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![Illustrative example of amplitude modulation [3].](profile/Andrew-Ball-3/publication/254495749/figure/fig1/AS:297818267897858@1448016816417/Illustrative-example-of-amplitude-modulation-3.png)
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![Figure 1. Illustrative example of amplitude modulation [3].](profile/Andrew-Ball-3/publication/254495749/figure/fig1/AS:297818267897858@1448016816417/Illustrative-example-of-amplitude-modulation-3_Q320.jpg)
![Figure 6. YD-5 accelerometers [2].](profile/Andrew-Ball-3/publication/254495749/figure/fig3/AS:669223378493472@1536566695940/YD-5-accelerometers-2_Q320.jpg)
Many new bearing monitoring and diagnosis methods have been explored in the last two decades to provide a technique that is capable of picking up an incipient bearing fault. Vibration analysis is a commonly used condition monitoring technique in world industry and has proved an effective method for rolling bearing monitoring systems. The focus of t...
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Context 1
... analysis is a common technique for bearing faults detection [3]. Suppose the resonance frequency is a continuous pure sine and the impulses have the form of a sine wave of much lower frequency, the signal detected will be a modulated signal which has the form of the individual signals multiplied together, as shown in figure 1. Basically, envelope analysis extracts the time history of the signals modulating the amplitude of the measured vibration signal. ...
Context 2
... three-dimensional patterns can be used to completely discriminate the faults. Figure 11 shows the S-Matrices of the envelope signal and the change in normalised values, expressed as a percentage of the envelope signal symbols for the healthy bearing and for three bearing faults seeded into the outer race, roller element and inner race of three similar bearings. As shown in figure 11(a) the healthy bearing is defined by its difference from the faulty conditions for symbols 2, 6 and 8, symbol 2 shows the most difference between the healthy and faulty bearings while the symbols for the faulty bearings have close amplitudes perhaps except symbol 3. Figure 11(b) reveals the symbols' normalization to the healthy case, symbol 8 exhibits the most difference between the four cases, and in addition, the faulty bearings in this symbol show the biggest difference comparing with the healthy case. ...
Context 3
... 11 shows the S-Matrices of the envelope signal and the change in normalised values, expressed as a percentage of the envelope signal symbols for the healthy bearing and for three bearing faults seeded into the outer race, roller element and inner race of three similar bearings. As shown in figure 11(a) the healthy bearing is defined by its difference from the faulty conditions for symbols 2, 6 and 8, symbol 2 shows the most difference between the healthy and faulty bearings while the symbols for the faulty bearings have close amplitudes perhaps except symbol 3. Figure 11(b) reveals the symbols' normalization to the healthy case, symbol 8 exhibits the most difference between the four cases, and in addition, the faulty bearings in this symbol show the biggest difference comparing with the healthy case. ...
Context 4
... 11 shows the S-Matrices of the envelope signal and the change in normalised values, expressed as a percentage of the envelope signal symbols for the healthy bearing and for three bearing faults seeded into the outer race, roller element and inner race of three similar bearings. As shown in figure 11(a) the healthy bearing is defined by its difference from the faulty conditions for symbols 2, 6 and 8, symbol 2 shows the most difference between the healthy and faulty bearings while the symbols for the faulty bearings have close amplitudes perhaps except symbol 3. Figure 11(b) reveals the symbols' normalization to the healthy case, symbol 8 exhibits the most difference between the four cases, and in addition, the faulty bearings in this symbol show the biggest difference comparing with the healthy case. ...
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Fault diagnosis is useful for ensuring the safe running of machines. Vibration analysis is one of the most important techniques for fault diagnosis of rotating machinery; as the vibration signal carries the dynamic information of the system. Many signal analysis methods are able to extract useful information from vibration data. In the present work...
Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to nois...
In this paper, we will discuss the efficiency of a modified Hilbert Huang transform (HHT) approach [1, 2], which is largely applied to fault diagnosis for rolling bearing and vibration analysis, for the time-frequency analysis of the GPR signals. The aim of this approach is to improve the readability of the HHT spectrum and hence improving the visi...
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Citations
... 2. Literature review [2] performed Bearing defect detection and diagnosis using a time encoded signal processing and pattern recognition method. He combined the two conventional methods: wavelet transform and envelope analysis for bearing monitoring and diagnosis. ...
The potential for machine Condition Monitoring (CM) to enhance system performance and forestall harmful failures has been rising considerably. It is applicable to all or any rotating machinery where preventive fault diagnosis could be a must like Engines, drive trains, gearboxes, pumps, turbines, compressors, fans etc. Detection of bearing faults is one in all the foremost challenging tasks in bearing health condition monitoring, especially when the fault is at its initial stage. The defects in bearing unless detected in time may result in malfunctioning of the machinery. Acquisition of vibration signals coming from various machine components, performing its analysis for prediction of faults and understanding the root cause for high vibrations has been an established practice. However, while performing analysis of vibration signals, it is close to impossible to separate out and focus on ‘bearing frequencies’ especially in the presence of strong masking signals from other machine components. In this paper a study and implementation of ‘advanced’ vibration analysis technique viz envelope analysis for localization of bearing frequencies from the masking signals generated by other machine components is discussed. A smart graphical user interface-based software tool has been developed for automatic detection of bearing faults. The automatic detection techniques presented in this paper can be very helpful for condition monitoring especially for early diagnosis of bearing faults. This may help industries for minimizing the downtime due to machine breakdown. With the help of vibration sensor (accelerometer), FFT analyzer, set of faulty bearings installed with rotating arrangement and a graphical user interface-based software tool designed with the help of multi-paradigm programming language, the setup for automatic detection of bearing faults has been validated.
