Detection of Single-Axis Pitch Bearing Defect in a Wind Turbine Using Electrical Signature Analysis

He, Lijun; Hao, Liwei; Pan, Di; Wang, Qiao

doi:10.1109/iemdc.2019.8785091

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Different methods are used to identify WT compartments failures, including analyzing mechanical vibrations, acoustic emission, temperature, oil parameters, and electrical signals. Electrical signals, such as current signal, are available remotely, do not require additional sensors, and can be analyzed in real-time [12]. Since faults of a bearing are associated with mechanical defects, they introduce excitations at particular frequencies [7], [13].…”

Section: In Addition Online Diagnosis Methods Allow Automatic Remotementioning

confidence: 99%

“…From (9), at the n-th frame, we can obtain an estimate of the parameters α and β for the j-th element aŝ Fig. 2 shows the empirical and analytical pdfs of x n1 and x n9 from the feature vector related to the outer race fault, by using parameters estimated with (12). In the experiment setup, the outer race fault is artificially introduced at the beginning of the experiment and its signature is shown in x n9 , which is at the frequency of 30.0 Hz.…”

Section: A Statistical Distributionmentioning

confidence: 99%

“…There is no fault signature at x n1 , which is at the frequency of 29.2 Hz. The analytical pdf is obtained by evaluating (8) by using parameters estimated from (12). The empirical pdf is obtained by normalizing the histogram of the experiment data.…”

Section: A Statistical Distributionmentioning

confidence: 99%

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Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Nath

Zhao

et al. 2020

IEEE Access

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Low latency change detection aims to minimize the detection delay of an abrupt change in probability distributions of a random process, subject to certain performance constraints such as the probability of false alarm (PFA). In this paper, we study the low latency detection of bearing faults of direct-drive wind turbines (WT), by analyzing the statistical behaviors of stator currents generated by the WT in real-time. It is discovered that the presence of fault will affect the statistical distribution of WT stator current amplitude at certain frequencies. Since the signature of a fault can appear in one of the multiple possible frequencies, we need to monitor the signals on multiple frequencies simultaneously, and each possible frequency is denoted as a candidate. Based on the unique properties of WT bearing faults, we propose a new multi-candidate low latency detection algorithm that can combine the statistics of signals from multiple candidate frequencies. The new algorithm does not require a separate training phase, and it can be directly applied to the stator current data and perform online detection of various possible bearing faults. The theoretical performance of the proposed algorithm is analytically identified in the form of upper bounds of the PFA and average detection delay (ADD). The algorithm allows flexible parametric adjustment of the tradeoff between PFA and ADD. INDEX TERMS Bearing fault, fault detection, quickest change detection, wind turbine.

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Section: In Addition Online Diagnosis Methods Allow Automatic Remotementioning

confidence: 99%

Section: A Statistical Distributionmentioning

confidence: 99%

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Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Nath

Zhao

et al. 2020

IEEE Access

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“…In the second paper [20], the fault was diagnosed with an Artificial Neural Network, to differentiate between 6 different fault cases. A paper from He et al [21] used electrical signature analysis of the pitch system to detect faults. This examined four fault indicators (FI): Negative Sequence FI, Positive Sequence FI, AC ripple peak, and root mean square (RMS).…”

Section: Pitch System Condition Monitoringmentioning

confidence: 99%

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

et al. 2021

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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Remote Monitoring and Diagnostics of Pitch Bearing Defects in a MW-Scale Wind Turbine Using Pitch Symmetrical-component Analysis

He¹,

Hao²,

Wang³

2019

2019 IEEE Energy Conversion Congress and Exposition (ECCE)

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Detection of Single-Axis Pitch Bearing Defect in a Wind Turbine Using Electrical Signature Analysis

Cited by 6 publications

References 12 publications

Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Remote Monitoring and Diagnostics of Pitch Bearing Defects in a MW-Scale Wind Turbine Using Pitch Symmetrical-component Analysis

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