Thematische Bibliographien / Condition monitoring of a bearing

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Auswahl der wissenschaftlichen Literatur zum Thema „Condition monitoring of a bearing“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Condition monitoring of a bearing"

Entezami, Mani, Clive Roberts, Paul Weston, Edward Stewart, Arash Amini, and Mayorkinos Papaelias. "Perspectives on railway axle bearing condition monitoring." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 1 (2019): 17–31. http://dx.doi.org/10.1177/0954409719831822.

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Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems a

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Lin, Hui Bin, and Kang Ding. "Rolling Element Bearing Condition Monitoring and Diagnosis." Applied Mechanics and Materials 34-35 (October 2010): 332–37. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.332.

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Bearing failure is one of the foremost causes of breakdown in rotating machinery. To date, Envelope detection is always used to identify faults occurring at the Bearing Characteristic Frequencies (BCF). However, because the impact vibration generated by a bearing fault has relatively low energy, it is often overwhelmed by background noise and difficult to identify. Combined the results of extensive experiments performed in a series of bearings with artificial damage, this research investigates the effect of many influencing factors, such as demodulation methods, sampling frequency, variable ma

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Hohmann, M., E. Blechschmidt, and O. Menck. "Prediction of the friction torque of scaled blade bearings in a test rig using machine learning." Journal of Physics: Conference Series 2767, no. 5 (2024): 052010. http://dx.doi.org/10.1088/1742-6596/2767/5/052010.

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Abstract Blade bearing friction torque is a required parameter for the design of a pitch actuator, and deviations from a bearing’s initial torque can be utilized for condition monitoring of the bearing. The torque of large-scale bearings can, however, be difficult to predict due to quality fluctuations in the production of these large-scale components. Therefore, this paper employs machine learning approaches to predict the torque of a given set of bearings in a controlled test environment based on measurement data from that same set of bearings. Possible applications of the model include use

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Gouws, R. "Active magnetic bearing condition monitoring." World Journal of Engineering 10, no. 2 (2013): 179–88. http://dx.doi.org/10.1260/1708-5284.10.2.179.

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Bai, Xiu Qin, Han Liang Xiao, and Lu Zhang. "The Condition Monitoring of Large Slewing Bearing Based on Oil Analysis Method." Key Engineering Materials 474-476 (April 2011): 716–19. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.716.

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Large slewing bearing is a special kind of rolling bearing with heavy load and very low rotation speed. It is important to carry out faults monitoring on this kind of rolling bearing. However, it is difficult to carry out vibration monitoring on such large slewing bearing. The running conditions of slewing bearings of ship loader and stacking crane in Qinghuangdao Port were analyzed using ferrography and spectrometric analysis technology. Monitoring results showed that the slewing bearing of SL-Q1 ship loader was under abnormal wear condition. Further inspection indicated that the rolling elem

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Wu, Zian, Wenxian Yang, Xiaoping Song, and Kexiang Wei. "Study on Condition Monitoring of Pitch Bearings Based on Stress Measurement." Machines 12, no. 3 (2024): 154. http://dx.doi.org/10.3390/machines12030154.

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Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This re

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Singh, R. C., R. K. Pandey, Rajiv Chaudhary, and Ranganath M. S. "Analysis of Ball Bearings under Dynamic Loading Using Non- Destructive Technique of Thermography." International Journal of Advance Research and Innovation 2, no. 4 (2014): 118–21. http://dx.doi.org/10.51976/ijari.241417.

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Roller bearing defect is a major factor of failure in rotating machinery that affects proper functioning of system which results in substantial time and economic losses. Therefore, condition monitoring of roller bearing is important and the study of severity of defects are necessarily required in order to avoid catastrophic consequences. Defects monitoring during dynamic loading conditions of rotational machineries with the use of contactless, non-destructive infrared thermographic method is proposed. By using a rotating ball bearing, passive thermographic experiment was performed as an altern

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Sandoval, Diego, Urko Leturiondo, Yolanda Vidal, and Francesc Pozo. "Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis." Sensors 21, no. 3 (2021): 849. http://dx.doi.org/10.3390/s21030849.

