A Bridge Health Monitoring System Based on NI Hardware and Software

Based on NI data acquisition hardware and related software, you can set up a bridge health monitoring system. This system not only meets the functionality requirements of the monitoring task, but also endures the severe environmental conditions that a bridge usually faces. This article discusses the fundamentals of structure health monitoring (SHM) and describes how the Shanghai JUST ONE Technology company implements SHM on the Donghai Bridge, China's first sea-crossing bridge.

Introduction

SHM is a standard engineering practice today for a variety of structures in many sectors.

For bridges, SHM is of central importance. The major objective of bridge health monitoring is to identify damages or deterioration. Bridge health monitoring provides quantitative data of the bridge, and the data can be used for additional purposes as well. For example, the data can be used for accessing extent of damages/deterioration, evaluating the structural performance, responding to unexpected accidents, performing repair or strengthening and managing the bridge's normal operations. The data can also be employed for research purposes to improve bridge design and construction technologies.

Bridge health monitoring has some specific characteristics, such as the following:

• A bridge is a geographically distributed system, which is usually several to tens of kilometers in length.
• Bridges are usually working under rough environments and the data are gathered while the bridge is working.
• The monitoring is a long-term process and certainly a remote process, preferred with minimized on-site maintenance because of both the geographical distance and the environmental hostility.
• The monitoring is a continuous, real-time process, in which huge amounts of data are gathered. Smart techniques need to be employed to obtain characterizing information for evaluation from these data.

These characteristics establish a set of challenges that need to be addressed in the development of the bridge health monitoring system.

Donghai Bridge and the Monitoring Challenges

Three and a half years of construction establishes China's first sea-crossing bridge, Donghai Bridge. Stretching across the East China Sea, the graceful cable-stay structure connects Shanghai to Yangshan Island. The bridge has a full length of 32.50 km, a 25.32 km portion of which is above water. The main navigation span, which is 420 m, has a navigation capacity of 5000 t and a navigation height of 40 m. Obviously, the monitoring system for Donghai Bridge is of large scale with a variety of quantities to be monitored and transmitted.

Donghai Bridge - Shanghai/Yangshan, China

The wide geographical area that the bridge occupies separates the sensors with long inter-distances. Thus, the real-time requirements inherent with many of the measuring items call for certain advanced synchronization technique, one that works over a large geographical area. The traditional method by sharing sample clock signal via coaxial cables is no longer feasible. Global Positioning System (GPS) time synchronization, which requires no direct connection between the measurement subsystems, is ideal for this situation.

Situated into China East Sea, Donghai Bridge has to endure the erosion of the seawater, the impact of typhoons and earthquakes, and the gradual damages caused by the traffics on the bridge. So the measuring system must work under hostile environmental conditions with endurance. Also, because the monitoring is a long-term activity, the measuring system should be extremely reliable with minimum maintenance. These requirements are imposed on the whole measurement and data acquisition system.

Given the scale of the monitoring system of Donghai Bridge, easy-to-use high level software that manages the whole system is a necessity. Also, it will be highly desirable if some of the most defining parameters of the bridges can be extracted on-line from the huge amount of data gathered in real-time. These parameters are important quantities that you cannot see directly from the raw data. Doubtless to say, a software tool fulfilling this purpose will be of great help.

In summary, the problems confronted in the bridge monitoring system call for a set of hardware and software with special requirements imposed on them.

NI products - Ideal for the Solution of Bridge Health Monitoring System

Bridge Monitoring System at a Glance

The items that need to be monitored for an operational bridge are basically coming from three categories: 1) Environments: Donghai Bridge is subject to rough environmental conditions -- violent wind, sea waves and a high concentration of Chloride ion. These factors have considerable effects on the functioning of the bridge. 2) Maintenance: from the bridge maintenance point of view, the sunk displacements of the piers and towers, the deformations of the spans, etc., are all important indicators that need to be monitored. 3) Accidents: as to the effects of the probable accidents happening to the bridge, the dynamical properties of the bridge are of central importance.

Overall, the major items that need to be monitored are as follows.

