Wind Turbine Condition Monitoring and Fault Diagnosis in China Xuefeng Chen, Ruqiang Yan, and Yanmeng Liu
W
ith the advent of a more severe energy crisis and environment contamination, wind energy, as one of the green and renewable energy resources, has attracted more and more attention worldwide [1], [2]. According to statistics issued by the Global Wind Energy Council (GWEC), by the end of 2014, the global installed capacity had reached 360,000 MW, while the installed capacity in China accounted for 114,763 MW, nearly one third of the total, as illustrated in Fig. 1. Meanwhile, it is envisioned in 2050 Blueprint of Wind Power Development in China that by the years 2030 and 2050, the scale of installed capacity will exceed 4 × 105 MW and 106 MW, respectively, so as to meet 8.4% and 17% of the demand for electricity nationwide, enabling wind energy to be one of five main energy resources. Since the first installation of a 1.5 MW wind turbine in 2005, China has witnessed increasing installment of wind turbines, adding up to over 70,000 wind turbines now. However, highspeed development can hardly deny low operation reliability, and the operation reliability of wind turbines in service is low. It is known that downtime caused by faults accounts for 25.6% of the rated generation time. For a wind turbine with a twentyyear service life, its operation and maintenance cost consumes 10%-15% of a wind farm’s revenue. Besides, for offshore wind farms, 20% to 25% of the revenue is spent on operation and maintenance. High operation and maintenance costs increase the running cost of wind farms, decreasing economic benefits accordingly. As a result, the industry is in urgent need of research and development of condition monitoring and fault diagnosis systems for wind turbines with the purpose of decreasing operation costs and lowering wind turbines’ operation risks, like disastrous accidents caused by early failure. In addition, Guidelines on Vibration Condition Monitoring of Wind Turbines issued by the National Energy Administration of China in November 2011 states that offshore wind turbines (≥2 MW) should be stationary mounted. For wind turbines with power lower than 2 MW, semi-fixed installation systems or portable systems should be applied. Although 1.5 MW wind turbines are popular in China, most manufacturers are investigating and promoting wind turbines with power above 2 22
MW, which means that more and more manufacturers need to add vibration monitoring into their integrated systems. Correspondingly, developing an on-line condition monitoring fault diagnosis system for wind turbines will help increase availability, maintain equipment, and improve utilization of wind turbines.
A Wind Turbine Monitoring and Diagnosis System There are many organizations that conduct research and development in the area of wind turbine condition monitoring and fault diagnosis in China, including Tsinghua University, Beijing University of Chemical Technology, North China Electric Power University, Chongqing University, etc. Their research results have been applied in the wind turbine industry, as by the Goldwind Group, and they have advanced wind energy utilization in China. In particular, a holistic approach of combining theoretical analysis, technical development, and practical verification has addressed some key issues in developing wind turbine condition monitoring and fault diagnosis systems. It is the HET-P system, which has been widely installed on more than one hundred wind farms. Based on the HET-P system, an integrated wind turbine drive-train vibration analysis and diagnosis model, named “Primary diagnosis-precision diagnosis-remote diagnosis,” was established. Primary assessment of fault components is realized through a two-grade vibration amplitude alarm strategy. Then, vibration data of those observed key components are analyzed in detail with the help of precision diagnosis to identify the location of fault sources. In the last step, doubts in fault diagnosis are reconfirmed by experts or diagnosis teams remotely so that mistakes can be prevented and the accuracy of fault diagnosis can be improved. This HET-P system takes component structure, variable speed operating conditions, and severe environmental temperature differences into consideration. Stable and reliable data acquisition and modern fault diagnosis techniques used in this system not only provide a supporting platform for reliable operation of wind turbines, but also identify conditions
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Fig. 1. Accumulative installed capacity of wind turbines in China and worldwide from 2001 to 2014 [3].
of key components online, realizing remote and real-time monitoring, fault alarm and operating conditions diagnosis of the drive-train in the wind turbines. They provide scientific evidence for preventative maintenance, which lowers the maintenance cost of wind turbines. The HET-P system mainly includes three key parts: Information Acquisition Module, the Software Module, and the Remote Monitoring and Diagnosis Module.
at both the turbine cabin and tower bottom; therefore, the data acquisition unit can transfer the data to the tower bottom through a fiber-optic transmission line. Fig. 2 shows the hardware for data acquisition and transmission, which features the functions of real-time data sampling, transmission, storage and management. Fig. 3 illustrates how data are collected for wind turbines.
