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Journal articles on the topic 'Condition Monitoring'

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1

OBA, Takuya, Hiromasa SHIMIZU, Eiichi MORIMOTO, and Naotaka KUMAGAI. "2B14 TEMPERATURE CONDITION MONITORING FOR SHINKANSEN BOGIES(Condition Monitoring-Vehicle)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _2B14–1_—_2B14–9_. http://dx.doi.org/10.1299/jsmestech.2015._2b14-1_.

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2

Itonaga, Nobuaki, and Hidemi Yano. "354086 DEVELOPMENT OF AUTO MATIC WHEEL CONDITIONS MONITORING SYSTEM(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _354086–1_—_354086–4_. http://dx.doi.org/10.1299/jsmestech.2009._354086-1_.

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3

Tsunashima, Hitoshi, Takashi Kojima, Yoshitaka Marumo, Akira Matsumoto, and Takeshi Mizuma. "345303 CONDITION MONITORING OF RAILWAY TRACK USING IN-SERVICE VEHICLE (Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _345303–1_—_345303–6_. http://dx.doi.org/10.1299/jsmestech.2009._345303-1_.

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4

Oba, Takuya, Koichi Yamada, Hitoshi Soma, and Katsuya Tanifuji. "356776 CONDITION MONITORING FOR SHINKANSEN BOGIES BASED ON VIBRATION ANALYSIS(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _356776–1_—_356776–6_. http://dx.doi.org/10.1299/jsmestech.2009._356776-1_.

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5

Naganuma, Yasukuni, Mikihito Kobayashi, and Takayuki Okumura. "355005 INERTIAL MEASUREMENT PROCESSING TECHNIQUES FOR TRACK CONDITION MONITORING ON SHINKANSEN COMMERCIAL TRAINS(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _355005–1_—_355005–6_. http://dx.doi.org/10.1299/jsmestech.2009._355005-1_.

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6

Hunt, T. M. "Condition monitoring ′91 — The international conference on condition monitoring." Tribology International 24, no. 6 (December 1991): 409–10. http://dx.doi.org/10.1016/0301-679x(91)90014-z.

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7

Badran, Rana N., Ihab Adly, and Hani Ghali. "Self-Sufficient Wind Turbine Condition Monitoring System." Journal of Clean Energy Technologies 6, no. 2 (March 2018): 112–16. http://dx.doi.org/10.18178/jocet.2018.6.2.444.

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8

NAKAMURA, Kazuki, Kunihiro KAWASAKI, Nagateru IWASAWA, Daisuke YAMAGUCHI, and Keiichi TAKEUCHI. "3F25 The Monitoring System Using 90GHz Band for Railway(Condition Monitoring-Infrastructure)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _3F25–1_—_3F25–7_. http://dx.doi.org/10.1299/jsmestech.2015._3f25-1_.

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9

Smith, J. R. "Plant condition monitoring." IEE Proceedings B Electric Power Applications 133, no. 3 (1986): 141. http://dx.doi.org/10.1049/ip-b.1986.0018.

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10

Finch, Robert D. "Acoustic condition monitoring." Journal of the Acoustical Society of America 104, no. 3 (September 1998): 1789. http://dx.doi.org/10.1121/1.423515.

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11

Schenck Ltd. "Integrated condition monitoring." NDT & E International 24, no. 6 (December 1991): 332–33. http://dx.doi.org/10.1016/0963-8695(91)90124-l.

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12

Cempel, C. "Vibroacoustic condition monitoring." NDT & E International 27, no. 4 (January 1994): 211. http://dx.doi.org/10.1016/0963-8695(94)90467-7.

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13

AEA Sonomatic. "Condition monitoring system." NDT & E International 27, no. 3 (June 1994): 175. http://dx.doi.org/10.1016/0963-8695(94)90754-4.

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14

YOSHINAGA, Jun, and Takeshi MIZUMA. "361235 DRIVER'S POSTURE MONITORING BY INFRARED LASER RANGING SENSOR(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _361235–1_—_361235–5_. http://dx.doi.org/10.1299/jsmestech.2009._361235-1_.

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15

Sugama, Yoji, Toshihide Kishi, and Nobuyuki Ishioka. "1B31 Development of an Overhead Contact Line Equipment Monitoring System(Condition Monitoring-Vehicle)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _1B31–1_—_1B31–9_. http://dx.doi.org/10.1299/jsmestech.2015._1b31-1_.

