (4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited. To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows: (1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering, (2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones), (3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and (4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification. With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification. The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.
Author: Xingkai Yang Publisher: ISBN: Category : Fault location (Engineering) Languages : en Pages : 0
Book Description
Gearboxes are key components commonly employed to transfer torque and power and adjust speed in mechatronic systems, such as wind turbines, automobiles, and mining machines. Due to the harsh working environment, various faults may occur in gearboxes. Tooth cracks account for a large proportion of gearbox faults. Detection and severity assessment of early tooth cracks is of vital significance to prevent gearbox failures since it enables efficient condition-based maintenance activities, which not only improves system reliability but also reduces operation and maintenance costs. Vibration analysis has been widely utilized for gear tooth crack detection and severity assessment. In industrial applications, gearboxes may work under either constant or time-varying operating conditions. Besides, gearboxes may suffer from either one single tooth crack or multiple tooth cracks depending on their working environment. All these factors render it challenging to get a good understanding of vibration characteristics of gearboxes with tooth cracks owing to their complexity, which undermines the effectiveness of vibration analysis for tooth crack detection and severity assessment. This thesis aims to procure some insights into vibration characteristics of fixed-axis spur gearboxes with tooth cracks through dynamic simulation, and the obtained insights are further adopted to guide the development of effective vibration signal analysis methods for tooth crack detection and severity assessment. To this end, the overarching objective of this thesis consists of four sub-objectives, which aim to address four issues related to tooth crack detection and severity assessment for fixed-axis spur gearboxes. Firstly, inspired by the observation that the Crack Induced Impulses (CII) contain more information on tooth crack growth, two novel condition indicators are developed by a proposed method which conducts a thorough analysis on the CII and are adopted for early tooth crack severity assessment. Secondly, to effectively track tooth crack severity progression under time-varying operating conditions, a comprehensive study on how time-varying operating conditions affect vibration signals of a fixed-axis spur gearbox with a tooth crack is conducted. A linear dependence of the Amplitude Modulation (AM) of the CII on the time-varying operating conditions is identified, through which a new condition indicator is proposed to track tooth crack severity progression under time-varying operating conditions. In addition, inspired by the finding that the AM of the CII is resulted from operating condition variations, a normalization method is proposed to remove the speed variation-induced AM of the CII and a normalized CII is obtained. The normalized CII preserve information on tooth crack growth and are free from gearbox speed fluctuations, which are used to track tooth crack severity progression under variable speed conditions. Lastly, insights into vibration characteristics of a fixed-axis spur gearbox with multiple tooth cracks are obtained using dynamic simulation and are further experimentally validated. Besides, inspired by the observation that the CII can well reflect tooth cracks, a method focusing on the CII is proposed to detect the number and locations of multiple tooth cracks in fixed-axis spur gearboxes. The research work conducted in this thesis enables us to procure a good understanding of vibration characteristics of fixed-axis spur gearboxes with tooth cracks working under both constant and time-varying operating conditions and provides effective vibration signal analysis methods for tooth crack detection and severity assessment of fixed-axis spur gearboxes. Future work will explore the effects of tooth lubrication and bearing faults on gearbox vibration characteristics.
Author: Stephan Schmidt Publisher: ISBN: Category : Electric fault location Languages : en Pages : 0
Book Description
Effective fault diagnosis techniques are important to ensure that expensive assets such as wind turbines can operate reliably. Vibration condition monitoring data are rich with information pertaining to the dynamics of the rotating machines and are therefore popular for rotating machine diagnostics. However, vibration data do not only contain diagnostic information, but operating condition information as well. The performance of many conventional fault diagnosis techniques is impeded by inherent varying operating conditions encountered in machines such as wind turbines and draglines. Hence, it is not only important to utilise fault diagnosis techniques that are sensitive to faults, but the techniques should also be robust to changes in operating conditions. Much research has been conducted to address the many facets of gearbox fault diagnosis e.g. understanding the interactions of the components, the characteristics of the vibration signals and the development of good vibration analysis techniques. The aforementioned knowledge, as well as the availability of historical data, are regarded as prior knowledge (i.e. information that is available before inferring the condition of the machine) in this thesis. The available prior knowledge can be utilised to ensure that e ective gearbox fault diagnosis techniques are designed. Therefore, methodologies are proposed in this work which can utilise the available prior knowledge to e ectively perform fault diagnosis, i.e. detection, localisation and trending, under varying operating conditions. It is necessary to design di erent methodologies to accommodate the di erent kinds of historical data (e.g. healthy historical data or historical fault data) that can be encountered and the di erent signal analysis techniques that can be used. More speci cally, a methodology is developed to automatically detect localised gear damage under varying operating conditions without any historical data being available. The success of the methodology is attributed to the fact that the interaction between gear teeth in a similar condition results in data being generated which are statistically similar and this prior knowledge may be utilised. Therefore, a dissimilarity measure between the probability density functions of two teeth can be used to detect a gear tooth with localised gear damage. Three methodologies are also developed to utilise the available historical data from a healthy machine for gearbox fault diagnosis. Firstly, discrepancy analysis, a powerful novelty detection technique which has been used for gear diagnostics under varying operating conditions, is extended for bearing diagnostics under varying operating conditions. The suitability of time-frequency analysis techniques and di erent models are compared for discrepancy analysis as well. Secondly, a methodology is developed where the spectral coherence, a powerful second-order cyclostationary technique, is supplemented with healthy historical data for fault detection, localisation and trending. Lastly, a methodology is proposed which utilises narrowband feature extraction methods such as the kurtogram to extract a signal rich with novel information from a vibration signal. This is performed by attenuating the historical information in the signal. Sophisticated signal analysis techniques such as the squared envelope spectrum and the spectral coherence are also used on the novel signal to highlight the bene ts of utilising the novel signal as opposed to raw vibration signal for fault diagnosis. Even though a healthy state is the desired operating condition of rotating machines, fault data will become available during the operational life of the machine. Therefore, a methodology, centred around discrepancy analysis, is developed to utilise the available historical fault data and to accommodate fault data becoming available during the operation of the machine. In this investigation, it is recognised that the machine condition monitoring problem is in fact an open set recognition problem with continuous transitions between the healthy machine condition and the failure conditions. This is explicitly incorporated into the methodology and used to infer the condition of the gearbox in an open set recognition framework. This methodology uses a di erent approach to the conventional supervised machine learning techniques found in the literature. The methodologies are investigated on numerical and experimental datasets generated under varying operating conditions. The results indicate the bene ts of incorporating prior knowledge into the fault diagnosis process: the fault diagnosis techniques can be more robust to varying operating conditions, more sensitive to damage and easier to interpret by a non-expert. In summary, fault diagnosis techniques are more e ective when prior knowledge is utilised.