Software Module Information Acquisition Module
There are two key techniques built into the software module. To effectively monitor the wind turbine drive train in real- One is that a peak index is constructed based on large data time, a data acquisition (DAC) system is built to continuously mining using a support vector machine. This index is able to capture its information. The system is made of an input in- provide an alarm threshold for identifying the operating staterface, preamplifier, filter, follower, A/D converter, buffer, a tus of wind turbines. The other is a sparse diagnosis method, core processor unit and an external interface. The signals are which is proposed to process vibration data and is able to measured by the sensors, which are connected to the DAC reflect fault features through energy concentration in the specsystem through the input interface. Then, the signals are am- trum. As a result, it can trace back and locate faults of the plified by a preamplifier, and a filter is used to remove some drive-train system in wind turbine equipment. unwanted noise. After that, the signal is sent through the folBased on established alarm standards, the software module lower with high driving capability before it enters the A/D is constructed to evaluate the operation status of the wind turconverter for A/D conversion and then stays in the buffer. The bines, which is executed through extraction and analysis of the data in the buffer are analyzed with corresponding algorithms test data. If it is approaching the alarm threshold, the module in the core processor unit. Finally, the processed data are packed, transferred through an external interface and are then displayed on a computer. The online data acquisition unit, power supply unit, and communication signal exchange unit are all set up in the protective unit of the data acquisition system, where wires connect the interface of the protective unit and the sensors at measurement points. Fig. 2. Data acquisition and transmission hardware. Switchboards are installed April 2016
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Fig. 3. Illustration of data collection for wind turbines. FOT stands for fiber optic transmitter.
will first identify the fault of key components with reference to quantitative diagnosis standards. Then, more attention will be paid to those key components whose vibration amplitude exceeds the alarm threshold. The monitoring interface is shown in Fig. 4. When the status information of the wind turbine exceeds the alarm threshold, fault location identification occurs. The reason why wind turbines are degraded is examined by both classical signal analysis and specialized signal analysis techniques. Then, key components are confirmed to guide corresponding strategies for wind farm maintenance. As one of the major classical signal analysis techniques, time domain statistics are implemented first, including
minimum/maximum vibration amplitude, mean value, variance, skewness, kurtosis, etc. Then, some specialized signal analysis techniques, such as sparse decomposition, are adopted to analyze non-stationary and nonlinear vibration signals. The inherent signal components are extracted, and their instantaneous frequency information is displayed to reflect the dynamics of the signal. With these specialized signal analysis techniques, the non-stationary signal component featuring faults can be effectively separated and identified. As an example, Fig. 5 shows the software analysis interface.
Fig. 4. Primary diagnosis interface based on alarm standards.
Fig. 5. Analysis interface of wind turbine vibration data.
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Fig. 6. Illustration of an Internet-based remote monitoring service.
Remote Monitoring and Diagnosis Module By utilizing the HET-P system and web technology, vibration data collected from single or multiple wind farms can be transmitted to a remote monitoring and diagnosis center, as Fig. 6 illustrates, where an internet-based remote monitoring and diagnosis service is provided. This can help to store vibration data with the potential to utilize large amounts of them to set up an alarm threshold for monitoring wind turbines. Furthermore, it can provide remote technical support with the help of fault diagnosis experts for solving difficult problems encountered in wind turbine condition monitoring. In such a way, information about the wind turbine status can be fused together to improve the accuracy of the fault diagnosis and secure safe operation of wind turbines. Based on the three modules described above, Fig. 7 shows the developed wind turbine monitoring and diagnosis system. This system has received certification of electromagnetic compatibility (EMC) according to instructions from the international Community European (CE) – GB/T Std 17799.2-2003, the General Standard of EMC, and safety supervision of the testing center in the China Electrical Power Research Institute. Currently, the system has been widely installed on more than one hundred wind farms in the Gansu, Inner Mongolia and Xinjiang Uygur Autonomous Regions. April 2016
The Key Technique in Monitoring Wind Turbines In general, monitoring and diagnosing wind turbines in an effective way are quite challenging. They are different from those large-scale rotating machines running under constant speed because wind turbines feature variable-speed operations. Therefore, traditional methods used for rotating machines with constant speed are not qualified for this application, which leaves a big issue in the field of vibration monitoring and diagnosis of wind turbines.