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration an

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Irfan, Muhammad, Faisal Althobiani, Abdullah Saeed Alwadie, et al. "Condition monitoring of water pump bearings using ensemble classifier." Advances in Mechanical Engineering 14, no. 3 (2022): 168781322210891. http://dx.doi.org/10.1177/16878132221089170.

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% throug

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Patel, R. K., and V. K. Giri. "Condition monitoring of induction motor bearing based on bearing damage index." Archives of Electrical Engineering 66, no. 1 (2017): 105–19. http://dx.doi.org/10.1515/aee-2017-0008.

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Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become

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Dissertationen zum Thema "Condition monitoring of a bearing"

Moussa, Wael. "Thermography-Assisted Bearing Condition Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31379.

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Abstract Despite the large amount of research work in condition based maintenance and condition monitoring methods, there is still a need for more reliable and accurate methods. The clear evidence of that need is the continued dependence on time based maintenance, especially for critical applications such as turbomachinery and airplane engines. The lack of accurate condition monitoring systems could lead to not only the unexpected failures as well as the resulting hazards and repair costs, but also a huge waste of material and time because of unnecessary replacement due to false alarms and unn

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Chen, Ping. "Bearing condition monitoring and fault diagnosis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq64993.pdf.

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Gouws, Rupert. "Condition monitoring of active magnetic bearing systems / R. Gouws." Thesis, North-West University, 2007. http://hdl.handle.net/10394/1305.

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Johnston, Andrew Beaton. "Condition monitoring of reciprocating compressors and rolling element bearings." Thesis, University of Aberdeen, 1985. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU365562.

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The prefailure detection of faults in operating plant can effect major rewards in both safety and economy. A successful on-condition maintenance philosophy would pay great dividends particularly in the offshore oil industry where -until recently, only token methods have been employed. Many techniques are available for monitoring mechanical plant and several of these are considered in general terms. Industrial methods are subsequently evaluated on reciprocating compressor and rolling element bearing faults. Bearing fault analysis is considered in two stages. Initially, a series of vibration bas

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Feng, Yanhui. "Novel acoustic emission signal processing methods for bearing condition monitoring." Thesis, University of Leicester, 2008. http://hdl.handle.net/2381/8613.

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Rolling Element Bearing is one of the most common mechanical components to be found in critical industrial rotating machinery. Since the failure of bearings will cause the machine to malfunction and may quickly lead to catastrophic failure of the machinery, it is very important to detect any bearing deterioration at an early stage. In this thesis, novel signal processing methods based on Acoustic Emission measurement are developed for bearing condition monitoring. The effectiveness of the proposed methods is experimentally demonstrated to detect and diagnose localised defects and incipient fau

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Gómez, Adrián (Gomez ́Velázquez) 1977. "Condition monitoring of bearing damage : test implementation and data acquisition." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89288.

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Chen, Su Liang. "Development of automated bearing condition monitoring using artificial intelligence techniques." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/195557/.

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A recent series of tapered roller bearing tests have been conducted at the University of Southampton to evaluate the effectiveness of using multiple sensing technologies to detect incipient faults. The test rig was instrumented with on-line sensors including vibration, temperature and electrostatic wear and oil-line debris sensors. Off-line techniques were also used such as debris analysis and bearing surface examination. The electrostatic sensors, in particular, have the potential to detect early decay of tribological contacts within rolling element bearings. These sensors have the unique abi

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Billington, Scott Alexander. "Sensor and machine condition effects in roller bearing diagnostics." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/17796.

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Soltani, Bozchalooi Iman. "Bearing vibration and oil debris signal enhancement for machinery condition monitoring." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27486.

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Vibration signal and lubricant oil condition are two major sources of information for machine health condition monitoring. Though vibration signal is an indirect indicator of machine conditions, it contains very rich information. On the other hand, the lubricating oil analysis provides a direct indicator of machine health conditions. The joint use of the two sources of information would compensate for their limitations and thus better maintenance actions can be expected. However, this alone is not sufficient since the two sources are often severely contaminated by background and machine interf

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Kaewkongka, Tonphong. "Bearing condition monitoring using acoustic emission and vibration : the systems approach." Thesis, Brunel University, 2002. http://bura.brunel.ac.uk/handle/2438/7862.

