Relevant bibliographies by topics / Degradation state of a bearing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Degradation state of a bearing'

Author: Grafiati
Published: 28 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Degradation state of a bearing"

1

Zheng, Yuhuang. "Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/3203959.

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Prognostics health management (PHM) of rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. The health indicator is extracted by using Hilbert–Huang entropy to process horizontal vibration signals obtained from bearings. We present a linear degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by the distribution of 600 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict its RUL. This method is suitable for the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict its RUL.
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Wang, Yaping, Chaonan Yang, Di Xu, Jianghua Ge, and Wei Cui. "Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM." Shock and Vibration 2021 (May 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/6615920.

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It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.
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Gan, Zu Wang, Chen Lu, Hong Mei Liu, and Tian Min Shan. "Real-Time Reliability Evaluation and Life Prediction for Bearings Based on Normalized Individual State Deviation." Applied Mechanics and Materials 764-765 (May 2015): 343–49. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.343.

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Most of the existing methods for bearing real-time reliability evaluation employ real-time transformation of traditional reliability indices, performance degradation trajectory analysis, and performance degradation distribution, which are usually limited in terms of accuracy and applicability. A method for real-time reliability evaluation and life prediction for bearings based on normalized individual state deviation is proposed in this study. First, a self-organizing map neural network is utilized to obtain the individual state deviation of a running rolling bearing. Second, individual state deviation is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for the realization of real-time reliability evaluation and residual life prediction. The proposed method combines population information with real-time monitoring information of individual bearings, and thus avoids the negligence of the real-time transformation of the monitored individual. The errors caused by the randomness of the individual bearing operational process are also reduced. Finally, the feasibility and efficiency of the proposed method is validated by performing run-to-failure experiments on bearings.
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Zhou, Qicai, Hehong Shen, Jiong Zhao, Xingchen Liu, and Xiaolei Xiong. "Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm." Shock and Vibration 2019 (April 1, 2019): 1–9. http://dx.doi.org/10.1155/2019/8471732.

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Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology.
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Zhang, Ying, Anchen Wang, and Hongfu Zuo. "Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors." Sensors 19, no. 4 (February 17, 2019): 824. http://dx.doi.org/10.3390/s19040824.

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This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.
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Tian, Qiaoping, and Honglei Wang. "Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression." Applied Sciences 11, no. 11 (May 23, 2021): 4773. http://dx.doi.org/10.3390/app11114773.

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High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.
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Gao, Tianhong, Yuxiong Li, Xianzhen Huang, and Changli Wang. "Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory." Sensors 21, no. 1 (December 29, 2020): 182. http://dx.doi.org/10.3390/s21010182.

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Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory. First, time-domain features are extracted from the bearing vibration signal and data are fused to build a health indicator (HI) and a state model of bearing degradation. Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established. The parameters of the Bayesian model are updated and bearing RUL is predicted by the Metropolis–Hastings algorithm. The method was validated by the XJTU-SY bearing open datasets and the prediction results are compared with the existing methods. Accuracy of the proposed method was demonstrated.
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Huang, Liangpei, Hua Huang, and Yonghua Liu. "A Fault Diagnosis Approach for Rolling Bearing Based on Wavelet Packet Decomposition and GMM-HMM." June 2019 24, no. 2 (June 2019): 199–209. http://dx.doi.org/10.20855/ijav.2019.24.21120.

