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Academic Journal

A condition monitoring system for an early warning of developing faults in wind turbine electrical systems.

Authors : Xiandong Ma¹ ; Cross, P.² ; Peng Qian³

Source: Insight: Non-Destructive Testing & Condition Monitoring. Dec2016, Vol. 58 Issue 12, p663-670. 8p. 1 Color Photograph, 3

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Academic Journal

Non-destructive online condition monitoring and trend prediction of lubricating oil in a steam turbine.

Authors : Xiaojian Xu^1,2 ; Xinping Yan³ ; Chenxing Sheng^2,4

Source: Insight: Non-Destructive Testing & Condition Monitoring. Dec2016, Vol. 58 Issue 12, p649-655. 7p. 3 Diagrams, 2 Charts, 5

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Academic Journal

A validation study of ACS-SSI for online condition monitoring of vehicle suspension systems.

Authors : Fulong Liu¹ ; Fengshou Gu¹ ; Yunshi Zhao²

Source: Vibroengineering Procedia. Dec2016, Vol. 10, p369-375. 7p.

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Academic Journal

Latest lessons learned, modern condition monitoring and advanced predictive maintenance for gas turbines.

Authors : Almasi, Amin¹ (AUTHOR)

Source: Australian Journal of Mechanical Engineering. Oct2016, Vol. 14 Issue 3, p199-211. 13p.

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Academic Journal

Tidal stream generators, current state and potential opportunities for condition monitoring.

Authors : Kappatos, Vassilios¹ ; Georgoulas, George² ; Avdelidis, Nicolas P.³

Source: Vibroengineering Procedia. Oct2016, Vol. 8, p285-293. 9p.

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Academic Journal

A Non-Invasive Method for Condition Monitoring of Induction Motors Operating Under Arbitrary Loading Conditions.

Authors : Irfan, Muhammad¹ ; Saad, Nordin¹; Ibrahim, Rosdiazli¹

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Sep2016, Vol. 41 Issue 9, p3463-3471. 9p.

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Academic Journal

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

Authors : Liansheng Liu¹ ; Datong Liu¹ ; Yujie Zhang¹

Source: Sensors (14248220). May2016, Vol. 16 Issue 5, p623. 17p.

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Academic Journal

Reliability estimation of a system subject to condition monitoring with two dependent failure modes.

Authors : Khaleghei, Akram¹ (AUTHOR); Makis, Viliam¹ (AUTHOR)

Source: IIE Transactions. Nov2016, Vol. 48 Issue 11, p1058-1071. 14p. 5 Charts, 3 Graphs.

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Academic Journal

Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring.

Authors : Salimiasl, Aydin¹ ; Özdemir, Ahmet²

Source: International Journal of Advanced Manufacturing Technology. Oct2016, Vol. 87 Issue 1-4, p1145-1158. 14p. 9 Diagrams, 9 Charts, 3 Graphs.

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Academic Journal

Multi-scale hybrid HMM for tool wear condition monitoring.

Authors : Liao, Zhirong ; Gao, Dong¹; Lu, Yong¹

Source: International Journal of Advanced Manufacturing Technology. Jun2016, Vol. 84 Issue 9-12, p2437-2448. 12p.

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فلترة النتائج

A condition monitoring system for an early warning of developing faults in wind turbine electrical systems.

Non-destructive online condition monitoring and trend prediction of lubricating oil in a steam turbine.

A validation study of ACS-SSI for online condition monitoring of vehicle suspension systems.

Latest lessons learned, modern condition monitoring and advanced predictive maintenance for gas turbines.

Tidal stream generators, current state and potential opportunities for condition monitoring.

A Non-Invasive Method for Condition Monitoring of Induction Motors Operating Under Arbitrary Loading Conditions.

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

Reliability estimation of a system subject to condition monitoring with two dependent failure modes.

Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring.

Multi-scale hybrid HMM for tool wear condition monitoring.

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