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This thesis proposes a bearing condition monitoring system using acceleration and acoustic emission (AE) signals. Bearings are perhaps the most omnipresent machine elements and their condition is often critical to the success of an operation or process. Consequently, there is a great need for a timely knowledge of the health status of bearings. Generally, bearing monitoring is the prediction of the component's health or status based on signal detection, processing and classification in order to identify the causes of the problem. As the monitoring system uses both acceleration and acoustic emi

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Bücher zum Thema "Condition monitoring of a bearing"

Condition Monitoring and Preventative Maintenance Conference (1989 Atlanta, Ga.). Condition Monitoring and Preventative Maintenance proceedings: Presented at the 44th Annual Meeting of the Society of Tribologists and Lubrication Engineers, May 1 - May 4, 1989, Atlanta, Georgia. Society of Tribologists and Lubrication Engineers, 1989.

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Rao, B. K. N., ed. Profitable Condition Monitoring. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1616-9.

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Rao, B. K. N. Profitable Condition Monitoring. Springer Netherlands, 1993.

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Cempel, C. Vibroacoustic condition monitoring. Ellis Horwood, 1991.

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Cempel, Czesław. Vibroacoustic condition monitoring. Edited by Haddad S. D. 1939-. Ellis Horwood, 1991.

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N, Rao B. K., and BHR Group Limited, eds. Profitable condition monitoring. Kluwer Academic Publishers, 1993.

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Randall, Robert Bond. Vibration-based Condition Monitoring. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470977668.

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Verma, Nishchal K., and Al Salour. Intelligent Condition Based Monitoring. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0512-6.

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Davies, A., ed. Handbook of Condition Monitoring. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4924-2.

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N, Rao B. K., ed. Handbook of condition monitoring. Elsevier Advanced Technology, 1996.

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Buchteile zum Thema "Condition monitoring of a bearing"

Hannon, William M. "Rolling Bearing Condition Monitoring." In Encyclopedia of Tribology. Springer US, 2013. http://dx.doi.org/10.1007/978-0-387-92897-5_385.

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Mahgoun, Hafida, and Ridha Ziani. "Bearing Diagnostics Using Time-Frequency Filtering and EEMD." In Applied Condition Monitoring. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96181-1_4.

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Farhat, Mohamed Habib, Xavier Chiementin, Fakher Chaari, Fabrice Bolaers, and Mohamed Haddar. "Time Synchronous Averaging Based Detection of Bearing Defects." In Applied Condition Monitoring. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_23.

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Cocconcelli, Marco, Jacopo Cavalaglio Camargo Molano, Riccardo Rubini, Luca Capelli, and Davide Borghi. "Bearing Fault Model for an Independent Cart Conveyor." In Applied Condition Monitoring. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_22.

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Mabrouk, Abdelileh, Olfa Ksentini, Nabih Feki, Mohamed Slim Abbes, and Mohamed Haddar. "Optimal Linear Quadratic Stabilization of a Magnetic Bearing System." In Applied Condition Monitoring. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76517-0_17.

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Mauricio, Alexadre, Wade Smith, Junyu Qi, Robert Randall, and Konstantinos Gryllias. "Cyclo-non-stationary Based Bearing Diagnostics of Planetary Gearboxes." In Applied Condition Monitoring. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_35.

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Sow, Souleymane, Xavier Chiementin, Lanto Rasolofondraibe, and Olivier Cousinard. "Ball Bearing Diagnosis Using Data Hybridisation in Supervised Machine Learning." In Applied Condition Monitoring. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34190-8_19.

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Younes, Ramdane, Nouredine Ouelaa, Nacer Hamzaoui, and Abderrazek Djebala. "Experimental Study of Combined Gear and Bearing Faults by Sound Perception." In Applied Condition Monitoring. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41459-1_14.