Environment monitoring:

• atmosphere temperature
• winds
• erosion of the Chloride ion
• waves

Static and dynamic response monitoring:        

• structure deformations
• structure stresses
• structure dynamics
• cable tensions
• displacements of dampers
• structure temperature

The measurement system for Donghai Bridge is spread out all over the bridge body. The bridge is divided into several segments, in each of which is settled a signal acquisition station. The distances between these stations are several to tens of kilometers. The overall data acquisition system is composed of these signal acquisition stations distributed at selected spots along the 32 km long bridge. Each station is hosting the sensors in its vicinity. The functionality of each station is as follows: data acquisition in a variety of formats in accordance with the type of sensors being connected, signal conditioning, data processing and management, data transmission, and so on.

Considering the hostile environment these stations are working in, the stations must endure, by no means inclusive, water, humidity, dust, shock, and chemical erosion especially by salts. Also, for obvious reasons, the stations must work with high robustness, reliability, and maintainability. Given the above requirements for the stations, there is not much thinking or argument going on before reaching the PXI-based solution. Actually, the PXI-based data acquisition solution is designed for rugged environment with superb reliability. With proper module selections, all of the functionality requirements of the stations can be satisfyingly met.

As a matter of fact, since the first launch, the data acquisition system built on Donghai Bridge has been working properly 7X24 for about two years, up to present.

To remotely configure, manage, and transmit the acquisitions is the requirement on the software side. LabVIEW suite is a set of software not only fulfilling this purpose but also making this process considerably convenient and highly efficient by seamlessly integrating with NI PXI modules and other related hardware including networks, and by employing proper abstractions and encapsulations.

Synchronization: GPS-based solution

As mentioned earlier in this document, one of the primary challenges in implementing the data acquisition system is the issue of synchronization, which has to be done over a large geographical area. The solution to this problem is to resort to the GPS timing signals. Each station is connected to a GPS receiver. The receiver receives the GPS synchronization signal, which is in turn sent to the PXI modules at the station. This GPS synchronization signal is used in data acquisition to ensure the proper synchronization of the overall distributed acquisition system.

GPS timing signal

The GPS consists of 24 satellites revolving around the earth every 12 hours. Each of these satellites has an atomic clock onboard with an accuracy of 10^{-13} seconds. The GPS satellites continuously transmit their coordinates in space along with a time message on a 1.5 GHz carrier frequency. In particular, the time message can be used to precisely correlate, trigger, and timestamp measurement data. There are typically two types (but three instances) of GPS timing signals out of a GPS receiver.

PPS (Pulse Per Second): the first type of signal supported is PPS. Most GPS receivers support PPS. A PPS signal does not contain information about the specific time of day or year; it only outputs a pulse once a second. The pulse width is generally 100 ms, but many GPS receivers allow the user to specify the pulse width, as long as it is less than 1 second. PPS is the simplest form of synchronization. A PPS signal might look like the following (Figure 1):

Figure 1: PPS signal

The one shown in Figure 1 is also called 1 PPS since it outputs one pulse per second. It is usually used as the trigger signal for acquisition. There is another instance of PPS type signal, namely, 10M PPS, which produces 10M pulses per second. This signal is usually used as the sample base frequency.

IRIG-B: the second type of signal supported is DC-level IRIG-B. IRIG, Inter-Regional Instrumentation Group, is an encoded transistor-transistor logic (TTL) signal carrying the absolute time. IRIG repeats or re-synchronizes every second. For IRIG-B, each frame is 1 second. Figure 2 is a timing diagram of the IRIG-B standard. Each bit is represented as a 10 ms period, with 0 being a 2 ms high, 1 being a 5 ms high, and the P bit being an 8 ms high. The P bit separates seconds from minutes, minutes from hours, and so on, within the 1-second frame.

Figure 2: IRIG-B signal

Synchronization for data acquisition

The acquisition system is composed of NI PXI 1045 18-slot chassis, NI PXI 8187 controller, NI PXI 6652, 6602, and 4472B modules. Figure 3 shows the schematic diagram of the acquisition system. The central piece is the dynamic signal acquisition (DSA) module, NI PXI 4472B, which is the actually unit to import and sample the measured data from the sensors. The other modules are used for NI PXI 4472B to work properly. The major challenge is the synchronization of the widely distributed NI PXI 4472B modules at multiple stations.

Figure 3: GPS PPS synchronization schematic

The NI PXI 6652 synchronization module is connected to the 10M PPS signal from the GPS receiver. This signal is sent to the PXI backplane after frequency division and then used as the oversample clock of NI PXI 4472B module.

