Current-Aided Order Tracking of Vibration Signals for Bearing Fault Diagnosis of Direct-Drive Wind Turbines

Zhu, Zhongkui; Peng, Yayu; Wang, Qiao

doi:10.1109/tie.2016.2571258

Cited by 145 publications

(50 citation statements)

References 31 publications

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“…Vibration analysis is one of the powerful tools that is currently used to monitor the mechanical integrity of wind turbines [38]. Vibration sensors installed on the casing of the wind turbine are used to detect faults within various wind turbine components such as gearbox, bearing, rotor and blade, tower, generator and main shaft [39].…”

Section: Vibrationmentioning

confidence: 99%

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

2018

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Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.

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Section: Vibrationmentioning

confidence: 99%

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

2018

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“…12(b). It can be seen that the characteristic order of the reference shaft (1 ) and its harmonics (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) are quite abundant. The frequency change of the modulation component with the largest energy is the same as the change of the frequency of the reference shaft, thereby verifying that the fault gear is on the reference shaft.…”

Section: Application To the Gear Fault Detectionmentioning

confidence: 99%

“…Order tracking is a method of great value for vibration signal analysis because of its effectiveness in identifying and separating speed-related vibration [5]. Tacholess order tracking technology has gotten widespread concern, for avoiding installation of tachometers or angle encoders by estimating instantaneous frequency of shaft [4,6] or instantaneous phase of shaft [7,8]. The angular-stationary signal acquired from non-stationary signal by equal angle resampling can represent fault features more intuitively [9].…”

Section: Introductionmentioning

confidence: 99%

Tacholess order tracking method for gearbox based on time-varying filter and energy centrobaric correction method

Jing¹,

Luo²,

Leng³

et al. 2019

J. vibroeng.

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Under the premise that the instantaneous speed can be accurately measured or precise estimated, order tracking is considered to be a classical and effective technique for non-stationary vibration analysis of rotating machinery. The meshing frequency components of the vibration signal measured from complex gearbox system interfere with each other mutually. The energy centrobaric correction method exhibits exact instantaneous frequency estimation (IFE) ability under the interference, but is susceptible to strong noise. Combining with time-varying filter and energy centrobaric correction method, a tacholess order tracking technique for gear fault diagnosis under the rotating frequency multi-linear fluctuation is proposed. The time-varying filter is designed to filter the strong noise signal, and then IFE could be accurately calculated from the filtered signal by energy centrobaric correction method. The instantaneous phase of reference shaft based on the Vold-Kalman filter (VKF) is used for angular resampling of the original vibration signal. The results show that the shaft rotating speed could be accurately identified from extracted gear meshing components. The order spectrum obtained by the proposed method with tacholess is substantially the same as the order spectrum obtained by a tachometer. Simulation analysis and experimental results verify the advantages of proposed tacholess order tracking technology for monitoring and fault diagnosis of gearbox vibration under the condition of strong noise and large speed variation.

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“…Therefore, an adaptive algorithm is required in general [12]. Although various time synchronous average methods are proposed, the method still relies on auxiliary devices to acquire information other than the vibrational signal [13]. Although several tacholess order tracking method can be implemented in limited conditions [14–16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive online dictionary learning for bearing fault diagnosis

Xie

Liang

2018

Int J Adv Manuf Technol

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One of the most common parts to maintain system balance and support the various load in rotating machinery is the rolling element bearing. The breakdown of the element in bearings leads to inefficiency and sometimes catastrophic events across various industries. The main challenge over the last few years for fault diagnosis of bearings is the early detection of fault signature. In this paper, an adaptive online dictionary learning algorithm is developed for early fault detection of bearing elements. The dictionary is trained using a set of vibration signal from a heavily damaged bearing. The enveloped signal of the bearing is obtained using the Kurtogram and split into several sections. The K-SVD algorithm is implemented to the dictionaries corresponding to the enveloped signal. OMP is implemented with the calculated dictionaries to obtain the sparse representation of the testing signal. Then the envelope analysis is implemented to obtain the fault signal from the recovered signal by the dictionaries. The adaptive algorithm is added to the dictionary learning to update the dictionary based on newly acquired data with the weighted least square method. Without retraining the dictionaries using the K-SVD algorithm, the computation speed is significantly improved. The proposed algorithm is compared with a traditional dictionary learning algorithm to show the improvement in detection of new fault frequency and improved signal to noise ratio.

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Current-Aided Order Tracking of Vibration Signals for Bearing Fault Diagnosis of Direct-Drive Wind Turbines

Cited by 145 publications

References 31 publications

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

Tacholess order tracking method for gearbox based on time-varying filter and energy centrobaric correction method

Adaptive online dictionary learning for bearing fault diagnosis

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