1

Development of an Embedded High Performance MEMS-based Fiber Bragg Grating Interrogation System for Wind Turbine

Applications*

Bill Allan (wrallan@ucdavis.edu) David A. Horsley (dahorsley@ucdavis.edu)

Department of Mechanical and Aeronautical Engineering, University of California, Davis Davis, CA 95616 USA

Jose R. Zayas (jrzayas@sandia.gov) Φ

Wind Energy Technology Division Sandia National Laboratories ξ Albuquerque, NM 87185-0708

Abstract

As wind turbines become larger, there is an increased need for real time sensors on wind turbine blades for both structural health monitoring and active load control. This is becoming increasingly important as a growing number of turbines are installed offshore, where unplanned maintenance can be quite costly. Optical strain sensors provide numerous advantages in comparison to conventional resistive strain gauges, including resistance to corrosion and electro magnetic interference (EMI) and efficient multiplexing of multiple sensors onto a common detection channel. In addition, optical fibers can also be embedded in the composite during the manufacturing process, making them mechanically robust and reliable. Fiber Bragg Grating (FBG) sensors are particularly attractive, since the sensor consists of an interference grating which is optically written into the fiber, maintaining the fiber’s mechanical integrity. Because the manufacturing process requires no manual sensor assembly or specialized packaging, the individual sensor elements have the potential of being cost competitive with conventional resistive strain gauges. Although these sensors are relatively well-established, they have seen little use in practical applications because of the high cost of existing interrogation systems, which currently exceed $10,000 per unit. Unlike some applications where the cost of the detection unit can be distributed over a large number of sensor elements, wind turbine blades typically only require a small number of sensors. As a result, fiber optic sensors have been significantly more expensive than traditional resistive strain gauges. To address this problem, a low-cost detection system based on a MEMS tunable filter has been developed. An off-the-shelf MEMS-based tunable Fabry-Perot optical filter is controlled with a real-time embedded digital signal processor to manage calibration, scanning, signal processing and communications with a host system. Broadband illumination produced by a single superluminescent diode passes through the tunable MEMS filter which is scanned across a broad wavelength range to interrogate each sensor. Light transmitted through the FBG sensors is sensed by a photodiode, and is correlated with calibration data to determine the local strain experienced by each sensor. The interrogation technique provides fast scanning rates for each sensor and can accommodate over 15 sensors on a signal fiber cable. The proposed sensing system is expected to yield microstrain-order resolution, and high sampling rates in a compact and rugged package. Preliminary system testing results indicate excellent response linearity and promising sampling rates.

* This paper is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Φ Wind Energy Technology Department, MS-0708. ξ Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin company, for the U.S. Department of Energy under contract DE-AC04-94AL85000.

44th AIAA Aerospace Sciences Meeting and Exhibit9 - 12 January 2006, Reno, Nevada

AIAA 2006-606

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.

2

Introduction A fiber Bragg grating (FBG) interrogation system based on a MEMS tunable filter for wind turbine applications has been developed (Figure 1). A FBG strain sensor consists of a series of permanent index of refraction modulations within a fiber optic cable [1]. The grating performs as an optical bandpass filter, where the grating pitch is related linearly to the bandpass center wavelength. Narrow band transmission on the order of 0.1nm full-width at half maximum (FWHM) and typical strain sensitivity of 1pm/µε (in units of wavelength shift per microstrain) characterize these sensors.

A key advantage of FBG strain sensor is the good sensitivity and noise immunity. An optical interrogation system, however, is required to measure shifts in center wavelength. Commercial off-the-shelf (COTS) interrogation systems typically rely on fixed optical filters (one per sensor) or integrated monochromators using opto-mechanical subassemblies. The former approach offers high sampling rates but requires high-cost custom filters corresponding to each sensor, while the latter is typically constrained in sampling rate due to mechanical limitations.

A conceptual ‘middle ground’ is proposed, where a tunable optical filter (TF) based on micro electromechanical systems (MEMS) technology is employed. The proposed interrogation system, controlled by embedded digital signal processing (DSP) hardware, promises scanning rates at up to 100Hz per sensor, resolution on the order of 1pm/µε, accommodation of over 15 sensors per fiber optic cable using a wavelength division multiplexed (WDM) architecture, and an order-of-magnitude decrease in overall system cost when compared with COTS interrogators.

