Dynamics-guided Vibration Signal Analysis for Fixed-axis Gearbox Fault Diagnosis PDF Download

Author: Xingkai Yang
Publisher:
ISBN:
Category : Fault location (Engineering)
Languages : en
Pages : 0

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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: Xihui Liang
Publisher:
ISBN:
Category : Science
Languages : en
Pages :

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Book Description
Vibration analysis has been widely used to diagnose gear tooth fault inside a planetary gearbox. However, the vibration characteristics of a planetary gearbox are very complicated. Inside a planetary gearbox, there are multiple vibration sources as several sun-planet gear pairs, and several ring-planet gear pairs are meshing simultaneously. In addition, due to the rotation of the carrier, distance varies between vibration sources and a transducer installed on the planetary gearbox housing. Dynamics-based vibration signal modeling techniques can simulate the vibration signals of a planetary gearbox and reveal the signal generation mechanism and fault features effectively. However, these techniques are basically in the theoretical development stage. Comprehensive experimental validations are required for their future applications in real systems. This chapter describes the methodologies related to vibration signal modeling of a planetary gear set for gear tooth damage diagnosis. The main contents include gear mesh stiffness evaluation, gear tooth crack modeling, dynamic modeling of a planetary gear set, vibration source modeling, modeling of transmission path effect due to the rotation of the carrier, sensor perceived vibration signal modeling, and vibration signal decomposition techniques. The methods presented in this chapter can help understand the vibration properties of planetary gearboxes and give insights into developing new signal processing methods for gear tooth damage diagnosis.

Author: Xihui Liang
Publisher:
ISBN:
Category : Fault location (Engineering)
Languages : en
Pages : 201

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Book Description
Vibration analysis has been widely used to detect gear tooth fault inside a planetary gearbox. However, the vibration characteristics of a planetary gearbox are very complicated. Inside a planetary gearbox, there are multiple vibration sources as several sun-planet gear pairs and several ring-planet gear pairs are meshing simultaneously. In addition, due to the rotation of the carrier, distance varies between vibration sources and a transducer installed on gearbox housing. This thesis aims to simulate and understand the vibration signals of a planetary gear set, and then propose a signal processing method to detect gear tooth fault more effectively. First, an analytical method derives the equations of a healthy planetary gear set's time-varying gear mesh stiffness. Then, a gear tooth crack growth model is proposed and equations are derived to quantify the effect of gear tooth crack on the time-varying mesh stiffness. After that, a two-dimensional lumped-mass model is developed to simulate the vibration source signals of a planetary gear set; an analytical model is proposed to represent the effect of transmission path; and the resultant vibration signals of a planetary gear set at a sensor location are generated by considering multiple vibration sources and the effect of transmission path. Finally, a signal decomposition method is proposed to detect a single tooth crack in a single planet gear and experimental validation is performed. The methods proposed in this thesis help us understand the vibration properties of planetary gearboxes and give insights into developing new signal processing methods for gear tooth fault detection.

Author: Stephan Schmidt
Publisher:
ISBN:
Category :
Languages : en
Pages :

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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 :

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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: Shengli Zhang
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :

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Book Description
This study presents an effort in system level identification and gear dynamics analysis. The mechanical system usually includes several parts with different mechanisms to achieve a particular job. To simulate the motion of the parts, evaluate the performance, and analyze the vibration of the system, a system level modeling is needed. However, the modeling is challenging because of unknown parameters, nonlinearities, and uncertainties. System identification is one of the key techniques to obtain a reliable dynamic model by appropriately choosing the mathematical model, identifying the unknowns, and reducing the uncertainties. This study illustrates approaches and procedures in building system-level model for an electric impact wrench. Electric impact wrench, whose operation involves dynamic events occurring at vastly different time scales, is an important tool used in manufacturing and maintenance services where high torque is required. A first-principle-based, system-level model is built by incorporating the dynamics of gear transmission, spindle, and impacting components. The nonlinear impact and kinematic constraints are explicitly analyzed, and systematic parametric identification is performed based on a multi-objective optimization approach, i.e. archived multi-objective simulated annealing. The predictions from the model with system identification correlate well with the experimental results. In the system level modeling, it is found that gear transmission is one of the most popular and important sub-system whose dynamics and health conditions affect the system performance significantly. Therefore, this study also presents the effort in the gear dynamics analysis and fault diagnosis. It is well known that the nonlinear characteristics of the gearbox are mainly induced by time-varying mesh stiffness and backlash. To solve this nonlinear system, numeric method is usually employed whose time step has to be carefully controlled and the accuracy suffers from cumulative errors. To overcome the limitations of the numeric method, an approach, integrating Floquet theory with harmonic balance method, is proposed to analytically analyze the dynamics of the gearbox that subjects to parameter excitation and backlash nonlinearity. This approach can not only solve the steady-state system response, as traditional harmonic balance method, but also the transient response of the system. Case study verifies the accuracy of the proposed approach and its efficiency in calculating the frequency response of the system. The proposed method also accurately predicts the nonlinear jump of the gearbox. In the gear fault diagnosis, a fault signature enhancement method, i.e. angle-frequency domain synchronous averaging, is developed. This method is capable of highlighting the fault related features from the nonstationary and noisy vibration signal. Rather than being averaged in time-domain as traditional method, the vibration signal is averaged in angle-frequency domain after being resampled from time domain into angle domain and analyzed by the joint angle-frequency technique so as to solve the phase shift problem. The enhanced results are then analyzed through feature extraction algorithms, i.e. Kernel Principal Component Analysis, Multilinear Principal Component Analysis, and Locally Linear Embedding, to extract the most distinct features for fault classification and identification. Experimental study demonstrates that the proposed method significantly enhances the fault related features and improves the identification rate of support vector machine in identifying multi gear faults.