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Considering frequency domain energy distribution differences of bearing vibration signal in the different failure modes, a rolling bearing fault pattern recognition method is proposed based on orthogonal wavelet packet decomposition and Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The orthogonal three-layer wavelet packet decomposition is used to obtain wavelet packet decomposition coefficients from low frequency to high frequency. Rolling bearing raw vibration signals are firstly decomposed into the wavelet signals of different frequency bands, then different frequency band signals are reconstructed respectively to extract energy features, which form feature vectors as the model input of GMM-HMM. A large number of samples are trained to get model parameters for different bearing faults, then several groups of test data are adopted to verify GMM-HMMs so different fault types of rolling bearings are recognized. By calculating the current state appearance probability of monitoring data in GMM-HMMs, different failure patterns are recognized and evaluated from the maximum probability. Similarly, we establish GMM-HMMs for different grade fault samples and evaluated the performance degradation state. Test results show that the proposed fault diagnosis approach can identify accurately the fault pattern of rolling bearings and evaluate performance degradation of bearings.
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Yu, He, Hong-ru Li, Zai-ke Tian, and Wei-guo Wang. "Rolling Bearing Degradation State Identification Based on LPP Optimized by GA." International Journal of Rotating Machinery 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9281098.

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In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD) and six typical features including relative energy spectrum entropy (LREE), relative singular spectrum entropy (LRSE), two-element multiscale entropy (TMSE), standard deviation (STD), RMS, and root-square amplitude (XR) are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP) is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.
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Zhu, Keheng. "Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance." Journal of Vibration and Control 24, no. 14 (April 5, 2017): 3194–205. http://dx.doi.org/10.1177/1077546317702030.

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Performance degradation assessment is crucial to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new method for performance degradation assessment of rolling element bearings is proposed based on hierarchical entropy (HE) and general distance. First, considering the nonlinear dynamic characteristics of bearing vibration signals, the HE method is utilized to extract feature vectors, which can obtain more bearing state information hidden in the vibration signals than sample entropy (SampEn) and multi-scale entropy (MSE). Then, the general distance between the feature vectors of the normal data and those of the tested data is designed as a degradation indicator by combining Euclidean distance and cosine angle distance. The experimental results indicate that this indicator can detect the incipient defects well and can effectively reflect the whole degradation process of rolling element bearings. Moreover, the designed indicator has some advantages over kurtosis and root mean square (RMS) values.
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Dissertations / Theses on the topic "Degradation state of a bearing"

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Popara, Nikola. "Využití umělé inteligence k monitorování stavu obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444960.

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This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Solano, Alvarez Wilberth. "Microstructural degradation of bearing steels." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/249201.

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The aim of the work presented in this thesis is to clarify one of the most fundamental aspects of fatigue damage in bearings steels through critical experiments, in particular whether damage in the form of cracks precedes hard “white-etching matter" formation, which is carbon supersaturated nanoscaled ferrite. Heat treatments have been designed to create four different crack types and distributions: scarce martensite plate cracks, fine grain boundary cracks, abundant martensite plate cracks, and surface cracks. Subsequent rolling contact fatigue experiments showed that the amount of hard white-etching matter is higher in pre-cracked samples compared to those without prior damage and that its formation mechanism is the frictional contact of disconnected surfaces within the bulk that elevate the temperature and localise deformation. These key experiments indicate that hard white-etching matter is the consequence, not the cause, of damage. Therefore, one way to avoid white-etching matter is by increasing the toughness of the material. The macroscopically homogenous distribution of microcracks proved also to be a useful rolling contact fatigue life enhancer due to damage deflection via crack branching and a powerful trap for diffusible hydrogen. Successful trapping was corroborated by the inability of hydrogen to cause crack propagation via embrittlement or accelerate white-etching matter generation during rolling contact fatigue. By also studying the behaviour of a nanostructured bainitic steel under rolling contact fatigue, it was found that its degradation mechanism is ductile void formation at bainitic ferrite/stress-induced martensite interfaces, followed by growth and coalescence into larger voids that lead to fracture along the direction of the softer phase as opposed to the conventional damage mechanism in 52100 steel of crack initiation at inclusions and propagation. Given the relevance of phase quantification in nanobainite and the possible surface artefacts introduced by preparation, alternative methods to X-ray diffraction such as magnetic measurements were also investigated. The lack of hard white-etching matter obtained in the carbide-free nanostructured bainite led to conclude that an alternative route to mitigate hard white-etching matter could be by eliminating pre-eutectoid carbides from the microstructure, therefore restricting their dissolution and ultimate carbon supersaturation of the mechanically deformed and homogenised nanoferrite.
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Patel, Prakash Shashikant. "Diffusion problems and degradation of bearing overlays." Thesis, London Metropolitan University, 1989. http://repository.londonmet.ac.uk/3192/.