Fig. 7. HET-P system for wind turbine monitoring and diagnosis.
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Table 1 – FAG6326C3 bearing defect characteristic frequency at a speed of 1791 r/min Outer ring fault frequency (Hz)
Inner ring fault frequency (Hz)
Roller fault frequency (Hz)
Holder fault frequency (Hz)
93.5
145.3
65.6
11.7
There are three main difficulties. First, it is difficult to identify the overall vibration thresholds with variable speed. Thus, real-time monitoring and an alarm system for wind turbines are hard to realize because the vibration amplitude fluctuates in complicated working conditions, such as variable-speed operating conditions. Second, with variable speed, fault diagnosis of the drive-train system is also difficult. Since the planetary gearbox and bearings inside the drive system run with variable speed, it causes structural defect-related response signals to be low in time-frequency aggregation, leading to difficulty in fault feature extraction. Third, failure modes of large-scale composite blades are clearly different from those of metal structures. Variable speed causes fluctuation of the stress and acoustic signals, making damage difficult to identify. To tackle this challenge, a key technique called “Sparse Decomposition” has been developed [4]. Specifically, recent advances in signal processing have focused on the use of sparse representation in various applications. as a Sparse representation aims to represent a signal linear combination of a few elements from a given dictionary
. In particular, we can write , where has nonzero entries. Moreover, we are interested in the case where m < n. Elementary linear algebra tells us that is not uniquely recoverable from x by linear algebraic means, as the equation x = D may have many solutions. However, we are seeking a sparse solution, and for a certain dictionary D, sparsity will prove a powerful constraint. The simplest way to pose a recovery algorithm is using the optimization
. (1)
Relying on the prior information that faulty features with a similar spectrum have different oscillating waveforms, apparently intractable problems could be reformulated into a sparse optimization problem with appropriate regularization terms, which enforce structured sparsity constraints over the subcomponents of the vibration signals. Moreover, harmonic components can be sparsely represented in the redundant harand impulsive components could monic dictionary be sparsely approximated through an over-complete Gawith an appropriate window length. bor dictionary Introducing a union of the two redundant dictionaries, strikingly, extracting multiple components from compound signals could be formally expressed as the following sparse optimization problem:
(2)
counts the nonzero entries of a vector. The variable x is an approximation of harmonic components x1, and is the coefficient of impulsive components under the Gabor dictionary D. Moreover, 1 and 2 are the regularization parameters which balance the sparse degree of every component. The constraint in this optimization problem accounts for the presence of noise and model imperfection, thus ≤ 0 depends upon the noise variance and is usually set as for practical applications. The solution to (2) can be solved using an orthogonal matching pursuit algorithm, and the details can be referred to in [4]. As an example, when one wind turbine is inspected on a wind farm, it is found that the peak Fig. 8. Time domain waveform and decomposed components of vibration signals in a wind turbine gearbox. 26
where
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consistent with the physical examination by putting a hose endoscope into the gearbox to check the bearing. Findings indicate that the high-speed end bearing inside the gearbox had a serious scratch, as Fig. 10 shows.