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16

Ali, Emad. "Condition Monitoring of Wear Progress in Hydrostatic Pumps." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 139–42. http://dx.doi.org/10.31142/ijtsrd18407.

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17

KUMAKURA, R., Y. KAKINUMA, T. ARAI, E. UCHISHIBA, M. MURAKAMI, T. SAGARA, and T. AOYAMA. "D004 Sensorless tool condition monitoring in buffing processes." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2013.7 (2013): 463–68. http://dx.doi.org/10.1299/jsmelem.2013.7.463.

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18

Liu, X., V. L. Markine, H. Wang, and I. Y. Shevtsov. "Experimental tools for railway crossing condition monitoring (crossing condition monitoring tools)." Measurement 129 (December 2018): 424–35. http://dx.doi.org/10.1016/j.measurement.2018.07.062.

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19

GOLOVIN, V. I., and S. Yu RADCHENKO. "TOOL CONDITION MONITORING SYSTEM IN SERIAL PRODUCTION CONDITIONS." Fundamental and Applied Problems of Engineering and Technology 4, no. 2 (2020): 161–68. http://dx.doi.org/10.33979/2073-7408-2020-342-4-2-161-168.

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One of the most important tasks of serial and mass production is to maintain the continuity of the technological process in order to reduce equipment downtime and, as a result, the cost of production. One of the systems is the tool condition monitoring system. However, the solutions used today are complex software and hardware systems that are not available for most medium and small productions. The article proposes a system based on a comparative analysis of the applied tool with reference instances. The results of the analysis are sent to the decision-making system, which determines the feasibility of further use of the cutting tool for subsequent machining. An example of an experimental study of milling processing is given. The results obtained show the possibility and rationality of using this model to predict the state of the instrument.
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20

Florkowski, Marek, and Jian Li. "Condition Monitoring and Diagnostics." IEEE Transactions on Dielectrics and Electrical Insulation 27, no. 6 (December 2020): 1769. http://dx.doi.org/10.1109/tdei.2020.009340.

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21

Horváth, L., and B. Szabó. "Tool Condition Monitoring System." IFAC Proceedings Volumes 19, no. 13 (November 1986): 291–95. http://dx.doi.org/10.1016/s1474-6670(17)59556-7.

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22

Wong, W. "Condition Monitoring Based Maintenance." IFAC Proceedings Volumes 19, no. 1 (January 1986): 91. http://dx.doi.org/10.1016/s1474-6670(17)59870-5.

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23

Stresswave Technology Ltd. "Stress wave condition monitoring." NDT & E International 23, no. 4 (August 1990): 240. http://dx.doi.org/10.1016/0963-8695(90)90978-r.

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24

Institut Dr Förster. "Integrated condition monitoring system." NDT & E International 24, no. 5 (October 1991): 275. http://dx.doi.org/10.1016/0963-8695(91)90501-s.

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25

Barclay, J., and R. Bannach. "Multi-parameter condition monitoring." NDT & E International 27, no. 4 (January 1994): 213. http://dx.doi.org/10.1016/0963-8695(94)90498-7.

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26

Green, L. "Condition monitoring by thermography." NDT & E International 27, no. 4 (January 1994): 217. http://dx.doi.org/10.1016/0963-8695(94)90538-x.

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27

ERA Technology Ltd. "Cable insulation condition monitoring." NDT & E International 27, no. 3 (June 1994): 170. http://dx.doi.org/10.1016/0963-8695(94)90744-7.

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28

Stresswave Technology Ltd. "Stress wave condition monitoring." NDT International 23, no. 4 (August 1990): 240. http://dx.doi.org/10.1016/0308-9126(90)91714-5.

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29

Ono, Shigetoshi. "ICONE23-1169 ADVANCED CONDITION MONITORING PROGRAM FOR TURBINE SYSTEM." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_92.

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30

Jenab, K., and K. Rashidi. "Fuzzy Bayesian Condition Monitoring Model based on Exponential Distribution." International Journal of Engineering and Technology 1, no. 2 (2009): 172–78. http://dx.doi.org/10.7763/ijet.2009.v1.32.

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31

Mori, Hirotaka, Yasuhiro Sato, Hiroyuki Ohno, Masayuki Ogino, Hitoshi Tsunashima, and Akira Asano. "2B23 Condition diagnosis of railway tracks by a compact size onboard device(Condition Monitoring-Infrastructure)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _2B23–1_—_2B23–6_. http://dx.doi.org/10.1299/jsmestech.2015._2b23-1_.