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The corrosion and degradation of Pb-Sn bearing overlays Is a bilateral process . During service, the lubricant becomes corrosive due to the breakdown of inhibitors with subsequent oxidation occurring at operating temperatures (120-170[degrees]). ...
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Swan, Iain Peter. "The efffect of elastomeric bearing degradation on bridge dynamic response." Thesis, Edinburgh Napier University, 2006. http://researchrepository.napier.ac.uk/Output/3800.

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The dynamic response of highway bridges is a topic that has been thoroughly researched over many years. However, understanding of how the dynamic response of bridges is affected by the performance of their bearings over an extended period of time is, at present, not clearly defined. Although health monitoring of bridge structures is relatively advanced, the scope for further research is wide. The study presented in this thesis contains research on plate structures; ranging from a simple Euler-Bernoulli method to determine natural frequencies; modal analysis of a plate structure in the laboratory; FEA of the plate structure; modal analysis on a full-scale structure subjected to vehicle loading; and FEA of a simplified model representing the full-scale structure. A combination of these methods has allowed the conclusions presented herein to be drawn with respect to the effects of support degradation, and the consequent effect on structural performance. In the laboratory, modal analysis of a small-scale, thin rectangular plate of Perspex' has been completed. A series of boundary conditions have been investigated through altering the support offered to the plate by a series of springs, each with a different stiffness, to simulate bearings with different stiffnesses. Vibrations of varying frequency have also been forced upon the plate, and its response recorded. Displacement values provided the clearest indication of the effect of bearing stiffness, with the least stiff spring resulting in the largest displacement. Alteration of support stiffness in the model can have a marked effect on the resonant frequency of the plate (approx. 23 % frequency change between spring 1 (1.22 Nmm 1) and 5 (15.62 Nmm 1)). Full-scale testing on a highway bridge at Berwick-upon-Tweed on the Al, over the River Tweed, was completed in May 2005. These data form the baseline for future dynamic testing and condition monitoring of the structure. To describe the dynamic properties of the structure, the force generated by each type of vehicle traversing the structure was determined using instrumentation already in place on the bridge. Statistics drawn from the data are presented, which indicate that the bearings are functioning as expected, but are subjected to forces of a much larger magnitude due to overloaded HGVs than in current design specifications. Larger HGVs made up a small percentage of overall vehicles recorded, but contribute a much higher proportion of damage caused to the bridge. Displacement and frequency were both valid measures of bearing performance and therefore state of degradation. The method is offered as a condition monitoring test for bridges and their bearings.
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Alshimmeri, Fiasael. "Diagnosis of low-speed bearing degradation using acoustic emission techniques." Thesis, Cranfield University, 2017. http://dspace.lib.cranfield.ac.uk/handle/1826/12324.

