Experimental Analysis of Fault Diagnosis of Self Aligning Ball Bearing Under Various Speeds and Angular Misalignments Using Vibration Signal Processing PDF Download
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Author: Independently Published Publisher: ISBN: 9781723799945 Category : Languages : en Pages : 66
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.
Author: Independently Published Publisher: ISBN: 9781723799945 Category : Languages : en Pages : 66
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.
Author: Huan Huang Publisher: ISBN: Category : Languages : en Pages :
Book Description
Bearings are among the most commonly used components in rotating machines and bearing fault diagnosis has been widely investigated, especially with vibration signal based techniques. Generally, bearing faults can be diagnosed in the frequency domain since each type of fault has a specific Fault Characteristic Frequency (FCF) proportional to the rotational frequency. However, this approach is complicated by the facts that: (1) bearing vibration signals are often contaminated by random noise and interference signals transmitted from other sources, (2) bearings often operate under time-varying speed conditions which make the FCF also time-varying. To address the problems for bearing fault signature extraction, new methods are proposed in this thesis. For the constant speed case, bearing fault signature extraction using the method of Oscillatory Behavior-based Signal Decomposition (OBSD) is investigated, which includes: (1) effects of parameter selection and (2) automatic parameter selection of OBSD for bearing fault signature extraction. For bearing fault diagnosis under time-varying speed conditions, research based on the time-frequency technique is conducted, including (1) a multiple time-frequency curve extraction algorithm and (2) a resampling-free and tachometer-free method for the time-varying speed case with the presence of interferences, and (3) proposing a short-time kurtogram for bearing fault signature extraction under time-varying speed conditions. The effectiveness of the proposed methods in this thesis has been validated by simulated signals and experimental data.
Author: John Piotrowski Publisher: CRC Press ISBN: 142001787X Category : Technology & Engineering Languages : en Pages : 865
Book Description
Rotating machinery is the heart of many industrial operations, but many engineers and technicians perform shaft alignment by guesswork or with limited knowledge of the tools and methods available to accurately and effectively align their machinery. Two decades ago, John Piotrowski conferred upon the field an unprecedented tool: the first edition of
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: Hussein Razzaq Al-Bugharbee Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Rolling element bearings (REBs) are one of the most critical mechanical components. Their failures can lead for catastrophic failures which might include great loss in economy or even in the lives of people. REBs are inherently dynamic and they demonstrate complex vibration behaviour where conventional vibration -based fault diagnosis methods might not give sensitive indicators of the presence of the defects. This thesis investigates the singular spectrum analysis (SSA) capabilities as completely data-based fault diagnosis method in REBs. The SSA is used to decompose the bearing vibration acceleration signals in a certain number of principal components having the trend, periodical components and structure-less noise. This thesis develops two methodologies to use SSA in different ways and for different purposes. The first methodology uses the SSA (i.e only the decomposition stage) to create a baseline space from healthy bearing vibration signals. Then, any new signals are projected onto this baseline space. From these projections, features are made and used for fault diagnosis purposes. In the second methodology, the SSA contributes to the development of an advanced signal pretreatment that efficiently improves representing the nonstationary bearing vibration signals by linear time invariant autoregressive (LTIVAR) model. Then the coefficients of LTIVAR model are used as features for fault diagnosis purposes.The two methodologies have been validated by using experimental data obtained from three different bearing test rigs. The data used in the analysis covers different defect locations and different defect severities. The results of both methodologies, in terms of correct classification, were compared to some other recent methodologies. In comparison, it is shown that both methodologies have a very good performance and they are superior to those methodologies.The thesis offers simple and efficient methodologies for a complete fault diagnosis in terms of fault detection, identification and severity estimation. Thus, these methodologies have a potential possibility for automation of the entire process of each method.
Author: Mohsen Nakhaeinejad Publisher: ISBN: Category : Languages : en Pages : 282
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: Zihao Chen Publisher: ISBN: Category : Languages : en Pages :
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: Atul Andhare Publisher: LAP Lambert Academic Publishing ISBN: 9783838357942 Category : Languages : en Pages : 128
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: Hamid A. Toliyat Publisher: CRC Press ISBN: 1420006282 Category : Technology & Engineering Languages : en Pages : 272
Book Description
With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme—thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect. Combines Theoretical Analysis and Practical Application Written by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles. Fault Modeling, Diagnosis, and Implementation in One Volume Based on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.
Author: Independently Published
Author: Independently Published
Author: Huan Huang
Author: John Piotrowski
Author: Juanjuan SHI
Author: Edgar Felipe Sierra Alonso
Author: Hussein Razzaq Al-Bugharbee
Author: Mohsen Nakhaeinejad
Author: Zihao Chen
Author: Atul Andhare
Author: Hamid A. Toliyat