Online condition monitoring and remaining useful life prediction of particle contaminated lubrication oil

Zhu, Junda; Yoon, Jae; He, David; Qiu, Bin; Bechhoefer, Eric

doi:10.1109/icphm.2013.6621415

Cited by 35 publications

(23 citation statements)

References 13 publications

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“…With reference to the former developed physical model for lubrication oil water contamination and the experimental setup for the particle contamination tests, the following notations are defined:…”

Section: Model Development and Particle Filtering For Rul Predictionmentioning

confidence: 99%

“…At approximately 60°C, the value of V oil,T for the healthy validation oil is 20. In order to extrapolate the presented methods to other types of lubrication oil such as typical wind turbine gearbox oil, which normally has a much higher viscosity value than the validation oil, one would need to obtain V oil,T and ε oil,T from healthy wind turbine gearbox oil and use them in equations (4), (7) and (8) to compute the viscosity V M,T and DC ε M,T for the particle-contaminated wind turbine gearbox oil. V oil,T and ε oil,T can be obtained from new oil by lab testing in a temperature-controlled chamber.…”

Section: Particle Contamination Modelsmentioning

confidence: 99%

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Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines

Zhu¹,

Yoon

et al. 2014

Wind Energ.

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The widespread deployment of industrial wind projects will require a more proactive maintenance strategy in order to be more cost competitive. This paper describes an ongoing research project on developing online lubrication oil condition monitoring and degradation detection tools using commercially available online sensors. In particular, an investigation on particle contamination of lubrication oil is reported. Methods are presented for online lubrication oil condition monitoring and remaining useful life prediction using viscosity and dielectric constant sensors along with particle filtering technique. Physical models are derived in order to establish the mathematical relationship between lubrication oil degradation and particle contamination level. Laboratory experiments are performed to validate the accuracy of the developed models by comparing viscosity and dielectric constant sensor outputs of different particle concentration levels with those simulated by the lubricant deterioration physical models. A case study on lubrication oil degradation detection and remaining useful life prediction is provided. Discussions on the potential for extrapolating the presented methods to typical wind turbine gearbox oil and the practical implementation of particle filter‐based approach for online wind turbine gearbox oil remaining useful life prediction are also included. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Model Development and Particle Filtering For Rul Predictionmentioning

confidence: 99%

Section: Particle Contamination Modelsmentioning

confidence: 99%

Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines

Zhu¹,

Yoon

et al. 2014

Wind Energ.

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“…This feature makes the particle filtering a promising technique for CMFD and RUL prediction of WTs. The Bayesian method or particle filtering technique has been used for blade fault diagnosis [93], [94], bearing fault diagnosis and RUL prediction [95], condition monitoring and RUL prediction of lubrication oil [27], [28], and reliability evaluation [96] of WTs.…”

Section: J Bayesian Methodsmentioning

confidence: 99%

“…The online oil condition monitoring overcame the drawbacks of the offline monitoring by using oil sensors, such as viscometer, level sensor, particle counter, and thermometer, to monitor the oil condition in real time [27], [28]. However, the use of additional sensors increased the costs of the WTs.…”

Section: F Lubrication Oil Parametersmentioning

confidence: 97%

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

Wang

2015

IEEE Trans. Ind. Electron.