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It is widely acknowledged that bearing failures are the primary reason for breakdowns in rotating machinery. These failures are extremely costly, particularly in terms of lost production. Roller bearings are widely used in industrial machinery and need to be maintained in good condition to ensure the continuing efficiency, effectiveness, and profitability of the production process. The research presented here is an investigation of the use of acoustic emission (AE) to monitor bearing conditions at low speeds. Many machines, particularly large, expensive machines operate at speeds below 100 rpm, and such machines are important to the industry. However, the overwhelming proportion of studies have investigated the use of AE techniques for condition monitoring of higher-speed machines (typically several hundred rpm, or even higher). Few researchers have investigated the application of these techniques to low-speed machines (<100 rpm), This PhD addressed this omission and has established which, of the available, AE techniques are suitable for the detection of incipient faults and measurement of fault growth in low-speed bearings. The first objective of this research program was to assess the applicability of AE techniques to monitor low-speed bearings. It was found that the measured statistical parameters successfully monitored bearing conditions at low speeds (10-100 rpm). The second objective was to identify which commonly used statistical parameters derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify the onset of a fault in either race. It was found that the change in AE amplitude and AE RMS could identify the presence of a small fault seeded into either the inner or the outer races. However, the severe attenuation of the signal from the inner race meant that, while AE amplitude and RMS could readily identify the incipient fault, kurtosis and the AE counts could not. Thus, more attention needs to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of severity of the fault. However, once the defect was established, it was found that of the parameters used only AE RMS was sensitive to defect size. The fourth objective was to assess whether the AE signal is able to detect defects located at either the centre or edge of the outer race of a bearing rotating at low speeds. It is found that all the measured AE parameters had higher values when the defect was seeded in the middle of the outer race, possibly due to the shorter path traversed by the signal between source and sensor which gave a lower attenuation than when the defect was on the edge of the outer race. Moreover, AE can detect the defect at both locations, which confirmed the applicability of the AE to monitor the defects at any location on the outer race.
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Elliott, David. "Corrosion inhibitors for load-bearing steels." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303645.

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Zambre, Nirupama. "Degradation Rate Modeling of Ohio State Bridges." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1205935821.

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Shields, Andrew James. "Discharge degradation of mica." Thesis, Glasgow Caledonian University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370768.

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Prescott, Robert. "The corrosion of alloys and metals in high-temperature chlorine-bearing gases." Thesis, University of Manchester, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236260.

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Boudghene, Stambouli Amine. "Degradation mechanism in zinc sulphide/manganese electroluminescent displays." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328389.

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Books on the topic "Degradation state of a bearing"

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(Organization), Northwest Environment Watch, ed. State of the Northwest. Seattle, Wash: Northwest Environment Watch, 1994.

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Ryan, John C. State of the Northwest. 2nd ed. Seattle, WA: Northwest Environment Watch, 2000.

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Blakely, William Addison. American state papers bearing on Sunday legislation. Buffalo, N.Y: W.S. Hein, 2000.

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Gaan, Narottam. Environmental degradation and inter-state conflict: India vs. Bangladesh. Denver: iAcademicBooks, 2001.

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The degradation of American history. Chicago, Ill: University of Chicago Press, 1997.

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Beswick, John M., ed. Bearing Steel Technology-Advances and State of the Art in Bearing Steel Quality Assurance: 7th Volume. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2007. http://dx.doi.org/10.1520/stp1465-eb.

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(Organization), Tebtebba. State of forests, policy environment & ways forward. Baguio City, Philippines: Tebtebba Foundation, 2010.

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Himalayan degradation: Colonial forestry and environmental change in India. New Delhi: Cambridge University Press India under the imprint of Foundation Books, 2009.

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The state bearing gifts: Deception and disaffection in Japanese higher education. Lanham, MD: Lexington Books, 2007.

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Western Australia. Water and Rivers Commission. The state of the northern rivers: A report designed to inform the community of the state of Western Australia's rivers in the Indian Ocean, Timor Sea and western plateau drainage divisions. East Perth, W.A: Water and Rivers Commission, 1997.

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Book chapters on the topic "Degradation state of a bearing"

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Chen, Hao, Niaoqing Hu, and Lun Zhang. "Rolling Bearing Degradation State Prediction with Deep Fusion Feature." In Advances in Asset Management and Condition Monitoring, 661–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57745-2_55.

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Sánchez-Silva, Mauricio, and Georgia-Ann Klutke. "Continuous State Degradation Models." In Springer Series in Reliability Engineering, 117–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20946-3_5.

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Sánchez-Silva, Mauricio, and Georgia-Ann Klutke. "Discrete State Degradation Models." In Springer Series in Reliability Engineering, 151–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20946-3_6.

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Koh, S., W. D. van Driel, C. A. Yuan, and G. Q. Zhang. "Degradation Mechanisms in LED Packages." In Solid State Lighting Reliability, 185–205. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3067-4_5.

