A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats
Abstract
:1. Introduction
Motivation and Organization of the Paper
- Incorporating feature-level contextual information in an intelligent way, adapting the so-called tandem approach widely used in speech recognition [42] to enhance the feature vector of the baseline system.
- Combining the outputs of different pattern classification processes, each of them using a combination of frequency-based and tandem features, exploiting different temporal ranges of contextual information.
2. Baseline System
2.1. Sensing System
2.2. Pattern Recognition System
- The machine + activity identification mode identifies the machine and the activity that the machine is conducting along the pipeline.
- The threat detection mode directly identifies if the activity is an actual threat for the pipeline or not.
- Feature extraction, which reduces the high-dimensionality of the signals acquired with the DAS system to a more informative and discriminative set of features.
- Feature vector normalization, which compensates for variabilities in the signal acquisition process and the sensed locations.
- Pattern classification, which classifies the acoustic signal into a set of predefined classes (using a set of signal models, GMMs, previously trained from a labeled signal database).
3. Novel Pipeline Integrity Threat Detection System
3.1. Contextual Feature Extraction
3.1.1. Posterior Probability Vector Computation
3.1.2. Tandem Feature Vector Building
3.2. Decision Combination
3.2.1. Sum Method
3.2.2. Product Method
3.2.3. Maximum Method
4. Experimental Procedure
4.1. Database Description
4.2. System Configuration
4.3. Evaluation Strategy
4.4. Evaluation Metrics
5. Experimental Results
5.1. Preliminary Experiments
5.2. Contextual Feature Extraction
5.3. Decision Combination
5.3.1. Machine + Activity Identification Mode
5.3.2. Threat Detection Mode
6. Conclusions and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Machine | Activity | Duration (in Seconds) | Threat/Non-Threat | ||||||
---|---|---|---|---|---|---|---|---|---|
LOC1 | LOC2 | LOC3 | LOC4 | LOC5 | LOC6 | Total | |||
Big excavator | Moving along the ground | 1100 | 1100 | 3540 | 1740 | 1620 | 4160 | 13,260 | Non-threat |
Hitting the ground | 120 | 140 | 240 | 220 | 80 | 260 | 1060 | Threat | |
Scrapping the ground | 460 | 460 | 920 | 620 | 200 | 580 | 3240 | Threat | |
Small excavator | Moving along the ground | 600 | 500 | 1700 | 820 | 820 | 1660 | 6100 | Non-threat |
Hitting the ground | 200 | 180 | 220 | 220 | 80 | 240 | 1140 | Threat | |
Scrapping the ground | 420 | 340 | 780 | 360 | 180 | 520 | 2600 | Threat | |
Pneumatic hammer | Compacting ground | 660 | 0 | 580 | 1320 | 0 | 1320 | 3880 | Non-threat |
Plate compactor | Compacting ground | 740 | 0 | 740 | 1240 | 0 | 1680 | 4400 | Non-threat |
Window Size | Machine + Activity Identification | ||||||||
---|---|---|---|---|---|---|---|---|---|
Big Excavator | Small Excavator | Pneumatic Hammer | Plate Compactor | Acc. | |||||
Mov. | Hit. | Scrap. | Mov. | Hit. | Scrap. | Compact. | Compact. | ||
Baseline [22] | |||||||||
Short | |||||||||
Medium | |||||||||
Long |
Window Size | Machine + Activity Identification | Threat Detection | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Big Excavator | Small Excavator | Pneumatic Hammer | Plate Compactor | Acc. | TDR | FAR | Acc. | |||||