The NI PXI 6602 counter receives simultaneously in two ports the 1 PPS signal from the GPS receiver. One is used as the SYNC pulse through the PXI backplane for NI PXI 4472B to synchronize the phases of acquired signals in all the NI PXI 4472B modules. The other is used as timer and compared with the pre-assigned acquisition time. When the acquisition time is reached, NI PXI 6602 generates the acquisition start trigger for NI PXI 4472B to start data acquisition, again through the PXI backplane.

The NI PXI 8187 controller reads the IRIG-B signal from the GPS receiver, and the absolute time can be obtained from the IRIG-B signal. The absolute time can then be used to timestamp the acquired samples.

Because the PPS signals are coming from the GPS, they are identical for all the NI PXI 4472B modules across all the stations. Synchronization of data acquisition for multiple NI PXI-4472B modules in a multichassis system over a large geographical area is thus accomplished.

The above configuration for synchronizing the data acquisition process over the whole bridge can be readily set by programming on NI software platform, in particular, NI-Sync for NI PXI 6652, LabVIEW DAQmx for NI PXI 4472B, 6602 and 8187.

Analysis for monitoring

The point of data acquisition is to mine useful information out of the acquired massive data. So, in addition to recording the raw data obtained through the data acquisition boards, which is important on its own right for obvious reasons, data processing and analyzing are also performed at various levels. For instance, at each station, the PXI controller (say, NI PXI 8187) does some basic statistical analysis on the data in addition to storing them. The data is further transmitted through networks from each station to a monitoring center. At the center, more complicated analysis techniques can be applied to the data. For instance, various signal analysis functions in LabVIEW and LabVIEW toolkits, such as the LabVIEW Advanced Signal Processing Toolkit and the LabVIEW System Identification Toolkit, can be applied to the data to obtain various types of information from different points of view. According to the real-timeness of the analysis methods, they can be generally classified into two categories: off-line methods and on-line methods. The type of method to use is depending on the monitoring task. The following sections discuss both methods in more details.

Off-line analysis of the measured data of the bridge

Multi-channel signal spectrum analysis: In addition to providing the FFT method, LabVIEW and LabVIEW toolkits provide standard multivariate signal processing algorithms. For example, the Advanced Signal Processing Toolkit provides the TSA MUSIC VI that you can use to obtain the spectrum of a multi-channel signal.  Figure 4 shows an instance of the resulting spectrum graph of Donghai Bridge.

Figure 4: Spectrum of Donghai Bridge by MUSIC

Modal analysis of the bridge: Modal analysis methods can be used to reflect the dynamic properties of the bridge. In fact, modal analysis is a standard engineering practice in today's SHM. In particular, the modal parameters---resonance frequencies, damping ratios, and mode shapes are calculated in the process of modal analysis. Conventionally, the modal parameters are obtained from the stimulus signal explicitly applied by the tester and the corresponding response signal.

To cope with the modal analysis on large structures like bridges, however, a relatively new type of modal analysis method has been developed, which works with the data gathered at the same time the structure being analyzed is working. This is operational modal analysis. In this method, no explicit stimulus signal is applied to the structure; rather, the natural forces from the environment and the work load applied to the structure serve as the stimulus, which are random and unknown. Only the signals measured by the sensors put on the structure can be obtained and used, which serve as the response signals. Thus, operational modal analysis is also called an ”output-only" method. This is ideal for bridge modal analysis since obviously, it is not so easy to apply any types of artificial stimulus to an object that is several to tens of kilometers long.

Within the operational modal analysis domain, there is a type of method that employs output-only system identification (or in other terms, time series analysis) techniques, namely, stochastic subspace identification (SSI). This type of method uses the output data of the system, which is here the sensor-measured data of the bridge, to identify a linear state-space model that best describes the observed output data. Then, in operational modal analysis context, the state transition matrix of the linear model is used to obtain the modal parameters.

The Advanced Signal Processing Toolkit provides the TSA Stochastic State-Space Modeling VI, which implements the SSI algorithm. Using this VI, the modal parameters can be computed with ease. An example of the modal analysis of Donghai Bridge by this method is shown as follows: Table 1 shows the resonance frequencies and their corresponding damping ratios; Figure 5 shows the mode shapes of the first several significant vibration modes of Donghai Bridge (main span only) and Figure 6 shows the polar plots of mode shapes of Donghai Bridge identified by the SSI method, which reveals that almost all of the modes presented here are nearly classically damped. Note that the resonance frequencies identified by this method are consistent with those obtained by some other methods shown in Table 2 right before the Conclusion section.