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Číhal, Vladimír, Marie Blahetová, Zdenka Krhutová, Stanislav Lasek, and E. Kalabisová. "Passive State Degradation of Stainless Steel Welds." In Solid State Phenomena, 189–92. Stafa: Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/3-908451-49-3.189.

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Ohyama, H., K. Takakura, T. Nagano, M. Hanada, S. Kuboyama, Eddy Simoen, and C. Claeys. "Degradation and their Recovery Behavior of Irradiated GaAlAs LEDs." In Solid State Phenomena, 119–24. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/3-908451-43-4.119.

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Gosiewski, Zdzisław, and Krzysztof Falkowski. "The Differential Passive Magnetic Bearing for High-Speed Flexible Rotor." In Solid State Phenomena, 273–78. Stafa: Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/3-908451-60-4.273.

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Alexander, Mark G., and Nele De Belie. "Testing for Degradation by Inorganic Acids." In RILEM State-of-the-Art Reports, 289–303. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5413-3_11.

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Neugebauer, H., N. S. Sariciftci, H. Kuzmany, and A. Necked. "In Situ FTIR Spectroscopy of Polyaniline Degradation." In Springer Series in Solid-State Sciences, 315–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83833-0_59.

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Lee, Sang Kon, Hyun Sang Byun, Byung Min Kim, Dae Cheol Ko, and C. G. Kang. "Flash Design for Automatic Transfer System of Bearing Hub in Hot Forging Process." In Solid State Phenomena, 120–23. Stafa: Trans Tech Publications Ltd., 2006. http://dx.doi.org/10.4028/3-908451-26-4.120.

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Conference papers on the topic "Degradation state of a bearing"

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Clasby, Dustin, and Monique Stewart. "Bearing Grease Degradation Related to Water and Roller Bluing." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6136.

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Bearing degradation and defects can result in a premature failure. Water ingress into the bearing is a factor for premature degradation, as water may corrupt internal parts and degrade the bearing grease. This paper presents the investigation of the properties of grease degradation from bearings with water-related degradations. This research provides insight into the internal state of bearings that have been replaced due to grease degradation as a result of water ingress. Separately, the railroad industry has observed bearing roller “bluing” or “lube staining.” This discoloration may be a harmless surface effect, or it may be similar to heat bluing. Determining true metallurgical effects may lead to the understanding between these two different types of “bluing”. To study bearings with water-related lubrication degradation, grease samples were collected from two populations of bearing lubrication at bearing service locations. One population contains bearings identified with water-related damage, and a second population is a control set of bearings. Primary grease analysis was done per ASTM 7918, providing metrics of wear, contamination, consistency, and oxidative properties. Additional testing was performed where results indicated utility; including measurements of anti-oxidant remaining in grease and microscopic analysis of wear particles in the grease. “Bluing” or “lube stain” bearing components were examined through analysis of lubrication and metallurgical metrics. Collections of samples from bearing shops included representative small amounts of grease and “blued” steel parts from bearings exhibiting surface discoloration. A second sample set included steel parts and grease samples from a control set of bearings. A third set of rollers were heat blued in the lab. Lube stained rollers and control set rollers were tested for metallurgical changes. Analysis of the bearing steel consisted of hardness and micro-hardness testing of polished samples, examination to compare microstructural features, and residual stress tests. The tests conducted in the investigation of water-related bearing grease degradation indicate a difference between bearings with “Water-Etch” and “Non-Verified” degradation modes based on ferrous debris levels in the grease. This difference is due to wear of the bearing material deposited in the grease. The tests conducted in the investigation of lube stain in bearings show lube stain does not affect any tested metallurgical material properties, other than surface discoloration.
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Cheng, Yiwei, Haiping Zhu, Jun Wu, Zuoyi Chen, and Guoqiang Li. "Novel data-driven performance degradation state monitoring of rolling bearing." In 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS). IEEE, 2018. http://dx.doi.org/10.1109/usys.2018.8779034.

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Wei, Yonghe, and Minghua Wang. "The Degradation State Recognition of Rolling Bearing Based on GA and SVM." In 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering. Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/meic-14.2014.123.