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32

MATSUMURA, KENTARO, and MAKOTO KIRIYA. "3F12 Condition-based maintenance of the ticket-issuing and ticket gate equipment(Condition Monitoring-Infrastructure)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _3F12–1_—_3F12–9_. http://dx.doi.org/10.1299/jsmestech.2015._3f12-1_.

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33

Pozo, Francesc, Diego A. Tibaduiza, and Yolanda Vidal. "Sensors for Structural Health Monitoring and Condition Monitoring." Sensors 21, no. 5 (February 24, 2021): 1558. http://dx.doi.org/10.3390/s21051558.

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Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables in mechanical, chemical, electrical, aeronautical, civil, electronics, mechatronics, and agricultural engineering applications, among others [...]
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34

Chittora, Manoj Kumar. "Condition Monitoring of Turbogenerator by Vibration Monitoring Technique." IOSR Journal of Engineering 3, no. 01 (January 2013): 33–37. http://dx.doi.org/10.9790/3021-03113337.

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35

Deng, Yanqing, Bingchang Hou, Yikai Chen, and Dong Wang. "Nonparametric nonlinear profile monitoring method for machine condition monitoring." Journal of Physics: Conference Series 2369, no. 1 (November 1, 2022): 012096. http://dx.doi.org/10.1088/1742-6596/2369/1/012096.

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Machine condition monitoring (MCM) aims to evaluate machine health conditions by statistically analysing machine condition data. Health indicators (HIs) such as Gini index, negative entropy, spectral kurtosis et al. have been commonly applied in MCM. But these HIs lack a statistical threshold and monotonic degradation tendency. To monitor the health condition of rotating machines, a nonparametric nonlinear profile monitoring method for MCM is proposed in this paper. And a Hotelling T-square health indicator (HTHI) with a statistical threshold is constructed with B-spline modeling and Hotelling T-square statistic. Experimental validation on a public bearing dataset shows that the constructed HTHI can effectively detect early machine faults and monotonically evaluate degradation trends, which enriches methods of MCM domain.
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36

KEYAKI, Takenori, Osamu SUZUKI, and Shoichi UEMURA. "2B13 Development of a new scour detector that enables monitoring of pier soundness (Condition Monitoring-Vehicle)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _2B13–1_—_2B13–9_. http://dx.doi.org/10.1299/jsmestech.2015._2b13-1_.

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37

OGINO, Masayuki, Hitoshi TSUNASHIMA, Kazuki YANAGISAWA, Hirotaka MORI, and Akira ASANO. "2P25 Development of track condition monitoring system using in-service vehicle and monitoring examples(Shotgun Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _2P25–1_—_2P25–11_. http://dx.doi.org/10.1299/jsmestech.2015._2p25-1_.

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38

Káš, M., and F. F. Wamba. "Anomaly detection-based condition monitoring." Insight - Non-Destructive Testing and Condition Monitoring 64, no. 8 (August 1, 2022): 453–58. http://dx.doi.org/10.1784/insi.2022.64.8.453.

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The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imply an intrusion attack. Other objectives of anomaly detection are industrial damage detection, data leak prevention, identifying security vulnerabilities or military surveillance. Anomalies are observations or a sequence of observations in which the distribution deviates remarkably from the general distribution of the whole dataset. A large majority of the dataset consists of normal (healthy) data points. The anomalies form only a very small part of the dataset. Anomaly detection is the technique used to find these observations and its methods are specific to the type of data. While there is a wide spectrum of anomaly detection approaches available today, it becomes increasingly difficult to keep track of all the techniques. In fact, it is not clear which of the three categories of detection methods, ie statistical approaches, machine learning approaches or deep learning approaches, is more appropriate to detect anomalies in time-series data, which are mainly used in industry. A typical industrial device has multi-dimensional characteristics. It is possible to measure voltage, current, active power, vibrations, rotational speed, temperature, pressure difference, etc, on such a device. Early detection of the anomalous behaviour of industrial devices can help reduce or prevent serious damage, which could lead to significant financial loss. This paper presents a summary of the methods used to detect anomalies in condition monitoring applications.
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39

Lees, A. W. "Where Next for Condition Monitoring?" Key Engineering Materials 245-246 (July 2003): 203–14. http://dx.doi.org/10.4028/www.scientific.net/kem.245-246.203.

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40

Orlova, Svetlana Sergeevna, Tatiana Anatolievna Pankova, Olga Valentinovna Mikheeva, and Elena Nikolaevna Mirkina. "Monitoring condition of hydrotechnical structures." Agrarian Scientific Journal, no. 9 (September 28, 2021): 98–103. http://dx.doi.org/10.28983/asj.y2021i9pp98-103.