... Важливість моніторингу стану та діагностики несправностей за останні десятиліття набула широкого визнання як в академічних, так і в промислових сферах. Щорічно проводиться декілька міжнародних конференцій на такі теми та тисячі публікацій у наукових виданнях, де вивчаються різні теми, враховуючи методи моніторингу [3], механічне моделювання дефектів [4][5], діагностику та прогноз несправностей [6][7]. Однак такі ефективні та передові методи рідко добре використовуються промисловістю. ...
Зростання глобальної конкуренції призвело до помітних змін у способах роботи виробничих компаній. Ці зміни вплинули на технічне обслуговування і зробили його роль ще більш важливою для успіху бізнесу. Щоб залишатися конкурентоспроможними, виробничі компанії мають постійно підвищувати ефективність своїх виробничих процесів. Те, що технічне обслуговування стає все більш важливим для промислового сектору, очевидно з поточних обговорень національних програм індустріалізації. При цьому, незважаючи на зростаючий попит на надійне виробниче обладнання, деякі компанії-виробники займаються розробкою стратегічного обслуговування. Крім того, традиційних стратегій технічного обслуговування, таких як реактивне технічне обслуговування, більше недостатньо для задоволення промислових потреб, наприклад, як максимальне скорочення відмов і втоми виробничих систем. У цій статті наведено аналіз стратегій технічного обслуговування. Їх огляд спрямований на те, щоб вказати на важливість незапланованих простоїв, які можуть статися під час роботи обладнання на виробничому підприємстві. Для підтримки працездатного стану машини було розроблено різні стратегії технічного обслуговування, серед яких найбільш популярними є реактивне технічне обслуговування, планово-профілактичне обслуговування і обслуговування на основі технічного стану. Метод прогнозованого стану більш ефективний для технічного обслуговування складних і відповідальних машин, але планово-профілактичне обслуговування залишається найбільш поширеною програмою технічного обслуговування.
... Transform (FFT) is used. This process changes the time domain signals into frequency domain signals that can then be employed for the analysis of the machine conditions [11]. If there is a local fault in the bearing or other mechanical components are not properly fitted; vibration amplitude occurs. ...
Rolling bearings are very critical components of rotating machines, and the presence of defects in the bearing may lead to failure of the machine. Hence, early detection of such defects under operating condition of the bearing may avoid malfunctioning and breakdown of the machine. Defective bearings are source of vibration and these vibration signals can be used to evaluate the faulty bearings. This research paper seeks to detect and diagnose roller bearing early faults using vibration data analysis techniques. Bearings of three different conditions, healthy bearing (baseline), outer race fault and inner race fault were considered. Time domain, frequency domain and the envelope spectrum techniques were utilised to analyze the experimental collected data. The results of the investigation revealed that, the envelope spectrum technique proved to be the most reliable technique to detect rolling bearings faults, where the noise and interference sources are suppressed significantly. Furthermore, one degree of freedom non-linear dynamic model was also developed using Matlab Toolbox to study the bearing vibration response of healthy and faulty bearings. There was a good correlations between the numerically simulated and experimental results, and it is proved that this model is reasonable and can be implemented for the study and prediction of roller bearings early defects.
... Transform (FFT) is used. This process changes the time domain signals into frequency domain signals that can then be employed for the analysis of the machine conditions [11]. If there is a local fault in the bearing or other mechanical components are not properly fitted; vibration amplitude occurs. ...
... By dividing the continuous data state space into amounts of discrete cell and gives each cell a number symbol, which transforms the complex data into symbols sequence flow. The signal characteristics of large scale data are able to be captured for reducing the dynamics noise impact in this method [11,12]. ...
Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is used to extracted features which constitute A-matrix. GMM is utilized to approximate the density distribution of singular values decomposed by A-matrix. TENLLP(Time-Encoded Negative Log Likelihood Probability) serves as a fault severity which can display the similarity of the singular values between normal samples and fault samples as quantificational. Results of its application to bearing fatigue test show that this performance degradation assessment can detect the incipient rolling bearing fault and be sensitive to the change of fault.
Condition-based maintenance (CBM) as a new maintenance philosophy can avoid the occurrence of insufficient and excessive maintenance efforts. For the purpose of quantitative assessment of bearing performance degradation underlying CBM, a GMM (Gauss mixture model) and TESPAR (time encoded signal processing and recognition) based approach is formulated. The S matrices in the TESPAR parameters of the bearing signals collected from fault-free stage are extracted as the original features, and dimensionally reduced with principal component analysis (PCA) to construct the feature vectors; and a GMM model for the fault-free bearing is established and trained with the S matrices. The S matrices from subsequent bearing stages are dimensionally reduced, and then fed to the trained GMM model to obtain the quantitive similarity degree between the fault-free sample and the sample under test. Such similarity degree serves as a fault severity index, which is herein called TELLP (time encoded log-likelihood probability). Experiment results of bearing fatigue test show that the proposed method is able to detect incipient bearing faults and can track the fault progress trend well.
