structural health monitoring n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Structural Health Monitoring PowerPoint Presentation
Download Presentation
Structural Health Monitoring

Loading in 2 Seconds...

play fullscreen
1 / 52

Structural Health Monitoring - PowerPoint PPT Presentation


  • 167 Views
  • Uploaded on

Structural Health Monitoring. Sukun Kim, David Culler James Demmel, Gregory Fenves, S teve G laser Thomas Oberheim, Shamim Pakzad UC Berkeley. NEST Retreat – Jun 4, 2004. Structure Monitoring. Data Acquisition. Data Collection. Processing & Feedback. Overview.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Structural Health Monitoring' - elata


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
structural health monitoring

Structural Health Monitoring

Sukun Kim, David Culler

James Demmel, Gregory Fenves, Steve Glaser

Thomas Oberheim, Shamim Pakzad

UC Berkeley

NEST Retreat – Jun 4, 2004

structure monitoring
Structure Monitoring

Data Acquisition

Data Collection

Processing & Feedback

overview
Overview
  • Low cost structure monitoring - Monitor structure, and analyze the health of structure based on sensed data at low cost
    • For Golden Gate Bridge, monitor vibration of bridge, and detect unusual behavior by wind, earthquake, or local damage
  • Extend reach of Wireless Sensor Network in a different direction – high fidelity sampling
    • High accuracy, high frequency with low jitter, large amount of data
challenges
Challenges
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
accelerometer board
Accelerometer Board
  • Both accelerometers for two axis
  • Thermometer
  • 16bit ADC
highfrequencysampling

Sampling

Other job

Non-preemptible portion (atomic section)

Preemptible task portion

0μs

10μs

Jitter Histogram

HighFrequencySampling
large scale reliable transfer

Receiver

Sender

Open

Ack for

Open

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Ack for

Data

DONE

DONE

Large-scale Reliable Transfer
  • Explicit open handshake - Data description and size of cluster is sent as a transfer request
  • Data transfer is composed of multiple rounds. In each round, sender sends packets missing in the previous round
  • Tear-down is implicit
slide9

Throttle for data packet is fixed at 10 pkt/s

  • Optimal case: window size is infinite
  • For the case with window size 16, throughput is 88% of optimal case.
  • Considering loss rate of 3%, actual relative throughput is 91%, which is higher than 85% of channel utilization ratio. This is because control packets do not follow 10 packets/s.
status
Status
  • Measure acceleration from multiple boards synchronously
    • Sather tower
    • PowerBar building
  • Data is available on the web
signal processing and system identification
Signal Processing and System Identification
  • Signal Processing
    • Analog low-pass filter with threshold frequency 25Hz is used
    • Averaging is used. If noise follows Gaussian distribution, by averaging N numbers, noise decreases by a factor of sqrt(N)
  • System Identification
    • Identifying model of target system
    • By matching input to system and output from system, construct a mathematical system model (Box-Jenkins multi-input multi-output model)
conclusion

Large Scale

Earthquake

G

G

Accelerometer

variation

Acoustic

Noise

mG

mG

Traffic

Identification

Local Damage

Detection

Temperature

μG

μG

Nuclear Test

Detection

Gravity

Variation

nG

nG

Challenges versus Accuracy

Possible Applications versus Accuracy

Conclusion
  • New challenges are analyzed which are brought by structure monitoring to wireless sensor network
    • High accuracy accelerometer, high frequency sampling with low jitter, low-pass filter, averaging, large-scale reliable data collection
table of contents
Table of Contents
  • Overview
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
  • Conclusion
  • Challenges & Future Work
highfrequencysampling1
HighFrequencySampling
  • Made by David Gay
  • Up to 6.67KHz with 4 bytes sample
  • MicroTimer – Supports one timer, micro second level granularity
  • BufferLog – Has two buffers. One is filled up by upper layer application while the other buffer is written to flash memory as a background task
jitter test 1khz 5khz 6 67khz
Jitter Test (1KHz, 5KHz, 6.67KHz)
  • Peak to Peak is time to fill up buffer
  • Spiky portion is time to write buffer to flash
  • Can sample as long as the former is larger than the latter
jitter test histogram 1khz 5khz 6 67khz
Jitter Test Histogram(1KHz, 5KHz, 6.67KHz)
  • Jitter is within 10µs
  • Peak at 625ns – Wakeup time from sleep mode
jitter analysis

Sampling

Sample

Other job

Non-preemptible portion (atomic section)

Preemptible task portion

F(k2)

F(k3)

. . .