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Monitoring condition of hydrotechnical structures conducted to ensure the management of the rational and safe operation of the waterworks. Based on the results of regular interconnected control observations of the state of the GTS, a database of observations is compiled. On its basis, analysis and evaluation of the forecast of the development of negative processes at the facility and development of recommendations for their overcoming and elimination of identified violations are carried out; prevention of emergency situations; creation of conditions for safe operation. Monitoring safety GTS carried out on the example of hydrotechnical structures of the blocking regulatory structure of the Aleksandrovogai reservoir in the Saratov region. The main quantitative and qualitative indicators and their criteria values are given. It is recommended that during operation, visual observations be carried out daily, instrumental observations - at least 1 time in 3 years, in addition to this, at least once every two weeks to inspect the hydraulic structures with the results recorded in the appropriate journal. Systematic visual observations should be supplemented by pre-flood and post-flood inspections of structures, as well as unscheduled inspections carried out after extreme events accompanied by damage to structures. Particular attention should be paid to observing the filtration regime in the body and base of the dam, the state of fastening of the inlet and outlet heads, closures and pipelines of the outlet structure.
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41

Lee, Kh Kim Hung, and Dennis Kong Wan Chau. "Condition Based Monitoring in Railway." Journal of International Council on Electrical Engineering 2, no. 1 (January 2012): 99–103. http://dx.doi.org/10.5370/jicee.2012.2.1.099.

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42

Kuzin, Evgeny, Vladimir Bakin, and Dmitriy Dubinkin. "Mining Equipment Technical Condition Monitoring." E3S Web of Conferences 41 (2018): 03020. http://dx.doi.org/10.1051/e3sconf/20184103020.

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The Earth, being the main object and operational basis for mining, is exposed to the greatest impact because of extracting minerals. Protection of elements of the biosphere, including subsoil, should provide for the provision of scientifically based and economically justified completeness and complexity of use. The article discusses the need to monitor the technical condition of mining equipment, as applied to assessing its technical condition and reducing energy consumption by this equipment. The dependence of energy consumption on vibration parameters and temperature of equipment surfaces is shown. The data of the results of vibration parameters monitoring are given. Criteria are given for estimating the energy efficiency of operation of process equipment and, accordingly, the influence of these parameters on the environment.
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43

Zulkarnaen, Hendra, Syafruddin Hasan, and Suherman. "Condition Monitoring of Distribution Transformers." IOP Conference Series: Materials Science and Engineering 420 (October 1, 2018): 012049. http://dx.doi.org/10.1088/1757-899x/420/1/012049.

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44

HARRIS, C. G., J. H. WILLIAMS, and A. DAVIES. "Condition monitoring of machine tools." International Journal of Production Research 27, no. 9 (September 1989): 1445–64. http://dx.doi.org/10.1080/00207548908942633.

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45

Jemielniak, Krzysztof. "Tool and process condition monitoring." Mechanik 90, no. 7 (July 10, 2017): 504–10. http://dx.doi.org/10.17814/mechanik.2017.7.64.

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Automatic tool condition monitoring is based on the measurements of physical phenomena which are correlated with this condition. There are numerous signal features (SFs) that can be extracted from the signal. As it is really not possible to predict which signal features will be useful in a particular case they should be automatically selected and combined into one tool condition estimation. This can be achieved by various artificial intelligence methods.
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46

Linton, Michael A., and Liping Fu. "Winter Road Surface Condition Monitoring." Transportation Research Record: Journal of the Transportation Research Board 2482, no. 1 (January 2015): 46–56. http://dx.doi.org/10.3141/2482-07.

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47

Basak, A. "Condition monitoring of power transformers." Engineering Science & Education Journal 8, no. 1 (February 1, 1999): 41–46. http://dx.doi.org/10.1049/esej:19990107.

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48

Penman, J., M. N. Dey, A. J. Tait, and W. E. Bryan. "Condition monitoring of electrical drives." IEE Proceedings B Electric Power Applications 133, no. 3 (1986): 142. http://dx.doi.org/10.1049/ip-b.1986.0019.

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49

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

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50

Florkowski, M., and S. Gubanski. "Condition monitoring and diagnostics [Editorial]." IEEE Transactions on Dielectrics and Electrical Insulation 20, no. 6 (December 2013): 1961. http://dx.doi.org/10.1109/tdei.2013.6678841.

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