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Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors explore fault data provided by the supervisory control and data acquisition system and offer fault prediction at three levels: (1) fault and no-fault prediction; (2) fault category (severity); and (3) specific fault prediction.

409 citations

Journal ArticleDOI
TL;DR: This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines (WTs) and discusses the common failure modes in the major WT components and subsystems.
Abstract: This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines (WTs). The Part I of this survey briefly reviews the existing literature surveys on the subject, discusses the common failure modes in the major WT components and subsystems, briefly reviews the condition monitoring and fault diagnostic techniques for these components and subsystems, and specifically discusses the issues of condition monitoring and fault diagnosis for offshore WTs.

402 citations

Journal ArticleDOI
01 Sep 2011
TL;DR: Support Vector Machine (SVM) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the frame vibrations during start-up to set up a base for condition monitoring technique of induction motor which will be simple, fast and overcome the limitations of traditional data-based models/techniques.
Abstract: Condition monitoring of induction motors is a fast emerging technology in the field of electrical equipment maintenance and has attracted more and more attention worldwide as the number of unexpected failure of a critical system can be avoided. Keeping this in mind a bearing fault detection scheme of three-phase induction motor has been attempted. In the present study, Support Vector Machine (SVM) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the frame vibrations during start-up. CWT has not been widely applied in the field of condition monitoring although much better results can been obtained compared to the widely used DWT based techniques. The encouraging results obtained from the present analysis is hoped to set up a base for condition monitoring technique of induction motor which will be simple, fast and overcome the limitations of traditional data-based models/techniques.

400 citations

Book
06 Apr 2011
TL;DR: In this paper, the authors present fault-tolerant systems for electrical drives, actuators, and sensors for 20 real technical components and processes as examples, such as:Electrical drives (DC, AC)Electrical actuatorsFluidic actuators (hydraulic, pneumatic)Centrifugal and reciprocating pumpsPipelines (leak detection)Industrial robotsMachine tools (main and feed drive, drilling, milling, grinding)Heat exchangers).
Abstract: Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity.This book is a sequel of the book Fault-Diagnosis Systems published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as:Electrical drives (DC, AC)Electrical actuatorsFluidic actuators (hydraulic, pneumatic)Centrifugal and reciprocating pumpsPipelines (leak detection)Industrial robotsMachine tools (main and feed drive, drilling, milling, grinding)Heat exchangersAlso realized fault-tolerant systems for electrical drives, actuators and sensors are presented.The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful.The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.

400 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology, in particular, reliability and value for money.
Abstract: Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology.

395 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022413
2021798
2020927
2019936
2018906