 

Ordinal	Resonance frequency (Hz)	Damping ratio (%)
1	0.367	0.4291
2	0.506	1.1193
3	0.635	0.3599
4	0.779	0.6237
5	1.037	0.7674
6	1.152	0.7758
7	1.262	1.3674
8	1.379	1.1622
9	1.844	0.3115

Table 1: Resonance frequencies and damping ratios of Donghai Bridge by off-line SSI method

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Figure 5: Mode shapes identified from the SSI algorithm of Donghai Bridge (main span)

Figure 6: Polar plots of the mode shapes identified from the SSI algorithm of Donghai
Bridge (main span)

On-line monitoring of resonance frequencies of the bridge

Furthermore, in order to monitor a bridge's health status better, some informative quantities are needed to be tracked in real-time. In particular, the resonance frequencies are highly desired to be monitored in real-time. The challenge now is to do resonance frequency calculation on-line, which is a topic of current research for applications in a wide range.

To enable SSI methods to be working on-line, SSI needs to be reformulated to some sort of recursive fashion so as to reach the necessary computational efficiency. This is recursive stochastic subspace identification (RSSI). With RSSI, the sampled data from multi-channel are read and possibly decimated. The decimated data then are fed to the RSSI algorithm. Each time a new decimated data sample is fed in, a new set of resonance frequencies of the system under investigation are produced out. That is, the resonance frequencies are updated as the data acquisition process goes on. If the RSSI algorithm is fast enough, this updating procedure is running in real-time.

In the Donghai Bridge instance, acceleration signals from tens of accelerometers mainly residing around the main navigation span are used to identify the corresponding resonance frequencies. The well-synchronized acceleration data from these accelerometers are timestamped at each station and then transmitted over network to the monitoring center. A LabVIEW VI can receive these multi-channel data to form a 2D-array. This multi-channel signal is then decimated to a reasonable rate for accommodating the targeting resonance frequency range. After this, for identification effect and computation speed, the decimated signal is simultaneously filtered into several properly chosen sub frequency bands, in each of which an RSSI algorithm is applied to the filtered signal. After an initialization procedure of the RSSI algorithm, the resulting frequencies are produced over time, forming frequency-time curves, and the speed of computation is managed to be fast enough. Thus, the resonance frequencies of interest of the bridge are successfully being tracked on-line. Figure 7 shows this process schematically.

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Figure 7: On-line modal frequency monitoring schematic diagram

Figure 8 shows one instance of the resulting frequency-time curves of Donghai Bridge. From this figure, it is clear that after a period of initialization, the frequency-time curves are being produced; and after a settling stage, the curves become very stable over time. (One thing worth of a clarification is that the curves on Figure 8 are obtained by gluing several sets of resulting curves, each produced within a sub-band mentioned above by an individual RSSI VI.) A comparison between these identified resonance frequencies (Figure 8) and those obtained by some other off-line methods (Table 2) reveals that they agree fairly well at most of the frequencies. Although further experiments need to be performed to validate the RSSI method, the results so far have shown feasibility and effectiveness of this method under the real-time requirement. With this method, the important resonance frequencies of the bridge can be tracked in real-time, which is necessary for better bridge health monitoring solutions.

Figure 8: On-line tracked resonance frequencies of Donghai Bridge

 

Ordinal Number	Resonance Frequency (Hz)
	Tester1	Tester2	Theoretical
1	0.368	0.365	0.361
2	0.434		0.436
3	0.507	0.5096	0.505
4	0.637	0.6419	0.606
5	0.767	0.7791	0.897
6	0.787	0.7910	0.759
7	1.040	1.0498	0.957
8	1.232	1.2305	1.042
9	1.127	1.1172	1.199
10	1.386	1.3818	1.219
11	1.837	1.7871	1.805
12	2.452	2.0752	1.813

Table 2: Referred resonance frequencies obtained by different methods

Conclusion

Developing a bridge health monitoring system is a challenging task. NI hardware and software can make this process much smoother. Further, the analysis software LabVIEW provides can render the process of health monitoring more effective and informative.

References

[1] National Instruments, the LabVIEW Help.

[2] National Instruments, NI-Sync User Manual.

[3] 詹永麟. 基于GPS技术实现分布式数据同步采集系统. 上海巨一科技发展有限公司 (Shanghai JUST ONE Technology).

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