Author: Qingkai Han
Publisher: Springer
ISBN: 9789811096952
Category : Technology & Engineering
Languages : en
Pages : 164

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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: Catalin Dascaliuc
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Yunpeng Guan
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Time-frequency methods are widely used tools to diagnose planetary gearbox fault under non-stationary conditions. However, the existing time-frequency methods still have some problems, such as smearing effect and cross-term interference, and these problems limit the effectiveness of the existing time-frequency methods in planetary gearbox fault diagnosis under non-stationary conditions. To address the aforementioned problems, four time-frequency methods are proposed in this thesis. As nowadays a large portion of the industrial equipment is equipped with tachometers, the first three methods are for the cases that the shaft rotational speed is easily accessible and the last method is for the cases of shaft rotational speed is not easily accessible. The proposed methods are itemized as follows: (1) The velocity synchronous short-time Fourier transform (VSSTFT), which is a type of linear transform based on the domain mappings and short-time Fourier transform to address the smear effect of the existing linear transforms under known time-varying speed conditions; (2) The velocity synchrosqueezing transform (VST), which is a type of remapping method based on the domain mapping and synchrosqueezing transform to address the smear effect of existing remapping methods under known time-varying speed conditions; (3) The velocity synchronous bilinear distribution (VSBD), which is a type of bilinear distribution based on the generalized demodulation and Cohen's class bilinear distribution to address the smear effect and cross-term interference of existing bilinear distributions under known time-varying speed conditions and (4) The velocity synchronous linear chirplet transform (VSLCT), which is a non-parametric combined approach of linear transform and concentration-index-guided parameter determination to provide a smear-free and cross-term-free TFR under unknown time-varying speed conditions. In this work, simple algorithms are developed to avoid the signal resampling process required by the domain mappings or demodulations of the first three methods (i.e., the VSSTFT, VST and VSBD). They are designed to have different resolutions, readabilities, noise tolerances and computational efficiencies. Therefore, they are capable to adapt different application conditions. The VSLCT, as a kind of linear transform, is designed for unknown rotational speed conditions. It utilizes a set of shaft-rotational-speed-synchronous bases to address the smear problem and it is capable to dynamically determine the signal processing parameters (i.e., window length and normalized angle) to provide a clear TFR with desirable time-frequency resolution in response to condition variations. All of the proposed methods in this work are smear-free and cross-term-free, the TFRs generated by the methods are clearer and more precise compared with the existing time-frequency methods. The faults of planetary gearboxes, if any, can be diagnosed by identifying the fault-induced components from the obtained TFRs. The four methods are all newly applied to fault diagnosis. The effectiveness of them has been validated using both simulated and experimental vibration signals of planetary gearboxes collected under non-stationary conditions.

Author: Robert Bond Randall
Publisher: John Wiley & Sons
ISBN: 0470977582
Category : Technology & Engineering
Languages : en
Pages : 409

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Book Description
"Without doubt the best modern and up-to-date text on the topic, wirtten by one of the world leading experts in the field. Should be on the desk of any practitioner or researcher involved in the field of Machine Condition Monitoring" Simon Braun, Israel Institute of Technology Explaining complex ideas in an easy to understand way, Vibration-based Condition Monitoring provides a comprehensive survey of the application of vibration analysis to the condition monitoring of machines. Reflecting the natural progression of these systems by presenting the fundamental material and then moving onto detection, diagnosis and prognosis, Randall presents classic and state-of-the-art research results that cover vibration signals from rotating and reciprocating machines; basic signal processing techniques; fault detection; diagnostic techniques, and prognostics. Developed out of notes for a course in machine condition monitoring given by Robert Bond Randall over ten years at the University of New South Wales, Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications is essential reading for graduate and postgraduate students/ researchers in machine condition monitoring and diagnostics as well as condition monitoring practitioners and machine manufacturers who want to include a machine monitoring service with their product. Includes a number of exercises for each chapter, many based on Matlab, to illustrate basic points as well as to facilitate the use of the book as a textbook for courses in the topic. Accompanied by a website www.wiley.com/go/randall housing exercises along with data sets and implementation code in Matlab for some of the methods as well as other pedagogical aids. Authored by an internationally recognised authority in the area of condition monitoring.