Sensor Failure Detection in Road Tunnel Ventilation

April 24, 2012by

Nakahori, I, Sakaguchi, T., Mitani, A., and Vardy, A. E.Sohatsu Systems Laboratory Inc. University of Dundee

Outline of Presentation

1. Road Tunnel Ventilation Control System

2. Sensor Failures

3. Longitudinal Ventilation Models

4. Proposed Method of Sensor Failure Detection

5. Illustrative Example of Sensor Failure Detection

6. Conclusions

1. Road Tunnel Ventilation Control System

Fig. 1 Configuration of Inverter-driven Road Tunnel Ventilation Control System

- Instrument Panel: sensor signals from VI, CO, AV, Traffic Counter, Fire Detector, Emergency Situation Detector and etc. are gathered.- Control Panel: control SW determines the speed of the jet-fans. - Inverter Panel: electrical voltage with variable frequency is generated to actually drive the jet-fans.

2. Sensor Failures

2.1 Typical Failure in Traffic Counter (“TC”)- Loop detector TC performance going down due to wear and tear.- Laser detector TC performance going down due to wear and tear.- Video TC performance going down under bad weather conditionssuch as heavy rain and snow.

2.2 Typical Failure in Air Velocity (“AV”) Meter- Measurements affected by the air flow disturbance due to vehiclemovement in local AV meter.

- Measurements disrupted by traffic congestion in cross sectional AVmeter.

2.3 Typical Failure in Pollution Concentration (“VI”, “CO”)- Windows in VI meter affected by dust and contaminants.- Screen in CO meter catching dust.

3. Longitudinal Ventilation Models

Vr Vr

Ck-1 Ck Ck+1

1 k-1 k k+1…

Air Velocity Model- Vehicle piston force- Fan thrust- Natural wind- Tunnel resistance force

Pollution Concentration Model- Pollution generated by vehicles in box- Pollution coming in from neighboring box- Pollution move out to neighboring box

Traffic Flow (Input)-Vehicle location-Vehicle Velocity

Employs quasi-steady air velocity and pollution concentration models

4. Architecture of Proposed Sensor Failure Detection

TC AV VI CO

Measured Data

Predicted Data

Online Simulation ModuleTraffic, Air Velocity, Pollution Density

Parameters State Variables

CalibrationModule

Failure DetectionModule

XX

Fan

Three numerical modules and two data storages

4.1 Calibration

JET FANS

Effective Resistance Area of Large& Small Vehicles

Pollution Gas Emission ofLarge & Small Vehicles

ATL, ATS

μL, μS

Objective is to revise the parameter values that best fit the measured data set

Calibration processprovides quantitative statistical data about deviation of measured and predicted values.

4.2 Statistical Nature of Measured and Predicted Values

AV data were collected at Kawasaki Koro Tunnel, Japan.The Variation (= difference of measured and predicted values)is indicative of “normal distribution”.

AV predicted value m/s

AV measured value m

/s

y = 0,8855x - 0,1029

R² = 0,934

0

2

4

6

8

10

0 2 4 6 8 10

4.2 Statistical Nature of Measured and Predicted Values

VI data were collected at Kawasaki Koro Tunnel, Japan.The Variation (= difference of measured and predicted values)Is indicative of “normal distribution”.

VI m

easured value g/kg-air

VI predicted value g/kg-air

y = 0,9543x + 2,81

R² = 0,4012

70

80

90

100

70 80 90 100

5. Illustrative Example of Sensor Failure Detection

-20-10

010

20

12:00

14:00

16:00

18:00

20:00

22:00

00:00

ΔV

I [%

]

-10

-5

0

5

10Δ

AV

[m/s

] 3σ

3σ

The sensor failure is detected at time 01:00.

∆AV(=AVmeasured-AVpredicted); ∆VI (=VImeasured-VIpredicted)

Collected TC, AV and VI data from Kawasaki Koro Tunnel and run the Method

5. Illustrative Example of Sensor Failure Detection

5. Illustrative Example of Sensor Failure Detection

Pictures indicating maintenance team running “unimog” machine andwashing the tunnel wall during cleaning up operation.

5. Illustrative Example of Sensor Failure Detection

Pictures showing visibility index (“VI”) meter: light transmitter (left) and light reflector (right). Water drops causing “failure” are seen in the reflector.

センサ調整状況とVI計測値

0

20

40

60

80

100

23:35 23:45 23:55 0:05 0:15 0:25 0:35 0:45 0:55 1:05

時刻

VI値

[%]

取付状況の目視点検

投受光部レンズの清掃

リフレクタ反射鏡の清掃

手動校正

5. Illustrative Example of Sensor Failure Detection

Time

VI[%

]

Cleaning of Transmitter/Receiver Lens

Cleaning of Reflector Glass

Manual CalibrationVisual Inspection of Installation

Procedure to restore VI sensor

6. Conclusions

• Has presented a sensor failure detection method in routine tunnel operations.

• Makes statistical comparisons between measured and predicted values based on quasi-steady approximations to air flow and pollution concentration model.

Summarizing statements:• Measurements of traffic data can be used to predict evolving air

velocities and pollution concentrations throughout the tunnel.• By analyzing measured data, it is possible to infer realistic

approximations for values of tunnel and vehicle parameters.• The method of determining optimal values for tunnel and vehicle

parameters provides quantitative statistical data about expected deviations between measured and predicted values at any sensor.

• By monitoring statistical variations of measured and predicted values of sensor during actual tunnel operation, it is possible to detect significant variations from normal behavior and thus to identify instances of probable sensor failures.

Thank you for your attention!