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Benali, Salwa, Anoire Benjdidia, Taissir Hentati, Slim Bouaziz, and Mohamed Haddar. "Application of Particle Swarm Optimization to Minimize Active Magnetic Bearing Forces." In Applied Condition Monitoring. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34190-8_40.

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Fourati, Aroua, Adeline Bourdon, Didier Rémond, Nabih Feki, Fakher Chaari, and Mohamed Haddar. "Current Signal Analysis of an Induction Machine with a Defective Rolling Bearing." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_5.

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Konferenzberichte zum Thema "Condition monitoring of a bearing"

Coats, David, Mohammed Hassan, and Abdel Bayoumi. "Condition Monitoring of Helicopter Drivetrain Components Using Bispectral Analysis." In Vertical Flight Society 70th Annual Forum & Technology Display. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9529.

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In this paper, bispectral analysis of vibration signals is used to assess health conditions of different rotating-components in an AH-64D helicopter tail rotor drive train. First, cross-bispectral analysis is used to investigate drive-shaft faulted conditions -- namely misalignment, imbalance, and a combination of misalignment and imbalance -- with respect to a baseline case. The magnitude of the cross-bispectrum shows high sensitivity to abnormalities in the drive shaft, and phase information can be used to distinguish between different shaft conditions. Auto-bispectral analysis is used to st

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Lehmann, Michel, and Andreas Möckel. "Comprehension and Limitations of Different Types of Data-Driven Condition Monitoring Systems for Electrical Machines in the Context of Bearing Condition Monitoring." In 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). IEEE, 2025. https://doi.org/10.1109/wemdcd61816.2025.11014132.

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Petty, D. J. "Analysis of bearing vibration monitoring." In IEE Colloquium Understanding Your Condition Monitoring. IEE, 1999. http://dx.doi.org/10.1049/ic:19990654.

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Sadoughi, Alireza, and Hamidreza Behbahanifard. "A practical bearing fault diagnoser." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580251.

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Taylor, Barry. "Real-Time Monitoring of Bearing Condition." In ASME 1999 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/99-gt-307.

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Magnetic chip detectors, vibration monitoring devices, and spectrographic oil analysis typically do not detect bearing distress until the bearing is in the latter stages of failure. Now, through the innovation of digital signal processing technology and a breakthrough in inductive sensors, it is possible to provide several months of advance notice on a bearing failure. Unlike magnetic chip detectors, this technology has the capability to track the shedding of both magnetic and nonmagnetic debris from the bearing. It is a true prognostic sensor that detects the first indications of a bearing sp

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Gunasekaran, Santhosh, Shrinathan Esakimuthu Pandarakone, Keisuke Asano, Yukio Mizuno, and Hisahide Nakamura. "Condition Monitoring and Diagnosis of Outer Raceway Bearing Fault using Support Vector Machine." In 2018 Condition Monitoring and Diagnosis (CMD). IEEE, 2018. http://dx.doi.org/10.1109/cmd.2018.8535744.

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Snell, O. D., and I. Nairne. "Acoustic bearing monitoring - the future RCM 2008." In 4th IET International Conference on Railway Condition Monitoring (RCM 2008). IEE, 2008. http://dx.doi.org/10.1049/ic:20080340.

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Sharma, Anju, and VPS Naidu. "Bearing Health Condition Monitoring- A Brief Exposition." In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C). IEEE, 2022. http://dx.doi.org/10.1109/i4c57141.2022.10057758.

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Wang, Changting, and Robert X. Gao. "Sensor Module for Integrated Bearing Condition Monitoring." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0098.

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Abstract To obtain bearing fault signals with high signal-to-noise ratio, an appropriate sensor module needs to be designed and implemented that can be placed in the vicinity of the bearing to be monitored. This paper presents several design issues related to the sensor module, the build-in electronics, and the scheme of wireless signal communication. Advantages of using an integrated sensor over an external sensor for acquiring bearing signals are presented. Experimental studies have been conducted under various loading and speed conditions, using a commercially available ball bearing that ha

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Kypuros, Javier A., Constantine M. Tarawneh, Andoni Zagouris, Sean Woods, Brent M. Wilson, and Andrew Martin. "Implementation of Wireless Temperature Sensors for Continuous Condition Monitoring of Railroad Bearings." In ASME 2011 Rail Transportation Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/rtdf2011-67017.