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Wang, Peng, Ruqiang Yan, and Robert X. Gao. "Multi-Mode Particle Filter for Bearing Remaining Life Prediction." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6638.

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As a critical element in rotating machines, remaining useful life (RUL) prediction of rolling bearings plays an essential role in realizing predictive and preventative machine maintenance in modern manufacturing. The physics of defect (e.g. spall) initiation and propagation describes bearing’s service life as generally divided into three stages: normal operation, defect initiation, and accelerated performance degradation. The transition among the stages are embedded in the variations of monitored data, e.g., vibration. This paper presents a multi-mode particle filter (MMPF) that is aimed to: 1) automatically detect the transition among the three life stages; and 2) accurately characterize bearing performance degradation by integrating physical models with stochastic modeling method. In MMPF, a set of linear and non-linear modes (also called degradation functions) are first defined according to the physical/empirical knowledge as well as statistical analysis of the measured data (e.g. vibration). These modes are subsequently refined during the particle filtering (PF)-based bearing performance tracking process. Each mode corresponds to an individual performance scenario. A finite-state Markov chain switches among these modes, reflecting the transition between the service life stages. Case studies performed on two run-to-failure experiments indicate that the developed technique is effective in tracking the evolution of bearing performance degradation and predicting the remaining useful life of rolling bearings.
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Zhuo, M., L. H. Yang, and L. Yu. "The Steady-State Thermal Effect on Rotordynamics of a Rod-Fastened Rotor-Bearing System." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37366.

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Gas turbine rotor works at high temperature, especially the turbine used to expand hot gas and generate power. For the F class heavy duty gas turbine, the inlet temperature of hot expanding gas of the first turbine stage can be as high as 1400°C. Temperature is not only the source of thermal stress of gas turbine rotors but also leads to the variation of elastic modulus. Both of these two factors can cause a local stiffness variation of the rotor and thus have an influence on the dynamic characteristics of the rotor. The rotor investigated in this paper is a circumferential distributed rod-fastened rotor which is made of several discs bolted together. A 3-D model of the combined rotor system considering contact effect is established. The steady-state temperature field of the combined rotor is obtained through a thermal analysis. The stress distribution due to restraint on free expansions of components of the rotor as well as the deformation under steady-state thermal load is derived from a static structural analysis. Then a pre-stress modal analysis is performed to calculate the lateral natural frequencies of the rotor-bearing system. Finally, based on modal results, unbalance response of the rotor-bearing system is calculated through a harmonic analysis. For all steady-state thermal related analysis, two kinds of elastic modulus are calculated, i.e., constant and temperature-dependent elastic modulus, corresponding to the effect of thermal stress and material degradation, respectively. Both two steady-state cases are compared with the room temperature results to evaluate the role of these two thermal factors.
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Hu, Xiangzhi, and Ning Li. "Anomaly Detection and Remaining Useful Lifetime Estimation Based on Degradation State for Bearings." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9188410.

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Vogl, Gregory W., Brian C. Galfond, and N. Jordan Jameson. "Bearing Metrics for Health Monitoring of Machine Tool Linear Axes." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-2911.

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Abstract Diagnostics and prognostics of rotating machinery ball bearings is quite mature with an abundance of available methods and algorithms. However, extending these algorithms to other ball bearing applications is challenging and may not yield usable results. This work used a linear axis to study the ability of an inertial measurement unit (IMU), along with nine signal features, to measure changes in geometric error motions due to induced faults on the recirculating ball bearings of two carriage trucks. The IMU data was analyzed with the nine features used for rotating machinery systems, including root-mean-square, standard deviation, and kurtosis. For each stage of degradation, the statistical population and median value of each feature were compared against the population and median for no degradation, to monitor feature changes due to ball damage. Correlation analyses revealed an ability of the standard deviation feature to detect statistically significant changes as small as 0.05 micrometers or 0.5 microradians, corresponding to a total damaged surface area of truck balls of less than 0.1 percent.
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Kerroumi, Sanaa, Lanto Rasolofondraibe, and Xavier Chiementin. "Dynamic Classification: A Tool for Fault Detection and Vibration Behavior Monitoring of Bearings." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64250.