324

147

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This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines. The Part II of this survey focuses on the signals and signal processing methods used for wind turbine condition monitoring and fault diagnosis. ). respectively. A. VibrationMany WT faults induce vibrations of the corresponding WT subsystems, which can be detected by using the signals acquired from vibration sensors. Vibration monitoring is the dominant technique used in almost all commercially available WT condition monitoring systems (CMSs), in which the vibration sensors are usually installed on the casing of the gearbox, generator, main shaft and bearing, and blade surface.The major types of vibration sensors include accelerometers, velocity sensors, and displacement sensors. Accelerometers have the widest working frequency range from 1Hz to 30 kHz, In contrast, velocity sensors have a working frequency range from 10 Hz to 1 kHz and displacement sensors have a working frequency range of 1-100 Hz. Accelerometers are the most widely used vibration sensors in CMFD of WTs for their wide working frequency range. The signals acquired from accelerometers contain the information of accelerations of WT components caused by faults [1]. Displacement sensors have also been used in WT CMFD systems for diagnosing the faults leading to lowfrequency vibrations of WT components. Vibration monitoring has been used for CMFD of WT gearbox [2], [3], bearing [4], rotor and blade [5], generator, tower, main shaft, etc. The amplitude of the vibration signal can indicate the severity of a fault [6]. For example, the amplitude of the 1P frequency components in vibration signals provides a measure of rotor asymmetries [5].Through years of applications, the vibration-based CMFD technologies have been mature and standardized by ISO10816. However, this approach requires the installation of the capital cost and wiring complexity of the WT system. The vibration sensors are usually mounted on the surface or are buried in the body of WT components, making them difficult to access during WT operation. Moreover, the sensors and data acquisition devices are also inevitably subject to failure. Sensor failure may further cause the failure of WT control, mechanical, and electrical subsystems. These could cause additional problems with system reliability and additional operation and maintenance (O&M) costs. In addition, vibration signals usually have a low signal-to-noise ratio (SNR) when used to diagnose an incipient fault. B. AEMaterials that are subjected to stress or strain may emit sound waves, which is called AE [7]. The sources of AE can be located to detect possible defects of a structure using one or more AE sensors. The AE monitoring technology has been used for CMFD of WT blades [1], gearboxes [8], [9], and bearings [10]. As a blade is usually made of various materials and components, damages often grow over critical areas and the interfaces of different internal components inside the blade. Therefore...

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“…Many authors have presented different techniques for monitoring the gearbox and especially the HS bearing. These ranges from monitoring and analysing of supervisory control and data acquisition (SCADA) signals [13][14][15][16][17][18][19][20][21][22] to vibration analysis 21,[23][24][25][26][27][28][29] , and other condition monitoring techniques, [30][31][32] used for predicting and diagnosing incipient gearbox failures. The authors agree with these techniques but argue that there has been little done in previous literature to understand how PM tasks can be exploited to either prevent or manage the consequences of the failure of gearbox modules upon the availability of historical data.…”

Section: Reliability Predictionmentioning

confidence: 99%

Effect of preventive maintenance intervals on reliability and maintenance costs of wind turbine gearboxes

et al. 2014

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As many of the installed wind turbines (WTs) get older or approach their design life, there will be a drive to keep extending the lives of the main components especially the gearbox. The challenge of operations and maintenance will potentially be even more as there will be a need to keep the cost to a minimum. Similarly, as years of experience of operating WTs accumulate, knowledge about the behaviour and failure of subsystems is gained as well. Also with good documentation and repository of historical operational, performance and failure data, future decisions of operations and maintenance can be taken on the basis of insights from past experience. This paper presents an approach for implementing preventive maintenance (PM) by using historical failure data to determine the optimal PM interval required to maintain desired reliability of a typical module or subassembly. This paper builds upon previous research in the area of WT gearbox reliability analysis and prediction, taking it further by examining the relationships between the frequency of a PM task and the reliability, availability and maintenance costs. The approach presented demonstrates how historical in-service failure data can be used in PM task selection based on the minimum maintenance cost and maximum availability. Available historical field failure data of the high speed module of a Vestas 2MW WT gearbox is used to validate the approach and show its practicality. The results of this study are then presented-indicating that choosing the right PM interval based on the minimum unit maintenance cost and maximum availability also improves WT gearbox reliability.

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Online condition monitoring and remaining useful life prediction of particle contaminated lubrication oil

Cited by 35 publications

References 13 publications

Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines

Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

Effect of preventive maintenance intervals on reliability and maintenance costs of wind turbine gearboxes

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