Author: Shazali Osman Publisher: ISBN: Category : Languages : en Pages :
Book Description
Nowadays, modern rotating machinery industries such as automotive, aerospace, turbo machinery, chemical plants, and power generation stations are rapidly increasing in complexity and in their everyday operations, which demand their systems to operate at higher reliability, extreme safety, and with lower production and maintenance costs. Therefore, accurate fault diagnosis of machine failure is vital to the operation of the related industries. The majority of machine imperfections has been related to gearbox faults (e.g., gears, shafts and bearings), which are subject to damage modes such as fatigue, impacts, and overloading. Faults not detected in time can result in severe damage to machinery, catastrophic injuries, and substantial financial losses. On the other hand, if a fault is detected in its early stages, corrective and preventive action can be taken to avoid any significant machine failure. Vibration monitoring, a method that is widely used to determine the condition of various mechanical systems, will be applied in this work. In data acquisition, a transducer is attached to the structure under investigation and the vibration signal is recorded. This signal is then processed to extract representative features for fault detection. Signal processing techniques are therefore required to extract representative features to assess the health condition of gearbox components. However, in practice, the theoretical frequencies and characteristic features of gearbox faults may be modulated and masked by parasitical frequencies due to numerous noisy vibrations, as well as by the complexity of the transmission mechanics. To solve the related problems, the objective of this research work is to propose new signal processing technologies to evaluate gearbox health conditions. This work will focus on fixed-axis gearboxes, in which all gears are designed to rotate around their perspective fixed centers. Firstly, an enhanced morphological filtering (eM) technique is proposed to improve signal-to-noise ratio. Secondly, under controlled operating conditions, an integrated Hilbert Huang transform (iHT) method is suggested for bearing fault detection. Thirdly, a leakage-free resonance sparse decomposition (LRSD)-based technique is developed for advanced vibration signal analysis to eliminate random noise and to recognize characteristic features for bearing in gearboxes health conditions. The effectiveness of the proposed techniques is verified by a series of experimental tests corresponding to different bearing and gearbox conditions.
Author: Turker Sengoz Publisher: ISBN: Category : Languages : en Pages :
Book Description
Induction motors (IMs) are commonly used in industry. Online IM health condition monitoring aims to recognize motor defect at its early stage to prevent motor performance degradation and reduce maintenance costs. The most common fault in IMs is related to bearing defects. Although many signal processing techniques have been proposed in literature for bearing fault detection using vibration and stator current signals, reliable bearing fault diagnosis still remains a challenging task. One of the reasons is that a rolling element bearing is not a simple component, but a system; its related features could be time-varying and nonlinear in nature. The objective of this study is to investigate an online condition monitoring system for IM bearing fault detection. The monitoring system consists of two main modules: smart data acquisition (DAQ) and bearing fault detection. In this work, a smart current sensor system is developed for data acquisition wirelessly. The DAQ system is tested for wireless data transmission, consistent data sampling, and low power consumption. The data acquisition operation is controlled by using an adaptive interface. In bearing fault detection, a generalized Teager-Kaiser energy (GTKE) technique is proposed for nonlinear bearing feature extraction and fault detection using both vibration and current signals. The proposed GTKE technique will demodulate the signal by tracking the instantaneous signal energy. An optimization method is proposed to enhance the fault-related features and improve signal-to-noise ratio. The effectiveness of the proposed technique is verified experimentally using a series of IM tests. The robustness is examined under different operating conditions.
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Author: Qingkai Han
Author: Sajid Hussain
Author: Juanjuan SHI
Author: Lindy Williams
Author: Xingkai Yang
Author: Stephan Schmidt
Author: Shazali Osman
Author: Sharad Jain
Author: Turker Sengoz