Figure 1. Block diagram of optical strain sensing system.

Wind Turbine Application Wind turbine blade (WTB) structures undergo constant cyclic loading and can develop cracks, or delamination that may lead to complete structural failure over time [5]. At this time, most commercial wind turbine blades being manufactured do not incorporate any sensors on the blade. This is primarily due to either the prohibitive cost of existing FBG interrogation systems, or the short life expectancy of traditional sensors such as resistive strain gauges. As wind turbine blades increase in size and become potentially more compliant, realtime information on blade response can used for both control input or structural health monitoring. There are three bending modes that can be used for control inputs – namely flap bending, edge bending and twist – all of which can be resolved by using fiber optic strain sensors (Figure 2). The FBG strain sensor is an ideal device to detect these modes due to its high sensitivity and compatibility with composite structures. Structural health monitoring is becoming a critical area of research, especially as they move forward to offshore applications, where operations and maintenance (O&M) costs are much larger.

Existing off-the-shelf interrogation systems do not offer the appropriate combination of reasonable sampling rates per sensor, durability, field installation capability, and low cost necessary to deploy FBG sensing technology on production turbines. The low number of sensors typically needed on the rotating

3

frame makes the existing interrogation system infeasible for manufacturers to incorporate on each turbine at a cost effective price.

“Edge Bending”

“Flap Bending” “Twist”

Blade Structure

Fiberglass I-Beam

Figure 2. Typical bending modes near the shear web and spar cap.

Fiber Optic Interrogation System Architecture Three major hardware layers compose the proposed interrogation system physical architecture. The optical layer contains the tunable filter, the sensors, and the optoelectronic devices. The interface layer contains the signal conditioning, the amplification, and the data conversion devices. The controller layer is essentially the digital signal processor and related peripheral electronics. Note that all signal conditioning, interrogation, signal processing and communications are performed within the system in real-time.

Optical Layer

A broadband superluminescent diode (SLD) [7] provides 10mW of infrared emission over the 1500nm to 1600nm (C-band) wavelength range. The device requires a constant-current power source and integrates a thermoelectric cooler (TEC) device and feedback thermistor in the package for active temperature regulation. Accordingly, an external temperature controller is also required. The system is designed for a single fiber optic cable with embedded FBG sensors. Figure 3 illustrates the FBG used in a reflective mode.

Figure 3. Schematic of Optical layer.

The tunable filter (Nortel MT-15-025) is a COTS MEMS Fabry-Perot etalon with a confocal feature,

where one side of the etalon is concave to reduce insertion loss. The device is used as a scanner to detect the optical spectrum reflected or transmitted by each FBG sensor. The device has an optical passband of 0.1 nm FWHM, an insertion loss of 3 dB, a natural mechanical resonance of 140kHz [2],

4

and a tuning range of 1525nm to 1595 nm. Thus it is suitable for a variety of relatively high-velocity scanning techniques spanning the entire tuning range or just over a single sensor or set of sensors; the particular algorithm implemented is discussed later. Tuning is accomplished electrostatically by setting a tuning voltage between 0 and 25V on the device, though the filter response to tuning voltage is very nonlinear and must be compensated for in the controller.

The light that passes through the MEMS TF is reflected off each FBG sensor and detected using a broadband photodiode. It is important to note that, although some broadening of the sensor spectral response will be evident due to the nonzero linewidth of the filter’s passband, this does not affect the strain sensing algorithm resolution or accuracy.

A limited number of sensors can fit within the 70nm tuning range. Assuming a ±2000µε strain range, the center wavelength of each sensor will shift by ±2nm. This limits the system to 17 multiplexed sensors can be scanned per fiber, which is more than enough for the wind turbine application. Additional sensors could be added by splitting the broadband light to additional fibers and recombining the light from these fibers at the input of the MEMS TF, reducing the signal-to-noise ratio due to the addition of multiple 3 dB couplers.

Interface Layer

The interface layer is responsible for the data conversion, amplification and signal conditioning necessary to interface the optical layer with the controller (Figure 4).

