Author: Nader AfshariPublisher:
ISBN:
Category :
Languages : en
Pages : 250
Book Description
Model-based Techniques for Real-time Fault Detection of Rolling Element Bearings
Author: Nader AfshariFault Detection for Rolling Element Bearings Using Model-based Technique
Author: Sorn SimatrangPublisher:
ISBN:
Category : Electrical engineering
Languages : en
Pages : 0
Book Description
In this research, we focus on fault detection of rolling element bearings by using a model-based technique. Vibration data are obtained from the mathematical model developed by Michael Louis Adams in [1]. The model is a time-varying nonlinear model with 29 degrees of freedom (DOF), derived by determining the energy terms of the Lagrange equations. Fault frequencies and fault types include outer race (OR), Ball and Inner race (IR). In order to determine the model for performing fault detection, we use the Hammerstein-Wiener model for observer design. The results of system identification for each of the fault types are provided. The Hammerstein-Wiener models were used for observer design and implementation, and we exploited the cross-correlation between observer residuals and temporal features of OR and IR faults for detection and diagnosis.
Fault Detection and Model-based Diagnostics in Nonlinear Dynamic Systems
Author: Mohsen NakhaeinejadPublisher:
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.
Morphology-based Fault Feature Extraction and Resampling-free Fault Identification Techniques for Rolling Element Bearing Condition Monitoring
Author: Juanjuan SHIPublisher:
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.
Methods for Rolling Element Bearing Fault Diagnosis Under Constant and Time-varying Rotational Speed Conditions
Author: Huan HuangPublisher:
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.
Non-parametric and Non-filtering Methods for Rolling Element Bearing Condition Monitoring
Author: Hamid FaghidiPublisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages :
Book Description
Rolling element bearings are one of the most significant elements and frequently-used components in mechanical systems. Bearing fault detection and diagnosis is important for preventing productivity loss and averting catastrophic failures of mechanical systems. In industrial applications, bearing life is often difficult to predict due to different application conditions, load and speed variations, as well as maintenance practices. Therefore, reliable fault detection is necessary to ensure productive and safe operations. Vibration analysis is the most widely used method for detection and diagnosis of bearing malfunctions. A measured vibration signal from a sensor is often contaminated by noise and vibration interference components. Over the years, many methods have been developed to reveal fault signatures, and remove noise and vibration interference components. Though many vibration based methods have been proposed in the literature, the high frequency resonance (HFR) technique is one of a very few methods have received certain industrial acceptance. However, the effectiveness of the HFR methods depends, to a great extent, on some parameters such as bandwidth and centre frequency of the fault excited resonance, and window length. Proper selection these parameters is often a knowledge-demanding and time-consuming process. In particular, the filter designed based on the improperly selected bandwidth and center frequency of the fault excited resonance can filter out the true fault information and mislead the detection/diagnosis decisions. In addition, even if these parameters can be selected properly at beginning of each process, they may become invalid in a time-varying environment after a certain period of time. Hence, they may have to be re-calculated and updated, which is again a time-consuming and error-prone process. This undermines the practical significance of the above methods for online monitoring of bearing conditions. To overcome the shortcomings of existing methods, the following four non-parametric and non-filtering methods are proposed: 1. An amplitude demodulation differentiation (ADD) method, 2. A calculus enhanced energy operator (CEEO) method, 3. A higher order analytic energy operator (HO_AEO) approach, and 4. A higher order energy operator fusion (HOEO_F) technique. The proposed methods have been evaluated using both simulated and experimental data.
Detection and Diagnosis of Rolling Element Bearing Faults Using Time Encoded Signal Processing and Recognition
Author: Shukri Ali AbdusslamPublisher:
ISBN:
Category :
Languages : en
Pages :
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.
Integrated Auto-diagnosis Based on Stochastic Model for Rolling Element Bearings
Author: Yaqiang JinPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Today, the most fundamental issue of condition monitoring in most industrial plants is fault diagnostics and prognostics. One of the most effective approaches to investigate this issue is condition monitoring based on vibration signal analysis. With the development of industry, multi-threaded maintenance and multi-channel acquisition are becoming more widespread in the current, which put forward higher requirements for maintenance. Based on this observation, it is proposed in this thesis one automated diagnosis framework for the rolling element bearing that integrates the successive steps of fault detection, fault type identification, fault signal reconstruction and fault size characterization. The advantage is that the complete diagnosis process is completed at once, while involving only one key hyperparameter, which improves the degree of automation of current Condition Based Maintenance (CBM) and liberating human participation. In the presence of incipient fault, vibrations of rolling element bearings show symptomatic signatures in the form of repetitive impulses. This can be seen as a non-stationary signal whose statistical properties switch between two states. The proposed maintenance strategy models such characteristics with an explicit-duration hidden Markov model (EDHMM) and uses the estimated model parameters to perform integrated diagnosis without requiring the user's expertise. The detection of a fault is first achieved by means of a likelihood ratio test built on the EDHMM parameters. One statistical counting approach and posterior probability spectrum are then used for identifying the fault type automatically. In order to obtain the fault signal in some cases, one Bayesian filter based on the EDHMM parameters is constructed. Finally, the fault size is estimated from the duration times returned by EDHMM. Subsequently, the capability of the integrated auto-diagnosis framework is illustrated on different experimental datasets. The first validation is forced on the vibration data for specific conditions. The results prove the robust and accurate maintenance of the rolling element bearing. In addition, the result of accelerated degradation data also shows the effectiveness of the method, especially the ability of detecting failure occurrence and tracking quantitatively fault development. This technique has potential for using in the machine CBM.
A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings
Author: Shahab Hasanzadeh GhafariPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Author: Lu Qian