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The emergence of a defect in a mechanical system is always associated with a change in the vibration’s behavior in the spectral and temporal domains. Fault detection by vibration analysis is based on monitoring the behavior of a mechanical component by examining the evolution of fault indicators in real time. However, mere traditional bearing diagnosis is not sufficient to ensure effective and reliable assessment of the component’s health condition. Coupling several fault indicators extracted by different signal processing technique adds more reliability and accuracy to the diagnostic process. Classifications methods are used to analyze the evolution of fault, yet only static methods are solicited which results in overlooking a great source of useful information. In fact, fault indicators issued from turning machine are evolving; they change constantly over time, particularly when the defect is growing. In such situations, static classification methods are a poor choice that deprives the user of the information conveyed in the evolution of the indicators over time. The dynamic classification of fault indicators in dynamic classes can provide useful information about the behavior of the damaged bearings. This information can also be used to predict the end of life of the components. Unlike the static classification, the dynamic classification introduces a new dimension (time), which allows real-time detection of the fault and better visibility of the bearing behavior revealed by the motion and the temporal evolution of classes formed by the indicators. This paper proposes a dynamic classification method that uses several fault detectors to assure the accuracy of the diagnostic and follow up any changing in the behavior of the bearing by analyzing the classes’ time evolution. The proposed multi-features dynamic classification is a new method for fault detection and health condition monitoring for bearings; this technique utilizes multiple features, including traditional features extracted from the raw signal, two special features extracted by wavelet analysis, and the spectral kurtosis, coupled with a nonlinear principal component analysis. This method of classification clusters the multi-features into several classes, the first class represents the healthy state of the bearing, and the other classes represent different damaging state. Monitoring the evolution of the” defective condition” class allows us to draw several useful information, such as the rate of degradation, the relationship between the cluster’ surface and density and the bearing state. The chosen dynamic classification method will be validated by analyzing several degradation bearings from a fatigue bench of thrust ball bearings SNR51207.
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Xiao, Zhong, Donghai Zhang, Haixiao Liu, and Ying Liu. "Bearing Capacities of Shallow Skirted Foundations After the Action of Multi-Directional Cyclic Displacements Considering Soil Degradation." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-96036.

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Abstract Shallow skirted foundations have been applied widely in ocean engineering. Under the action of external excitation, the shallow skirted foundations on soft soil undergo cyclic displacements during service state. Under the action of cyclic displacements, the foundations drive the surrounding soft soil to produce a continuous accumulation of absolute plastic shear strain, which weakens the shear strength of the soft soil around the foundations. Therefore, the bearing capacities of shallow skirted foundations reduce after the action of cyclic displacements considering soil degradation. In order to study the evolutions of bearing capacities of shallow skirted foundations after the action of multi-directional cyclic displacements considering soil degradation, the elastoplastic finite element models of shallow skirted foundations with different embedment ratio are established. Cyclic displacements are applied along different displacement loading paths, and the evolutions of soil shear strength and bearing capacities of shallow skirted foundations after the action of cyclic displacements are analyzed. The results show that the soil softening zone gradually expands from the stress concentration zone of the soft soil to the surrounds with increasing number of loading cycles. Due to the enlargement and weakening of the soil softening zone, the failure envelopes of shallow skirted foundations gradually shrink, but the shrinkage trend gradually converges with increasing number of loading cycles. The shapes of the failure envelopes are relatively less affected by the cyclic number of displacements. The size of the failure envelopes is greatly affected by the loading paths while the shape of the failure envelopes is relatively less affected.
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Sneha, S. S., and K. P. Ramaswamy. "A Comprehensive Review on the Mechanism of Concrete Deterioration in Accelerated Aggressive Environment." In International Web Conference in Civil Engineering for a Sustainable Planet. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.112.40.

