Wind Turbine Planetary Gearbox Condition Monitoring Method Based on Wireless Sensor and Deep Learning Approach

Lü, Li; He, Yigang; Ruan, Yi; Yuan, Wanmai

doi:10.1109/tim.2020.3028402

Cited by 26 publications

(17 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generator and gearbox failures make up 95% of all failures in the major replacement category [3]. The gearbox is one of the most vulnerable subsystems accounting for 20% of the maintenance breaks [4]. All in all, the costs of maintenance accumulate to nearly 30% of the total costs during the life cycle of an offshore wind turbine [5].…”

Section: B Wind Farms Failures and Their Preventionmentioning

confidence: 99%

“…• In [4], a wireless condition monitoring system based on nRF24L01 radio transceiver module has been promoted to early detect the gearbox faults. • In [13], authors introduce a mobile edge computingdriven unmanned aerial vehicles (UAVs) to visually inspect the wind turbines with HD camera and report the data through a satellite.…”

Section: A Wireless Monitoring In Wind Farmsmentioning

confidence: 99%

See 1 more Smart Citation

Enabling mMTC in Remote Areas: LoRaWAN and LEO Satellite Integration for Offshore Wind Farm Monitoring

Ullah

Mikhaylov

Alves

2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

The offshore wind farms are gaining momentum due to their promise to offer sustainable energy with low pollution and greenhouse gases emission. However, despite all the immense technological progress of recent years, the operation in a harsh and hard-to-reach environment remains challenging. According to the reports, each offshore wind turbine requires five maintenance visits a year on average, and the cumulative repair costs constitute around 30% of the turbine's life-cycle expenditure. Motivated by the advancement of massive machinetype connectivity (mMTC) and satellite technologies, in this study, we investigate the potential of these to enable remote monitoring of the offshore wind farms. Specifically, the two alternative architectures are considered. The indirect architecture relies on using a local mMTC gateway (GW) with a backbone over a reliable communication channel (e.g., satellite or wire-based). The direct approach implies the transmission of the data by sensors on the wind turbines directly to the mMTC GW on the lowearth orbit (LEO) satellite. The details of the system design, the alternative implementation strategies and relevant pros, cons and trade-offs are pin-pointed. Finally, we employ simulations using realistic deployment and traffic and advanced propagation and collision models to characterize these two approaches' feasibility and packet delivery probability numerically when implemented over LoRaWAN mMTC technology.

show abstract

Section: B Wind Farms Failures and Their Preventionmentioning

confidence: 99%

Section: A Wireless Monitoring In Wind Farmsmentioning

confidence: 99%

Enabling mMTC in Remote Areas: LoRaWAN and LEO Satellite Integration for Offshore Wind Farm Monitoring

Ullah

Mikhaylov

Alves

2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…Finally, cameras can be used for metal deformation image recording [91,92]. For more centralization and ease of CM system implementation in a single processing system rather than individually installed ones, wireless sensors can be used to send data measurements to a centered data analysis base for less complex processing [93][94][95]. Moreover, one can find more detection methods such as ultrasonic, thermo graphical, and radio graphical testing (e.g., see García Márquez et al [96]).…”

Section: Data Acquisitionmentioning

confidence: 99%

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

et al. 2021

View full text Add to dashboard Cite

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

show abstract

“…In recent years, data-driven wind turbine power curves (WTPCs) have become vital for many applications such as condition monitoring, forecasting, see for example [20], [21]. Examples of data-driven techniques for monitoring WTs are also presented in [8] and [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Operational Variables for Improving Industrial Wind Turbine Yaw Misalignment Early Fault Detection Capabilities Using Data-Driven Techniques

Pandit

Infield

Dodwell

2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Offshore wind turbines are complex pieces of engineering and are, generally, exposed to harsh environmental conditions that are making them to susceptible unexpected and potentially catastrophic damage. This results in significant down time, and high maintenance costs. Therefore, early detection of major failures is important to improve availability, boost power production and reduce maintenance costs.This paper proposes a SCADA data based Gaussian Process (GP) (a data-driven, machine learning approach) fault detection algorithm where additional model inputs, called operational variables (pitch angle and rotor speed) are used. Firstly, comparative studies of these operational variables are carried out to establish whether the parameter leads to improved early fault detection capability; it is then used to construct an improved GP fault detection algorithm. The developed model is then validated against existing methods in terms of capability to detect in advance (and by how much) signs of failure with a low false positive rate.Failure due to yaw misalignment results in significant down time and a reduction in power production was found to be a useful case study to demonstrate the effectiveness of the proposed algorithms. Historical SCADA 10-minute data obtained from pitch-regulated turbines were used for models training and validation purposes. Results show that (i) the additional model inputs were able to improve the accuracy of GP power curve models with rotor speed responsible for a significant improvement in performance; (ii) the inclusion of rotor speed enhanced early failure detection without any false positives, in contrast to the other methods investigated.

show abstract

Wind Turbine Planetary Gearbox Condition Monitoring Method Based on Wireless Sensor and Deep Learning Approach

Cited by 26 publications

References 51 publications

Enabling mMTC in Remote Areas: LoRaWAN and LEO Satellite Integration for Offshore Wind Farm Monitoring

Enabling mMTC in Remote Areas: LoRaWAN and LEO Satellite Integration for Offshore Wind Farm Monitoring

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Operational Variables for Improving Industrial Wind Turbine Yaw Misalignment Early Fault Detection Capabilities Using Data-Driven Techniques

Contact Info

Product

Resources

About