Yayu Peng

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

Vibration monitoring is one of the most popular, effective, and reliable methods for bearing fault diagnosis. A key issue in the vibration monitoring for the bearings used in variable-speed wind turbines is the elimination of the effect of the turbine shaft speed fluctuation in the vibration signals measured under varying-rotating-speed conditions. This paper proposes a new current-aided vibration order tracking method for bearing fault diagnosis of variable-speed direct-drive (i.e., no gearbox) wind turbines. The method explores a new simple and effective approach to acquire the reference signal from a current signal measured from the stator of the generator for vibration order tracking. First, the instantaneous fundamental frequency of the current signal is estimated in the time-frequency domain to obtain the shaft rotating frequency. Then, the shaft phase-time relationship is established. With this information, the envelope of the synchronously recorded vibration signal is subsequently resampled at the equal-phase-increment time points. Finally, bearing fault diagnosis is performed by observing the peaks at bearing characteristic frequencies in the power spectrum of the resampled vibration envelope signal. The proposed method is validated by successful diagnosis of different bearing faults in a direct-drive wind turbine under varying-speed conditions.

Quantitative Evaluation of Wind Turbine Faults Under Variable Operational Conditions

Wind turbines have been widely used for clean and renewable electricity generation. The maintenance costs of wind turbines constitute a significant portion of the total cost of the generated electricity. Thus, health management systems are increasingly needed to reduce the maintenance costs and improve the reliability of wind turbines. This paper proposes a novel framework for the quantitative evaluation of faults and health conditions of wind turbines using generator current signals. A synchronous resampling algorithm is designed to handle nonstationary current signals for fault feature extraction. The extracted fault features are used to reconstruct new signals, whose correlation dimensions are then calculated by using the Grassberger–Procaccia (G–P) algorithm for fault and health condition evaluation of the wind turbines. Experimental studies are carried out for a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) in the healthy condition and two faulty conditions. Results show that the proposed framework can not only detect the faults but also quantify different health conditions for the wind turbine.

Current-Based Fault Detection and Identification for Wind Turbine Drivetrain Gearboxes

This paper proposes a new fault detection and identification framework for drivetrain gearboxes of wind turbines equipped with doubly-fed induction generators (DFIGs) based on the fusion of DFIG stator and rotor current signals. First, the characteristic frequencies of gearbox faults in DFIG stator and rotor currents are analyzed. Different time- and frequency-domain features of gearbox faults in DFIG stator and rotor current signals are then defined, and the methods to extract these features are introduced. These features are used as the inputs of multiclass support vector machines with probabilistic outputs for fault mode identification. Different schemes that use a single stator or rotor current signal or both stator and rotor current signals for the feature- or decision-level information fusion are designed. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to validate the proposed current-based fault detection and identification framework.

Current-based fault detection and identification for wind turbine drivetrain gearboxes

This paper proposes a new fault detection and identification framework for drivetrain gearboxes of wind turbines equipped with doubly-fed induction generators (DFIGs) based on the fusion of DFIG stator and rotor current signals. First, the characteristic frequencies of gearbox faults in DFIG stator and rotor currents are analyzed. Different time- and frequency-domain features of gearbox faults in DFIG stator and rotor current signals are then defined, and the methods to extract these features are introduced. These features are used as the inputs of multiclass support vector machines with probabilistic outputs for fault mode identification. Different schemes that use a single stator or rotor current signal or both stator and rotor current signals for the feature- or decision-level information fusion are designed. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to validate the proposed current-based fault detection and identification framework.

Bearing Fault Diagnosis of Direct-Drive Wind Turbines Using Multiscale Filtering Spectrum

Fault diagnosis of the bearings in direct-drive (i.e., no gearbox) wind turbines is a challenging issue due to the varying shaft rotating frequency (SRF) caused by the erratic wind environment. To remove the spectrum smearing phenomena of the SRF-related components and the disturbances of the SRF-unrelated components in a measured signal, this paper proposes a novel method, called multiscale filtering spectrum (MFS), to obtain the weighted energy distribution of the monocomponent signals within a local order range based on the Vold-Kalman filter (VKF). First, the instantaneous SRF of the wind turbine is estimated from a generator current signal. Then, a VKF-based multiscale filter bank is designed according to the center frequencies corresponding to the SRF at different scales. The monocomponent signals whose frequencies are continuous multipliers of the SRF are subsequently extracted from the envelope of the measured current or vibration signal. Finally, a weighted energy spectrum is constructed within the selected order range, from which possible bearing fault characteristic orders can be identified. Simulation and experiment results show that the proposed new MFS method can enhance the characteristic orders and suppress the noise, and therefore has better performance than the traditional angular resampling method for bearing fault diagnosis of direct-drive wind turbines under varying speed conditions.