A 16-bit, 2.5V digital-to-analog converter (DAC) is used to set the TF tuning voltage. Amplifying the DAC to a 25V rail (10x), the DAC resolution is 0.38mV per least-significant bit (LSB). Using a linear approximation for the TF wavelength span gives approximately 70nm range over 25V or 2.8nm/V, and the TF sensitivity is then approximately 1pm/LSB or 1µε/LSB. Thus in a noiseless system the controller would be able to resolve approximately 1µε resolution. However, the TF tuning curve varies widely from device to device which affects overall system resolution.

As the SLD intensity and TF center wavelength drift significantly with temperature, active temperature regulation of both devices is required. An H-bridge amplifier is used to switch power to the TEC on each device by amplifying two pulse-width modulated (PWM) signals generated from the controller. A closed-loop controller is implemented in software using a thermistor. Signal conditioning converts the change in resistance of the thermistor to an analog signal suitable for the controller’s ADC inputs.

Optical

H-BRIDGEAMP

THERMA

Controller

TEC A

THERMAMP

16-BITDAC

PWM AIN SPIx 2

SIGCOND

AIN

T.O.F.PHOTODIODE

COMMXCVR

Figure 4. Schematic of the interface layer.

Signal conditioning for the photodiode is a simple low-noise transimpedance amplifier circuit. An integrated photodiode / amplifier device was used for the purposes of testing. Finally, a transceiver is required for both RS-232 and controller area network (CAN) communications protocols discussed in the next section.

5

Controller Layer

The controller layer implements the DSP hardware and firmware. The embedded DSP chosen for this project is the Texas Instruments TMS320F2812 32-bit DSP running at 150MHz. The chip has typical microcontroller features such as integrated Flash memory and RAM, onboard communication, and sampling and signal generation peripherals.

Figure 5 shows silicon hardware (gray), software drivers (white) and the firmware application (yellow). Hardware shown in the diagram is integrated in the DSP silicon. The hardware and peripheral drivers were developed for this application, and the DSP/BIOS real-time operating system (RTOS) was bundled with the DSP from Texas Instruments. Each yellow block represents an RTOS task in parallel.

Two communications peripherals – the CAN and serial communications interface (SCI) – are implemented on the controller. The CAN protocol uses a bus architecture with a high level of noise immunity and fault tolerance, and is the preferred method of transmitting data to a host system. The SCI hardware implements the RS-232 protocol and is available to present a technician or user with a calibration interface and/or redundant data acquisition output.

The serial peripheral interface (SPI) is used to communicate with the 16-bit DAC hardware. While a parallel interface is capable of running DAC hardware at faster data rates, the SPI hardware is simple to implement and reduces hardware complexity and circuit footprint size.

The analog-to-digital conversion (ADC) hardware provides a multi-channel 12-bit analog measurement function. Conversion time for the 12-bit sample is 80ns per channel. With this fast conversion time, oversampling is a viable technique for increased analog resolution.

Figure 5. Schematic of the controller layer.

The PWM peripheral is used to generate the pulsed power signal for the H-bridge amplifiers driving the TEC devices. The signal is generated in hardware so no software overhead is required to maintain a given PWM value.

Finally, the general purpose I/O (GPIO) hardware is used for support functions, including driving indicator LEDs and providing chip-select functionality on the SPI bus.

Interrogation Strategy The proposed system relies heavily on DSP technology. In all DSP applications the critical constraint is the multiply-accumulate (MAC) instruction speed, as the bulk of all DSP algorithms are composed primarily of this instruction. Accordingly, the processor selected for this design includes a fast MAC instruction cycle time of one clock cycle or 6.6ns.

6

Correlation

Finding one signal within another, delayed by some time, is a common problem in signal processing for which there is an optimal solution. Correlation is the processing method for identifying areas within one signal that are the best match to a second signal. In this application, a window is defined around the expected center wavelength of each FBG sensor. The deviation in wavelength and therefore strain can be determined by correlating a stored version of the waveform captured at calibration time with subsequent ‘live’ captures.

