Priority analysis for risk factors of equipment in a hydraulic turbine generator unit

Xu, Beibei; Chen, Diyi; Li, Huanhuan; Zhuang, Keyun; Hu, Xiao-Long; Li, Jinwei; Skjelbred, Hans Ivar; Kong, Jiehong; Patelli, Edoardo

doi:10.1016/j.jlp.2019.01.002

Cited by 14 publications

(9 citation statements)

References 37 publications

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“…In conclusion, the calculated results in cases 1 to 3 are validated in refs., [42][43][44][45][46] and the diagnostic results obtained in cases 4 to 6 are consistent with ref. 47 …”

Section: Cpt Of Node F3f0supporting

confidence: 90%

“…From the analysis of cases 3 and 6, when the HGS shows the same fault characteristic except for the mechanical axial crack, the occurrence probability of the rubbing fault, and the misalignment fault of rotor will decrease. In conclusion, the calculated results in cases 1 to 3 are validated in refs., [42][43][44][45][46] and the diagnostic results obtained in cases 4 to 6 are consistent with ref. 47…”

Section: Cpt Of Node F3f0supporting

confidence: 90%

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Bayesian network approach to fault diagnosis of a hydroelectric generation system

Pang

et al. 2019

Energy Science & Engineering

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This study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic‐mechanical‐electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time‐based maintenance to transform to the condition‐based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy‐Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system.

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“…In conclusion, the calculated results in cases 1 to 3 are validated in refs., [42][43][44][45][46] and the diagnostic results obtained in cases 4 to 6 are consistent with ref. 47 …”

Section: Cpt Of Node F3f0supporting

confidence: 90%

Section: Cpt Of Node F3f0supporting

confidence: 90%

Bayesian network approach to fault diagnosis of a hydroelectric generation system

Pang

et al. 2019

Energy Science & Engineering

Self Cite

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“…So that over time, this classification model can be more accurate. This study is similar to [17], performed by analyzing, ranking and prioritizing risk factors that cause the failure of hydraulic turbine generator sets.…”

Section: Discussionmentioning

confidence: 99%

Priority Analysis of Community Complaints through E-Government Based on Social Media

Madyatmadja¹,

Olivia²,

Sunaryo³

2019

IJRTE

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Now days in this modern era of globalization, the development of information and communication technology has offered solutions to improve public service performance. This also provides new innovations for the government to be able to offer the latest solutions in improving its services. This report discusses research based on Presidential Instruction No. 3 of 2003 concerning national policies and strategies for the development of e-Government, various regions began to develop the application. The case is discussed about the application of e government in Tangerang city. LAKSA is a feature for the people of Tangerang City to convey their aspirations, aim to the government can effectively and efficiently fulfill the needs of the city of Tangerang. With this prosperity, the government response is needed in accordance with the level of urgency of the complaints conveyed by the community. This research will compare the methods that can be used to solve this problem, namely Naive Bayes and decision trees

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“…The hydro-generator is a device that converts the mechanical energy generated by the rotation of the hydro-turbine into electrical energy. Its structure and performance play a vital role in the stable and efficient operation of the entire power station [44,45]. In the operating conditions of the generator set, due to the air movement, the imbalance of the stator and rotor, the friction and collision of mechanical parts, and the electromagnetic effect, the generator set produces shock and vibration, thereby causing the noise.…”

Section: Acoustic Vibration Detection Of the Hydro-generator Faultsmentioning

confidence: 99%

Acoustic Vibration Approach for Detecting Faults in Hydroelectric Units: A Review

et al. 2021

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The health of the hydroelectric generator determines the safe, stable, and reliable operation of the hydropower station. In order to keep the hydroelectric generator in a better state of health and avoid accidents, it is crucial to detect its faults. In recent years, fault detection methods based on sound and vibration signals have gradually become research hotspots due to their high sensitivity, achievable continuous dynamic monitoring, and easy adaptation to complex environments. Therefore, this paper is a supplement to the existing state monitoring and fault diagnosis system of the hydroelectric generator; it divides the hydroelectric generator into two significant parts: hydro-generator and hydro-turbine, and summarizes the research and application of fault detect technology based on sound signal vibration in hydroelectric generator and introduces some new technology developments in recent years, and puts forward the existing problems in the current research and future development directions, and it is expected to provides some reference for the research on fault diagnosis of the hydroelectric generator.

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Priority analysis for risk factors of equipment in a hydraulic turbine generator unit

Cited by 14 publications

References 37 publications

Bayesian network approach to fault diagnosis of a hydroelectric generation system

Bayesian network approach to fault diagnosis of a hydroelectric generation system

Priority Analysis of Community Complaints through E-Government Based on Social Media

Acoustic Vibration Approach for Detecting Faults in Hydroelectric Units: A Review

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