Mov. | Hit. | Scrap. | Mov. | Hit. | Scrap. | Compact. | Compact. | |||||
Baseline [22] | ||||||||||||
Short | ||||||||||||
Medium | ||||||||||||
Long |
Method | Machine + Activity Identification | Threat Detection | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Big Excavator | Small Excavator | Pneumatic Hammer | Plate Compactor | Acc. | TDR | FAR | Acc. | ||||||
Mov. | Hit. | Scrap. | Mov. | Hit. | Scrap. | Compact. | Compact. | ||||||
Baseline [22] | |||||||||||||
Prod | S-M | 59.9% | 19.4% | 36.3% | 60.4% | 13.0% | 33.8% | 75.8% | 44.4% | 53.06% | 76.8% | 33.2% | 69.10% |
S-L | 64.3% | 23.7% | 32.1% | 57.7% | 18.0% | 31.1% | 80.4% | 40.1% | 53.91% | 74.9% | 33.7% | 68.25% | |
M-L | 66.1% | 22.2% | 33.7% | 57.9% | 14.3% | 36.6% | 78.4% | 41.3% | 54.92% | 73.9% | 32.0% | 69.32% | |
S-M-L | 61.5% | 24.0% | 34.0% | 57.6% | 15.0% | 36.9% | 78.2% | 39.8% | 53.09% | 75.0% | 33.2% | 68.68% | |
Max | S-M | 67.3% | 17.3% | 36.9% | 64.2% | 9.7% | 27.2% | 79.5% | 56.6% | 57.75% | 81.0% | 36.2% | 67.66% |
S-L | 76.8% | 17.2% | 32.1% | 62.9% | 10.9% | 29.4% | 81.1% | 50.0% | 60.20% | 79.7% | 35.0% | 68.29% | |
M-L | 76.6% | 14.8% | 34.2% | 64.1% | 11.5% | 29.2% | 80.1% | 49.9% | 60.33% | 78.4% | 33.4% | 69.24% | |
S-M-L | 77.0% | 14.5% | 34.0% | 65.0% | 10.0% | 27.8% | 81.7% | 51.4% | 60.82% | 81.1% | 35.4% | 68.34% |
Recognized Class | ||||||||||||
Big Excavator | Small Excavator | Pneumatic Hammer | Plate Compactor | | ||||||||
Moving | Hitting | Scrapping | Moving | Hitting | Scrapping | Compacting | Compacting | |||||
Real class | Big excavator | Moving | 66.09 | |||||||||
Hitting | 30.60 | 22.15 | 19.21 | |||||||||
Scrapping | 24.64 | 33.74 | 18.39 | |||||||||
Small excavator | Moving | 57.91 | 16.92 | |||||||||
Hitting | 17.03 | 14.01 | 14.32 | 29.55 | ||||||||
Scrapping | 15.55 | 12.62 | 36.57 | |||||||||
Pneumatic hammer | Compacting | 78.38 | ||||||||||
Plate Compactor | Compacting | 14.24 | 16.29 | 41.28 |
Big Excavator | Small Excavator | Pneumatic Hammer | Plate Compactor | Averages | |||||
---|---|---|---|---|---|---|---|---|---|
Moving | Hitting | Scrapping | Moving | Hitting | Scrapping | Compacting | Compacting | ||
Baseline | |||||||||
Novel | |||||||||
Relative improvement | 34.74% | 10.14% | 29.62% | 12.89% | 3.92% | 21.01% | 9.10% | 4.48% | 21.30% |
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Tejedor, J.; Macias-Guarasa, J.; Martins, H.F.; Piote, D.; Pastor-Graells, J.; Martin-Lopez, S.; Corredera, P.; Gonzalez-Herraez, M. A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats. Sensors 2017, 17, 355. https://doi.org/10.3390/s17020355
Tejedor J, Macias-Guarasa J, Martins HF, Piote D, Pastor-Graells J, Martin-Lopez S, Corredera P, Gonzalez-Herraez M. A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats. Sensors. 2017; 17(2):355. https://doi.org/10.3390/s17020355
Chicago/Turabian StyleTejedor, Javier, Javier Macias-Guarasa, Hugo F. Martins, Daniel Piote, Juan Pastor-Graells, Sonia Martin-Lopez, Pedro Corredera, and Miguel Gonzalez-Herraez. 2017. "A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats" Sensors 17, no. 2: 355. https://doi.org/10.3390/s17020355
APA StyleTejedor, J., Macias-Guarasa, J., Martins, H. F., Piote, D., Pastor-Graells, J., Martin-Lopez, S., Corredera, P., & Gonzalez-Herraez, M. (2017). A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats. Sensors, 17(2), 355. https://doi.org/10.3390/s17020355