In practice, the algorithm takes an input signal x[] of size N, a correlation kernel h[] of size M, and an output y[] of size N. The correlation algorithm executes as

∑=

+⋅=M

m

miymhiy0

][][][ (1)

for each sample i in y[]. Computing each element of y[] requires M MAC instructions, and accordingly a single correlation requires approximately O(N·M) MAC instructions. Some end condition issues exist, particularly when i approaches N such that y[i+m] exceeds the bounds of y[] for M > 0. This condition is handled in software to prevent unnecessary computation (zero padding) while respecting the bounds of each array.

Calibrating the Sensors

An initial calibration is used to capture the optical spectrum transmitted through each FBG sensor. The calibration also serves to null out any initial wavelength shift in the sensors due to residual stress induced by the manufacturing process used to embed the sensor fibers into the blade. This procedure can be done in the field by orienting the blade in an unloaded state.

Initially, the number and spectral location of the sensors are unknown. To acquire a full window scan, the controller directs the TF to scan at each wavelength by setting the tuning voltage with the DAC hardware. At each DAC value, (65,536 values if the full window span is used) the controller waits for mechanical settling of the TF and samples the photodiode response at the current wavelength. The array of sampled responses contains a snapshot of each sensor’s spectral response. By executing a simple peak-finding algorithm the full-width half-maximum (FWHM) values are found. Each sensor’s calibrated center wavelength is defined as the midpoint of the sensor’s FWHM, and the calibrated center wavelength is stored in the controller memory.

The correlation kernel is captured during this calibration routine. A typical FBG sensor exhibits a 0.3nm FWHM, in which 300 samples can fit. The kernel is therefore defined to be 256 samples long. Each sensor’s kernel is captured and stored for use during real-time correlations.

It is assumed that, during the calibration scan, the sensors are statically loaded at some known strain. Calibration values may be configured using an RS-232 user interface via some user-supplied serial terminal application.

Hardware Limitations

Digital-to-analog conversion is handled by a Texas Instruments DAC8831 DAC component, with 16-bit output resolution from 0 to 2.5V, 10nV/√Hz noise floor and a 1µs settling time. Analog sampling time is 80ns. If 4000 points are to be sampled, the absolute best performance bounds the scanning time to a 4.3ms minimum.

The SPI transfer time from the controller to the DAC at 10MHz using a 16-bit data word is approximately 1.6µs. Settling time for the DAC once a word is received is 1µs. With a 4000-point sample, the minimum time required is 10ms. Higher performance DAC hardware exists with settling times on the order of 10ns and parallel interfaces to eliminate both the settling time and data transfer

7

times. The prototyped system uses a lower-performance DAC for simplicity in hardware design, though future hardware revisions are expected to integrate these advanced DAC devices.

Software Limitations

Correlation is the desired tool to detect shifts in a sensor’s center wavelength as the shape of the waveform remains relatively constant over the strain range. Given the 1µε/LSB scanning resolution and the desired strain range of ±2000µε, a scan window size N of approximately 4000 LSBs, or samples, is required. Considering the computational case encountered in this problem, where the kernel size M is approximately 300 samples wide, there are 1.2×106 MAC instructions required to perform the single correlation, consuming almost 8ms.

The fast Fourier transform (FFT) algorithm executes at O (N log2N). With a sample size of 4096 points, approximately 50,000 instructions are required for a total execution time of 0.3ms. However, further optimization can be achieved by implementing a real (non-complex) FFT algorithm, saving up to 30% of the total instructions executed time when compared with complex algorithms [4]. Optimization particular to the Texas Instruments DSP controller is possible, potentially doubling the performance of sequential MAC instructions as 16-bit analog samples compose the operand dataset.1

Limitations Summary and Optimization

Table 1 summarizes the timing performance of the correlation algorithm components and includes estimated potential for a high-performance system implementation.

Table 1. Interrogation strategy performance summary.

