Detection and Diagnosis of Rolling Element Bearing Faults Using Time Encoded Signal Processing and Recognition PDF Download

Author: Shukri Ali Abdusslam
Publisher:
ISBN:
Category :
Languages : en
Pages :

View

Book Description
This thesis presents a systematic study of using TESPAR (Time Encoded Signal Processing and Recognition), which presently is in use as an effective tool for speech recognition and shows great advantages in computational demands and accuracy, to develop a new technique for rolling element bearing fault detection and diagnosis. The fundamentals of rolling element bearings are presented in line with different failure modes and relevant monitoring methods in the time domain, the frequency domain, the envelope spectrum and the wavelet analysis. These reviews show that vibration measurements are a proven and widely accepted data source for bearing monitoring of machinery. This research thus has focused on developing TESPAR based approaches using vibration signals which are generated from bearings under different severities of faults located at the outer race, the inner race and the roller element. It firstly examines the theoretical basis of TESPAR and examines the diagnosis performance with a number of different simulated signals, which confirms that TESPAR based methods are able to resolve different signals by using their statistics including S-matrix, A-matrix and epoch duration, which paves a frame work to process and interpolate the bearing signal. With understandings of the insights of bearing vibrations and TESPAR approaches a signal processing framework is then suggested to analyse bearing vibration signals. It consists of a pre-processing step which removes possible noise in the signal, a TESPAR coding step which converts the signal into TESPAR representations-TESPAR streams, a feature calculation step, which produces different TESPAR statistic parameters, and finally a diagnosis step which applies common statistics to TESPAR statistic parameters to obtain required results. The TESPAR solution proposed in this thesis shows that discrimination between different bearing signal waveforms has been implemented successfully. TESPAR S- and A-Matrices were constructed for the cases tested and used together with statistical correlation to differentiate between the types of faults. However, the severities of bearing faults have been identified using another TESPAR feature called the mean absolute magnitude value calculated using epoch durations. The performance of the TESPAR approach was then evaluated against the envelope spectrum; this being the most common method for bearing condition monitoring that is conducted in two terms; the process complexity and diagnosis performance. A major contribution of this research programme is the development of a method that can provide improved detection and diagnosis of bearing fault types and severity of faults seeded into roller bearings.

Author: Andrew Ball
Publisher: Springer Nature
ISBN: 3030577457
Category : Technology & Engineering
Languages : en
Pages : 1566

View

Book Description
This book gathers select contributions from the 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2019), held at the University of Huddersfield, UK in September 2019, and jointly organized by the University of Huddersfield and COMADEM International. The aim of the Congress was to promote awareness of the rapidly emerging interdisciplinary areas of condition monitoring and diagnostic engineering management. The contents discuss the latest tools and techniques in the multidisciplinary field of performance monitoring, root cause failure modes analysis, failure diagnosis, prognosis, and proactive management of industrial systems. There is a special focus on digitally enabled asset management and covers several topics such as condition monitoring, maintenance, structural health monitoring, non-destructive testing and other allied areas. Bringing together expert contributions from academia and industry, this book will be a valuable resource for those interested in latest condition monitoring and asset management techniques.

Author: Zihao Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :

View

Book Description
Rolling element bearings are widely used in rotating machinery. Bearing health condition monitoring plays a vital role in predictive maintenance to recognize bearing faults at an early stage to prevent machinery performance degradation, improve operation quality, and reduce maintenance costs. Although many signal processing techniques have been proposed in literature for bearing fault diagnosis, reliable bearing fault detection remains challenging. This study aims to develop an online condition monitoring system and a signal processing technique for bearing fault detection. Firstly, a Zigbee-based smart sensor data acquisition system is developed for wireless vibration signal collection. An enhanced Teager-Huang transform (eTHT) technique is proposed for bearing fault detection. The eTHT takes the several processing steps: Firstly, a generalized Teager-Kaiser spectrum analysis method is suggested to recognize the most representative intrinsic mode functions as a reference. Secondly, a characteristic relation function is constructed by using cross-correlation. Thirdly, a denoising filter is adopted to improve the signal-to-noise-ratio. Finally, the average generalized Teager-Kaiser spectrum analysis is undertaken to identify the bearing characteristic signatures for bearing fault detection. The effectiveness of the proposed eTHT technique is examined by experimental tests corresponding to different bearing conditions. Its robustness in bearing fault detection is examined by the use of the data sets from a different experimental setup.

Author: Hosameldin Ahmed
Publisher: John Wiley & Sons
ISBN: 1119544645
Category : Technology & Engineering
Languages : en
Pages : 475

View

Book Description
Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Author: Rea, Pierluigi
Publisher: IGI Global
ISBN: 1799867234
Category : Computers
Languages : en
Pages : 314

View

Book Description
Cyber-physical systems (CPS) can be defined as systems in which physical objects are represented in the digital world and integrated with computation, storage, and communication capabilities and are connected to each other in a network. The goal in the use of the CPS is integrating the dynamics of the physical processes with those of the software and networking, providing abstractions and modelling, design, and analysis techniques for the integrated whole. The notion of CPS is linked to concepts of robotics and sensor networks with intelligent systems proper of computational intelligence leading the pathway. Recent advances in science and engineering improve the link between computational and physical elements by means of intelligent systems, increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. The potential of cyber-physical systems will spread to several directions, including but not limited to intervention, precision manufacturing, operations in dangerous or inaccessible environments, coordination, efficiency, Maintenance 4.0, and augmentation of human capabilities. Design, Applications, and Maintenance of Cyber-Physical Systems gives insights about CPS as tools for integrating the dynamics of the physical processes with those of software and networking, providing abstractions and modelling, design, and analysis techniques for their smart manufacturing interoperation. The book will have an impact upon the research on robotics, mechatronics, integrated intelligent multibody systems, Industry 4.0, production systems management and maintenance, decision support systems, and Maintenance 4.0. The chapters discuss not only the technologies involved in CPS but also insights into how they are used in various industries. This book is ideal for engineers, practitioners, researchers, academicians, and students who are interested in a deeper understanding of cyber-physical systems (CPS), their design, application, and maintenance, with a special focus on modern technologies in Industry 4.0 and Maintenance 4.0.