C

C+T(k1)

C+T(k2)

Jitter

Jitter Analysis
table of contents1
Table of Contents
  • Overview
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
  • Conclusion
  • Challenges & Future Work
large scale reliable data transfer
Large-scale Reliable Data Transfer
  • 4Byte of data and 4Byte of time stamp at 100Hz in 100 nodes, transfer 40pkt/s – Sample data for 5 minutes, and collect data for more than 5 hours!!!
  • Efficient and reliable data transfer is crucial
  • RAM to RAM one-hop transfer is implemented as a building block - LRX
lrx component continued

Receiver

Sender

Open

Ack for

Open

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Ack for

Data

DONE

DONE

LRX component (continued)
  • Explicit open handshake - Data description and size of cluster is sent as a transfer request
  • Data transfer is composed of multiple rounds. In each round, sender sends packets missing in the previous round
  • Tear-down is implicit
slide22

Throttle for data packet is fixed at 10 pkt/s

  • Optimal case: window size is infinite
  • For the case with window size 16, throughput is 88% of optimal case.
  • Considering loss rate of 3%, actual relative throughput is 91%, which is higher than 85% of channel utilization ratio. This is because control packets do not follow 10 packets/s.
channel utilization
Channel Utilization
  • LRX (data only) is the theoretical limit of LRX (when window size is infinite)
  • Usage LRX lowers channel utilization by 15%
table of contents2
Table of Contents
  • Overview
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
  • Conclusion
  • Challenges & Future Work
signal processing
Signal Processing
  • As an analog signal processing low-pass filter is used, which filters high frequency noise
  • For accelerometer board, low-pass filter with threshold frequency 25Hz is used. Then ADC should sample at frequency much higher than 50Hz by Nyquist theorem, and imperfect low-pass filter
  • As a digital signal processing, averaging is used. If noise follows Gaussian distribution, by averaging N numbers, noise decreases by a factor of sqrt(N)
system identification
System Identification
  • Identifying model of target system
  • By matching input to system and output from system, we can construct a mathematical system model.
  • Usual process is (1) fitting a general Box-Jenkins multi-input multi-output model to sampled data. (2) And natural frequencies, damping ratios and mode shape are then estimated using the estimated Box-Jenkins model.
  • Most part of system identification is under development on civil engineering side.
table of contents3
Table of Contents
  • Overview
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
  • Conclusion
  • Challenges & Future Work
conclusion1

Large Scale

Earthquake

G

G

Accelerometer

variation

Acoustic

Noise

mG

mG

Traffic

Identification

Local Damage

Detection

Temperature

μG

μG

Nuclear Test

Detection

Gravity

Variation

nG

nG

Challenges versus Accuracy

Possible Applications versus Accuracy

Conclusion
  • New challenges are analyzed which are brought by structure monitoring to wireless sensor network
    • High accuracy accelerometer, high frequency sampling with low jitter, low-pass filter, averaging, large-scale reliable data collection
table of contents4
Table of Contents
  • Overview
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
  • Conclusion
  • Challenges & Future Work
challenges future work
Challenges & Future Work
  • Calibrating acceleration value to temperature
  • Time synchronization – RBS, TPSN
  • To maximize utility of channel, we need to monitor channel quality (loss rate), and throttle packet injection rate accordingly
  • Using LRX as a building block, multi-hop data collection need be implemented
  • TASK
cost comparison
Cost Comparison
  • Conventional piezoelectric accelerometer with PC system costs $40,000
  • Budget for structure monitoring budget is $1,000,000 level
  • Wireless sensor network with MEM accelerometer costs $500
    • Cheaper by a factor of 100
shaking table test
Shaking Table Test
  • Silicon Design 1221L is more quite, but less sensitive to dynamic movement
noise floor test
Noise Floor Test
  • Blue – Seismic Vault
  • Red – McCone Hall
jitter analysis continued

Sample

F(k2)

F(k3)

. . .