Prototype High Perform. DAC word transfer 1.6µs/samp 12ns/samp DAC settling 1.0µs/samp 10ns/samp2 Analog sampling 80ns/samp 80ns/samp Correlation alg. 8ms/sensor 250µs/sensor3 TOTAL 18.7ms/sensor 0.65ms/sensor 50Hz max 1.5kHz max

The table represents the best-case execution time based on a 150MHz DSP with a 6.6ns MAC instruction speed. With the current firmware implementation on the prototype system, the maximum sample rate is limited to approximately 50Hz total, or 10Hz per sensor using 5 sensors. However, by upgrading the DAC hardware and implementing an FFT-based correlation algorithm, the expected performance increase would bring up to a 100x increase in sampling rate with no other performance consequences. At this increased sampling rate of 1.5kHz, up to 15 sensors can be sampled at 100Hz each.

Further optimizations can be implemented to increase system performance with relatively small impact on overall system cost. Processor speed scales the DAC word transfer and correlation algorithm properties linearly; moving to a 300MHz DSP offers another 25% increase in sampling rate. It is also conceivable to implement an external programmable logic (FPGA-type device) to run the correlation algorithm. The FPGA, under direction of the DSP, captures analog data and receives a correlation kernel from the DSP. FFT algorithms can be implemented in programmable logic with tremendous parallelism, reducing the correlation algorithm time and potentially the analog sampling time greatly.

1 See http://www-s.ti.com/sc/psheets/spru430d/spru430d.pdf 2 Analog Devices AD9777 16-bit DAC 3 TI Test results of FFT convolution on 320F67xx DSP

8

Packaging and System Cost An important feature of the proposed interrogation system is the lack of bulky electromechanical actuation devices typically found in conventional opto-mechanical monochromators. Some mechanical shock sensitivity is expected from the tunable optical filter but is not expected to interfere with system performance under normal operation. With a ruggedized, shock-mounted enclosure, the system is suitable for long-term field deployment operation. Temperature effects are expected to be negligible because of the actively-cooled source and tuning devices, though careful attention will be paid to the analog amplifiers and signal conditioning to minimize errors from temperature drift.

Table 2 summarizes the hardware costs of the proposed interrogation system based on the developed prototype. Cost estimates are for single unit quantities, and are expected to decrease significantly for larger quantities.

Table 2. Estimated System Cost.

Component Manufacturer Cost Ea. TF Nortel Networks $300 SLD Inphenix $1000 DSP Texas Instruments $30 Hardware - $60 PCB & electronics - $150 TEC heat sink - $10 Assembly/Test - $60 TOTAL $1610

More than one off-the-shelf tunable optical filter exists, though the Nortel device offers the greatest rated tuning range. The SLD price could decrease significantly if ordered in large quantities. The DSP hardware, control electronics, connectors, enclosure and mechanical elements are not expected to cost more than $250. A heat sink is necessary as a heat reservoir for the TEC devices in both the SLD and TF packages. Creative packaging could mate the device packages directly to the enclosure to eliminate this part. Finally, significant assembly and test would be required per device due to the fiber optic cable management and any necessary connectorizing.

In all, a total hardware and assembly cost of $1600 is estimated. This marks an order of magnitude cost reduction over existing off-the-shelf systems with comparable performance specifications.

Results and Discussion A FBG sensor from SmartFibres (UK) with a center wavelength of 1568 nm was used to perform initial system characterization. The sensor fiber was fixed at two points, approximately 1 meter apart, with the embedded FBG sensor between the two constraints. One constraint was adjustable with a standard 10µm mechanical micrometer to induce strain on the fiber. The measurement system was configured to scan the center wavelength of the tunable filter at a rate of ∆λ/∆t = 67 nm/s. The resulting photodiode output voltage was sampled with a period ∆t = 8 µs, corresponding to a wavelength change of ∆λ = 0.5 pm. N = 1024 samples were collected over 8.2 ms, resulting in a wavelength span of λs = N × ∆λ = 0.47 nm. The specified sensitivity of the FBG to strain is 1.2 pm/µε, so this wavelength span allowed a maximum of 389 µε to be detected. This is not a limitation of the measurement system; larger strain values could be detected by increasing the wavelength span.

The optical signal recorded from the photodiode is illustrated in Figure 6 for strain values from 0 to 225 µε. This figure illustrates the sensor as it ‘appears’ to the DSP. Because the system leverages correlation, the shape of the reflected spectrum does not effect the measurement; the strain detection

9

algorithm will perform consistently so long as the waveform read by the DSP is consistent in shape among successive reads.