Summary With the advance of scie n c e a n d t e c h n o l o g y, China is promoting the development of wind energy. However, with the high demand for energy efficiency, the size of wind turbines has been increasing, leading to higher operational costs, and a higher risk of failures. To lower the operational risk to wind turbines and decrease the corresponding costs, developing condiFig. 9. An envelope spectrum of the vibration signal in a wind turbine gearbox after decomposition. tion monitoring and fault value of the bearing vibration signal near to the high speed end diagnosis tailored to wind turbine technology is becoming of the gearbox fluctuates seriously, and the root mean square a direction of long-term focus. In this article, we introduced value is much higher than that of other measuring points, high some of the research activities regarding the development above the normal value. To ensure safe operation of the wind of wind turbine condition monitoring and fault diagnosis turbine, features are extracted from vibration signals of the systems at a collaborative innovation center of high-end mangearbox, and the corresponding health condition evaluation ufacturing equipment in China. In particular, a key technique is conducted. It is known that the model of the rear bearing is based on sparsity theory has been developed to monitor and FAG6326C3. Since the rotating speed is 1971 r/min, the cor- diagnose wind turbines running in variable speed conditions. responding bearing defect characteristic frequencies can be It is envisioned that with the increased maturity in technolanalytically calculated, as Table 1 shows. ogy, including monitoring and diagnosis techniques for wind The sparse decomposition technique is adopted to process the vibration signal as shown in the graphs in Fig. 8, with the discrete cosine transform (DCT) extracting the harmonic waves and the discrete wavelet transform (DWT) extracting the impact components. The processed data length is 8,192 with 12,800 Hz sampling frequency, leading to 1.5625Hz frequency resolution. Fig. 8 shows the time domain waveform of each signal component after decomposition, and Fig. 9 shows the corresponding envelope spectrum. Due to the influence of interfering signals, the defect-related information can hardly be observed in the envelope spectrum of the original signal, as shown in Fig. 9. From the composition of spectrum peaks with the frequency of 144 Hz in the envelope spectrum of the impact signal after decomposition, there is an indication that regular vibration impact with the frequency of 144 Hz exists in the original signal. The frequency of 144 Hz corresponds to the defect characteristic frequency of the bearing’s inner ring, indicating that the abnormal vibration of the bearing is caused by the defect in the inner ring. This is Fig. 10. Scratch on the inner ring of a high-speed end bearing in the gearbox. April 2016
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turbines, wind energy will be available for the Chinese people’s daily lives more and more.
References [1] S. Chu and A. Majumdar, “Opportunities and challenges for a sustainable energy future,” Nature, vol. 488, pp. 294-303, 2012. [2] W. Liu, B. Tang, J. Han, X. Lu, N. Hu, and Z. He, “The structure
2007, the Second Awards of Technology Invention of China in 2009, the China National Funds for Distinguished Young Scientists in 2012 and was named a chief scientist of the National Key Basic Research Program of China (973 Program) in 2015. Dr. Chen is the chapter chairman of the IEEE Xi’an and Chengdu Joint Section Instrumentation and Measurement Society.
healthy condition monitoring and fault diagnosis methods in wind turbines: a review,” Renewable and Sustainable Energy Reviews, vol. 44, pp. 466-472, 2015. [3] “Global Wind Report 2014: Annual Market Update,” IEA Wind, G. W. E. Council, vol. 149, 2015. [4] D. Zhaohui, C. Xuefeng, Z. Han, and Y. Ruqiang, “Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis,” IEEE Trans. Ind. Electron., vol. 62, pp. 6594-6605, 2015.
Xuefeng Chen (
[email protected]) received his Ph.D. degree from the Xi’an Jiaotong University, Xi’an, China in 2004. He is a Professor of Mechanical Engineering with Xi’an Jiaotong University. His research interests include finite-element method, mechanical systems and signal processing, diagnosis and prognosis for complex industrial systems, smart structures, aero-engine fault diagnosis and wind turbine system monitoring. Dr. Chen was a recipient of the National Excellent Doctoral Dissertation of China in
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Ruqiang Yan (
[email protected]) received his Ph.D. degree from the University of Massachusetts Amherst in 2007. He was a Guest Researcher at the National Institute of Standards and Technology (NIST) from 2006 to 2008. He joined the School of Instrument Science and Engineering at the Southeast University, China as a Full Professor in October 2009. He is also affiliated with the Collaborative Innovation Center of High-End Manufacturing Equipment at Xi’an Jiaotong University as a researcher. His research interests include nonlinear time-series analysis, multi-domain signal processing, and energy-efficient sensing and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Yanmeng Liu received her M.S. degree from Xi’an Jiaotong University, Xi’an, China, in 2015. She joined the Collaborative Innovation Center of High-End Manufacturing Equipment at Xi’an Jiaotong University, Xi’an, China in June 2015.
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