Author: Juanjuan SHI
Publisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages :

View

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: Atul Andhare
Publisher: LAP Lambert Academic Publishing
ISBN: 9783838357942
Category :
Languages : en
Pages : 128

View

Book Description
Rolling bearings are the most important machine elements. Proper functioning of a machine depends on condition of bearings. Vibrations help in diagnosing various faults in machines. Therefore, vibration based condition monitoring is the most popular method to know health of any machine. However, as found from the literature, vibration monitoring and diagnostics of faults in tapered roller bearing is not well established. This book is therefore focused on vibration based condition monitoring of tapered roller bearings. It presents results of experiments performed towards diagnosis of defects in tapered roller bearings using vibration analysis. The bearing vibration data are analyzed using various time and frequency domain techniques. The results for defect-free and defective bearings are compared to get information for defect diagnosis. A MATLAB based computer interface, which was developed for vibration signal processing and diagnostics, is also discussed in the book. This interface made use of all the time and frequency domain vibration data to diagnose defects in bearings. This book will be useful for the practicing engineers and students working on condition monitoring.

Author: Edgar Felipe Sierra Alonso
Publisher:
ISBN:
Category :
Languages : en
Pages : 99

View

Book Description

Author: Mohsen Nakhaeinejad
Publisher:
ISBN:
Category :
Languages : en
Pages : 282

View

Book Description
Modeling, fault assessment, and diagnostics of rolling element bearings and induction motors were studied. Dynamic model of rolling element bearings with faults were developed using vector bond graphs. The model incorporates gyroscopic and centrifugal effects, contact deflections and forces, contact slip and separations, and localized faults. Dents and pits on inner race, outer race and balls were modeled through surface profile changes. Experiments with healthy and faulty bearings validated the model. Bearing load zones under various radial loads and clearances were simulated. The model was used to study dynamics of faulty bearings. Effects of type, size and shape of faults on the vibration response and on dynamics of contacts in presence of localized faults were studied. A signal processing algorithm, called feature plot, based on variable window averaging and time feature extraction was proposed for diagnostics of rolling element bearings. Conducting experiments, faults such as dents, pits, and rough surfaces on inner race, balls, and outer race were detected and isolated using the feature plot technique. Time features such as shape factor, skewness, Kurtosis, peak value, crest factor, impulse factor and mean absolute deviation were used in feature plots. Performance of feature plots in bearing fault detection when finite numbers of samples are available was shown. Results suggest that the feature plot technique can detect and isolate localized faults and rough surface defects in rolling element bearings. The proposed diagnostic algorithm has the potential for other applications such as gearbox. A model-based diagnostic framework consisting of modeling, non-linear observability analysis, and parameter tuning was developed for three-phase induction motors. A bond graph model was developed and verified with experiments. Nonlinear observability based on Lie derivatives identified the most observable configuration of sensors and parameters. Continuous-discrete Extended Kalman Filter (EKF) technique was used for parameter tuning to detect stator and rotor faults, bearing friction, and mechanical loads from currents and speed signals. A dynamic process noise technique based on the validation index was implemented for EKF. Complex step Jacobian technique improved computational performance of EKF and observability analysis. Results suggest that motor faults, bearing rotational friction, and mechanical load of induction motors can be detected using model-based diagnostics as long as the configuration of sensors and parameters is observable.

Author: Independently Published
Publisher:
ISBN: 9781723799945
Category :
Languages : en
Pages : 66

View

Book Description
Dynamic equipments, in their vast majority, have rolling bearings in their components. Measurement and analysis of the values of rolling bearings vibration, on time, represent a safe and effective measure for identifying the state of wear of bearings, and to predict the evolution of their technical condition and of the entire equipment. The fault diagnosis of self aligning ball bearing (1205K type) at angular misalignments is the main concern of the study. This paper presents the detection of causes which lead to the damage of rolling bearing by using bearing condition signature spectrums in frequency domain and root mean square values of acceleration in time domain, by measuring its housing vibrations mounted in a test rig. The bearing fault simulator system is created for vibration signal observation by inducing fault in the bearing and misalignments in shaft. The main components of vibration observing system used is piezoelectric transducer, data transfer cable, vibscanner gadget and pc, signal investigation omnitrend software. A test bench consists of a motor and a shaft attached along with two different bearing housings with rotor disc at the centre giving constant loading. The particular imperfections are considered as defect in outer race, inner race and ball element and vibrations are recorded for five speeds and five different angles. It is clear from both the time domain and frequency domain signals that for the same defect size the maximum vibrations are produced by the bearing with ball defect and then by the outer race fault and lastly minimum by the inner race faulty bearing. So, on the basis of the results it becomes easy to recognize and diagnose the type of fault occurred in the bearing during it operation before the major failure.