Figure 6. Captured FBG Waveforms Under Varying Strain.

The correlation calculation begins with a kernel composed of 256 samples of the FBG spectrum recorded at zero strain. For each successive scan, 1024 analog samples of the optical spectrum are taken and correlated with this kernel. The following three waveforms in Figure 7 illustrate the correlation data. The kernel (gray) is shifted along the scan data until the correlation value (blue) is at its peak. The sample index corresponding to the peak of the correlation result is stored and converted to a wavelength shift. The process is repeated for each scan. Note that the 32-bit value axis corresponds to the Correlation Result (blue) only.

The linearity and accuracy of the system were tested by inducing seven strain values and processing the interrogation system output, as illustrated in Figure 8. The rough horizontal lines illustrate the raw output from the interrogation system, which is post-processed to create a single averaged point per induced strain. These averaged points were fit linearly and are shown to be highly linear (R2 > 0.99). Furthermore, the sensitivity of the FBG sensor is found to be 1.12pm/µε, which differs slightly from the approximate value of 1.2 pm/µε specified by the manufacturer. This discrepancy can be calibrated out in a startup routine.

10

Figure 7. Internal Correlation Waveforms.

Basic algorithm performance was characterized during this test as well. The time required to collect each optical scan was 8.2 ms. The software filtering, correlation algorithm and peak finding algorithm consumed 50 ms per scan, however the algorithms were not optimized for speed and instead clarity of code for debugging. The algorithm was also slowed because of time required to output each internal waveform (2300 samples) to a PC for plotting at each scan. Efficiency gains when basic optimizations are applied to the correlation and related algorithms are expected to be significant.

Finally, the standard deviation in the raw output signal was on average approximately 4.2pm, and ranged from 2.2pm to 6.3pm over the seven samples taken. A number of factors contribute to this high noise level, including relatively high optical signal loss, poor noise shielding on the experimental setup, ground noise coupling from AC test equipment, and multiple analog amplifiers in series to boost the D/A and A/D signals. These effects should become much less pronounced in a second revision of the experimental setup.

Figure 8. System Response and Linearity.

11

Conclusion At the time of this paper, a subset of the proposed interrogation system had been assembled, and basic software algorithms have been developed and tested using a single embedded FBG. The results showed promise for both resolution and sampling rates when software optimizations are implemented. Manually induced strain on the FBG generated a very linear system output and was shown to closely match the expected FBG sensitivity.

The proposed interrogation system promises potential sampling rates of 100Hz per sensor and can accommodate up to 15 sensors. The DAC and ADC hardware provide an estimated 1µε resolution per sensor and accommodate ±2000µε range per sensor. With more advanced control system hardware and firmware it is possible to further increase sampling rates if necessary, though the current design promises to meet the needs of wind turbine blade field applications. Finally, the overall system cost was reduced to approximately $1600 in non-production quantities. This drastic cost decrease opens FBG sensor technology to a wide variety of previously cost-prohibitive applications, including wind turbine blade structural heath monitoring.

References [1] Kashyap, Raman. “Fiber Bragg Gratings.” Academic Press, San Diego 1999. [2] Mortensen, Ivan. First Results from LM Glasfiber’s Blade Monitoring System (BMS).

Presentation, EWEC London 2004. http://www.lmglasfiber.com [3] R. S. Tucker et al., “Thermal Noise and Radiation Pressure in MEMS Fabry-Perot Tunable

Filters,”IEEE J. Select. Topics Quantum Electronics, Vol 8, Feb 2002. [4] Smith, S. W., “Digital Signal Processing – A Practical Guide for Engineers and Scientists,”

Newnes Press, Boston 2003. [5] Sundaresan, M.J. et al., “Structural Health Monitoring Static Test of a Wind Turbine Blade.”

Intelligent Structures & Mechanisms Laboratory, Department of Mechanical Engineering, North Carolina A&T State University. National Renewable Energy Lab, 1999.

[6] Udd, Eric, “Fiber Optic Smart Structures.” John Wiley & Sons. New York, NY 1995. [7] Inphenix, Livermore, CA