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Concrete is being extensively utilized for the constructional and other allied works on account of its versatility and mechanical properties. However, it exists to be in a state of disequilibrium with its ambient environment owing to its universal alkaline nature and hence is susceptible to deterioration when exposed to aggressive environments. The reactive species emanating from chemical plants, fertiliser industries, marine water, agro-food industries etc., produce detrimental effects on the concrete structures through the dissolution of calcium bearing phases from the hydrated matrix. This degradation culminates in decalcification, volumetric expansion, salt crystallisation, micro-cracking, surface scaling, delamination, spalling and corrosion. Diffusivity, capillary porosity, permeability, chemical nature of hydrated matrix and pore network are the parameters that influence the chemical mechanism of concrete degradation. The mechanism of concrete degradation is distinct for various aggressive species and its fair comprehension remains as one of the challenges in accomplishing the durability based concrete design. This paper critically reviews the basic mechanism of the concrete deterioration in accelerated aggressive environment of mineral acids, organic acids and inorganic salts. In addition to this, a glimpse of the effect of degradation on different binder systems viz., Ordinary Portland Cement system, blended cement system, special cement system and alkali activated system is provided.
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Reports on the topic "Degradation state of a bearing"

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Meike, A. State of chemical modeling modules for the degradation of concrete and cements. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/646398.

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Balasubramaniam, Krishnan. Understanding of Materials State and its Degradation using Non-Linear Ultrasound (NLU) Approaches - Phase 3. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada592196.

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Mason, H. E., P. Campbell, and A. Sawvel. Progress Summary: Solid-state NMR spectroscopic methods are being used to deduce the molecular structure of degradation products in Material 2. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1557075.

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Solaun, Kepa, Gerard Alleng, Adrián Flores, Chiquita Resomardono, Katharina Hess, and Helena Antich. State of the Climate Report: Suriname. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003398.

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Suriname is highly vulnerable to the effects of climate change. Among the factors that exacerbate its vulnerability are its dependency on fossil fuels, the degradation of important ecosystems (e.g., mangroves), and the fact that 87% of the population, and most of the countrys economic activity is located within the low-lying coastal area. Many sectors are at risk of suffering losses and damage caused by gradual changes and extreme events related to climate change. For Suriname to develop sustainably, it should incorporate climate change and its effects into its decision-making process based on scientific- evidence. The State of the Climate Report analyzes Surinames historical climate (1990-2014) and provides climate projections for three time horizons (2020-2044, 2045-2069, 2070-2094) through two emissions scenarios (intermediate/ SSP2-4.5 and severe/ SSP5-8.5). The analysis focuses on changes in sea level, temperature, precipitation, relative humidity, and winds for the seven subnational locations of Paramaribo, Albina, Bigi Pan MUMA, Brokopondo, Kwamalasamutu, Tafelberg Natural Reserve, and Upper Tapanahony. The Report also analyzes climate risk for the countrys ten districts by examining the factors which increase their exposure and vulnerability on the four most important sectors affected by climate change: infrastructure, agriculture, water, and forestry, as well as examining the effects across the sectors. The State of the Climate Report provides essential inputs for Suriname to develop and update its climate change policies and targets. These policies and targets should enable an adequate mainstreaming of climate change adaptation and resilience enhancementinto day-to-day government operations. It is expected that the Report will catalyze similar efforts in the future to improve decision-making by providing science-based evidence.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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Commonwealth Bank - Head Office cnr Pitt Street & Martin Place - Facade - Shields bearing State emblem of Western Australia (plate 653). Reserve Bank of Australia, March 2021. http://dx.doi.org/10.47688/rba_archives_pn-000936.

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Commonwealth Bank - Head Office cnr Pitt Street & Martin Place - Exterior - Shields bearing State emblem of Tasmania - c.1916 (plate 657). Reserve Bank of Australia, March 2021. http://dx.doi.org/10.47688/rba_archives_pn-000937.

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