A comparative study on Vibration- and current-based approaches for drivetrain gearbox fault diagnosis

Gearboxes are widely used in rotary machines, such as wind turbines, automobiles, and helicopters. Failures of gearboxes contribute to a significant portion of the total failures and downtime in these machines. Gearbox fault diagnosis is an effective means to prevent catastrophic failures, improve the reliability, and reduce the downtime and maintenance cost of these machines. Vibration-based approaches have been employed in most commercially available condition monitoring systems (CMSs); while current-based approaches have received increasing attentions in the industry and academia. This paper presents a comparative study on the vibration- and current-based approaches for gearbox fault diagnosis. Theoretical analysis and experimental tests are carried out to show that: in vibration signals, ghost frequencies appear when the gearbox is healthy and disappear when a gear fault occurs; vibration signals of a faulty gearbox are modulated by shaft rotating frequencies at gear meshing frequencies (GMFs); in current signals, the fundamental frequency component is dominant when the gearbox is healthy; and current signals are modulated by gearbox characteristic frequencies at the fundamental frequency when gear faults appear.

Gearbox fault diagnosis using vibration and current information fusion

This paper proposes a novel vibration and current information fusion-based fault diagnostic method for drivetrain gearboxes. First, two multiclass support vector machines (SVMs) are designed to output the probabilities of different fault (or health condition) classes according to the input features extracted from a vibration signal and a current signal collected from the condition monitoring system, respectively. The Dempster-Shafer (D-S) theory is then applied to fuse the probabilistic outputs of two SVMs to get the final fault diagnostic result. Experiments are conducted for a gearbox with different types of fault, where a gearbox vibration signal and a generator current signal are collected to prove the effectiveness of the proposed method. Results show that the proposed method is more robust and reliable than the traditional methods of using a single sensor or a single type of sensor for gearbox fault diagnosis.

Bearing fault diagnosis of direct-drive wind turbines using multiscale filtering spectrum

Bearing fault diagnosis of direct-drive (i.e., no gearbox) wind turbines is a challenging issue due to the varying shaft rotating frequency (SRF) caused by the erratic wind environment. To solve the spectrum smearing problem of the SRF-related components and remove the disturbances of the SRF-unrelated components in a measured signal, this paper proposes a novel method, called multiscale filtering spectrum (MFS), to obtain the weighted energy distribution of the mono-component signals within a local order range based on the Vold-Kalman filter (VKF). First, the instantaneous SRF of the wind turbine is estimated from a generator current signal. Then, a VKF-based multiscale filter bank is designed according to the center frequencies corresponding to the SRF at different scales. The mono-component signals whose frequencies are continuous multipliers of the SRF are subsequently extracted from the envelope of the measured current or vibration signal. Finally, a weighted energy spectrum is constructed within the selected order range, from which possible bearing fault characteristic orders can be identified. Simulation and experiment results show that the proposed new MFS method can enhance the characteristic orders and suppress the noise and, therefore, has better performance than the traditional angular resampling method for bearing fault diagnosis of direct-drive wind turbines under varying speed conditions.

Quantitative evaluation of wind turbine faults under variable operational conditions

Wind turbines have been widely used for clean and renewable electricity generation. The maintenance costs of wind turbines constitute a significant portion of the total cost of the generated electricity. Thus, health management systems are increasingly needed to reduce the maintenance costs and improve the reliability of wind turbines. This paper proposes a novel framework for the quantitative evaluation of faults and health conditions of wind turbines using generator current signals. A synchronous resampling algorithm is designed to handle nonstationary current signals for fault feature extraction. The extracted fault features are used to reconstruct new signals, whose correlation dimensions are then calculated by using the Grassberger-Procaccia (G-P) algorithm for fault and health condition evaluation of the wind turbines. Experimental studies are carried out for a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) in the healthy condition and two faulty conditions. Results show that the proposed framework can not only detect the faults but also quantify different health conditions for the wind turbine.

Sensor fault detection and isolation for a wireless sensor network-based remote wind turbine condition monitoring system

To improve the reliability of wind turbines, various condition monitoring systems (CMSs) have been developed and most of them transmit data using wired communication channels. Recently, wireless sensor networks (WSNs) have been used to transmit data in wind turbine CMS due to the low cost and easy deployment feature of WSNs. However, since wind turbines are installed in harsh environments, the sensors and sensor nodes used in the WSN-based wind turbines CMSs are easily subject to faults, leading to corruption of the signals used for condition monitoring, which decreases the reliability of the CMS. This paper proposes a three-stage method for detection and isolation of three most common sensor faults, i.e., SHORT fault, CONSTANT fault, and NOISE fault, in WSN-based wind turbine CMS. The proposed sensor fault detection and isolation (SFDI) greatly increases the accuracy and reliability of wind turbine CMSs. Data collected from wind turbines in the field are used to validate the effectiveness of the proposed method.