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At present there are no existing bearing health monitoring systems capable of continuous monitoring and tracking of railroad bearings on freight cars. Current wayside equipment is used to garner intermittent bearing cup temperatures, which at times could be every 65 km (∼40 mi) or more. Such devices are not designed to provide continuous condition monitoring which would enable users to assess the rate of bearing health degradation and predict when a bearing will require service. To this end, IONX, LLC, a subsidiary of Amsted Rail, Inc., has developed low power Wireless Sensor Nodes (WSNs) whic

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Berichte der Organisationen zum Thema "Condition monitoring of a bearing"

Hawley. PR-015-11707-R01 Test Diagnostic Methods for Turbine Gas Meters. Pipeline Research Council International, Inc. (PRCI), 2013. http://dx.doi.org/10.55274/r0010671.

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Similar to most metering technologies, turbine meters are known to be affected by abnormal flow or abnormal mechanical conditions which can cause bias in flow measurement. These types of flow conditions include blockage at the flow meter or straightening vanes, grime or liquid contamination on the internal meter components, damage to the internal meter components, and pulsation in the flow. With the introduction of ultrasonic and Coriolis meters for gas applications, the natural gas industry has embraced the concept of meters with embedded diagnostic capabilities. These capabilities allow the

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Ward, Kimiora. Sierra Nevada Network white pine monitoring: 2022 annual report. National Park Service, 2023. http://dx.doi.org/10.36967/2301003.

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Five-needle white pines (Family Pinaceae, Genus Pinus, Subgenus Strobus), and in particular whitebark pine (Pinus albicaulis), limber pine (P. flexilis), and foxtail pine (P. balfouriana) are foundation species in upper subalpine and treeline forests of several National Park Service Pacific West Region parks, including Sequoia and Kings Canyon National Parks (SEKI) and Yosemite National Park (YOSE). The Sierra Nevada Network Inventory & Monitoring Program, in collaboration with the Klamath Network, Upper Columbia Basin Network, and Mojave Desert Network have implemented a joint long-term m

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Villaran, M., R. Lofaro, and na. Condition Monitoring of Cables Task 3 Report: Condition Monitoring Techniques for Electric Cables. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1013436.

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Green, Andre Walter. Navy Condition-Based Monitoring Project Updates. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1634947.

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Gomez-Leon, Steve, and Anthony J. Smalley. TR-97-7 Main Bearing Temperature Monitoring. Pipeline Research Council International, Inc. (PRCI), 1998. http://dx.doi.org/10.55274/r0011736.

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Evaluates methods to monitor main bearing temperatures in reciprocating compressor units including industry practice, typical data, analysis of data characteristics and variability, identification of requirements for a monitoring strategy, alternative monitoring approaches, performance simulation for these alternatives, and the synthesis of a preferred approach, with factors to consider in selecting limits or alternatives to the preferred approach. It includes recommendations for the gathering of data from station logging systems which will help guide and refine monitoring decisions.

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Ward, Kimiora. Sierra Nevada Network high elevation white pine monitoring: 2021 annual report. National Park Service, 2024. http://dx.doi.org/10.36967/2302327.

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Ulerich, Nancy, Getnet Kidane, Christine Spiegelberg, and Nikolai Tevs. Condition Based Monitoring of Gas Turbine Combustion Components. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1117202.

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Green, Andre Walter. Navy Condition Based Monitoring Project Update: Requested Materials. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1634945.

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Sundararajan, Visvanatha. Laboratory Equipment for Machinery Condition Monitoring and Diagnostics. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416046.

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Huang, Haiying, Ankur Jain, Jian Luo, Franck Mbanya Tchafa, Jun Yao, and Jiuyuan Nie. Distributed Wireless Antenna Sensors for Boiler Condition Monitoring. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1503678.

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