C

C+T(k1)

C+T(k2)

Jitter

Jitter Analysis (continued)
  • T(i): execution time of atomic section i
  • X(i): a random variable uniformly distributed in [0, T(i)]
  • C: context switch time
  • F(i): frequency of occurrence of atomic section i
  • Assume that the probability of timer event occurring at any point in atomic section i is same, then jitter will follow C+X(i).
  • Since jitter distribution of every atomic section begins from C, the frequency is highest near C and decreases as moving farther. And frequency drop at C+T(i) by F(i), since atomic section i will not have any distribution beyond C+T(i).
  • Actually there is a peak at C, because when program is in preemptible section, it will immediately service timer event after context switch time C.
calculation of transfer timer
Calculation of Transfer Timer
  • Let us assume each node store 4Byte of data and 4Byte of time stamp at 100Hz. And assume there are 100 nodes, radio throughput is 1.2KB/s, and data is collected to one base station. If acceleration data worthy 5 minutes is collected, each node will transfer 240,000Bytes. 100 nodes will transfer 24,000,000Bytes. Since the end link to base station is a bottleneck, it will take more than 5 hours. We can see bandwidth is narrow compared to aggressive data sampling. Even if we alleviate this problem using multi-channel or multi-tier network, still we will be in short of bandwidth.
lrx component
LRX component
  • Transfers one data cluster, which is composed of several blocks.
  • One block fits into one packet, so the number of blocks is equal to window size.
  • Each data cluster has a data description. After looking at data description, receiver may deny data (receiver already has that data, or that data is not useful anymore).
slide41

Receiver

Sender

Open

Ack for

Open

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Ack for

Data

DONE

DONE

slide42

Receiver

Sender

Open

Open

Ack for

Open

Data

Block 1

Data

Block 2

slide43

Receiver

Sender

Open

Ack for

Open

Open

Ack for

Open

Data

Block 1

Data

Block 2

slide44

Receiver

Sender

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Ack for

Data

Data

Block 2

Ack for

Data

DONE

DONE

slide45

Receiver

Sender

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Ack for

Data

DONE

Data

Block 4

Ack for

Data

DONE

slide46

Receiver

Sender

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Data

Block 4

Ack for

Data

DONE

DONE

slide47

Receiver

Sender

Data

Block 1

Data

Block 2

Data

Block 3

Data

Block 4

Ack for

Data

Data

Block 2

Ack for

Data

Data

Block 3

why sender times out
Why Sender times out
  • There are two reasons why only sender times out and stimulate receiver for Ack. The first reason is shown in Figure 16. If sender doesn’t time out, for a receiver to make sure Ack is delivered to sender, receiver should get acknowledgement from sender for Ack itself. This is not good. So it is clear that sender should timeout. Given that sender times out, timeout of receiver makes no difference except that channel is wasted by unnecessary Ack from receiver. So timeout in only sender side is desirable. As a second reason, if receiver times out, in case like Figure 18 (if first Data after Ack is lost), second Data always collide with resent Ack of receiver. This is not a good phenomenon. Therefore, after sending last packet in each round, if acknowledgement does not come, sender sends the last packet in that round again to stimulate acknowledgement. However, this does not mean receiver has no timeout. Receiver waits sufficient amount of time, and if nothing happens, it regards the situation as a failure.
imperfect low pass filter

Amplitude

Frequency

Filtering threshold

Imperfect Low-pass Filter
time synchronization
Time Synchronization
  • Temporal jitter is handled by high frequency sampling component. Spatial jitter should be solved by time synchronization. ITP [8] is a time synchronization protocol widely used in Internet. In wireless sensor network, there were several studies. In RBS [9], synchronization is done among receivers, eliminating sender’s jitter in media access. TPSN [10] put time stamp after obtaining channel. This gives even better synchronization accuracy than RBS (10μs compared to 20μs). Still there is a source of jitter at receiver side. As we saw in jitter for sampling, handling interrupt by radio can be delayed by atomic section of other activity. As suggested in [10], putting time stamp at MAC layer in receiver side will eliminate this jitter.
table of contents5
Table of Contents
  • Overview
  • Data Acquisition
    • Accelerometer Board
    • High Frequency Sampling & Jitter
  • Data Collection
    • Large-scale Reliable Data Transfer
  • Signal processing & System Identification
  • Conclusion
  • Challenges & Future Work
acknowledgement
Acknowledgement
  • This work is supported, in part, by the National Science Foundation under Grant No. EIA-0122599.