Sensor Failure Detection in Road Tunnel Ventilation

Sensor Failure Detection in Road Tunnel
Ventilation
April 24, 2012
by
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 conditions
such as heavy rain and snow.
2.2 Typical Failure in Air Velocity (“AV”) Meter
- Measurements affected by the air flow disturbance due to vehicle
movement in local AV meter.
- Measurements disrupted by traffic congestion in cross sectional AV
meter.
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
Employs quasi-steady air velocity and pollution concentration 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
Traffic Flow (Input)
-Vehicle location
-Vehicle Velocity
Pollution Concentration Model
- Pollution generated by vehicles in box
- Pollution coming in from neighboring box
- Pollution move out to neighboring box

4. Architecture of Proposed Sensor Failure Detection
Calibration
Module
X
Three numerical modules and two data storages
TC AV VI CO
Measured Data
Online Simulation Module
Traffic, Air Velocity, Pollution Density
Parameters State Variables
Predicted Data
Fan
Failure
Detection
Module
X

4.1 Calibration
Objective is to revise the parameter values that best fit the measured data set
Pollution Gas Emission of
Large & Small Vehicles
JET FANS
μL, μS
Calibration process
provides quantitative
statistical data about
deviation of measured
and predicted values.
ATL, ATS
Effective Resistance Area of Large
& Small Vehicles

4.2 Statistical Nature of Measured and Predicted Values
10
AV measured value m/s
8
6
4
2
y = 0,8855x - 0,1029
R² = 0,934
0
0 2 4 6 8 10
AV predicted value m/s
AV data were collected at Kawasaki Koro Tunnel, Japan.
The Variation (= difference of measured and predicted values)
is indicative of “normal distribution”.

4.2 Statistical Nature of Measured and Predicted Values
100
VI measured value g/kg-air
90
80
y = 0,9543x + 2,81
R² = 0,4012
70
70 80 90 100
VI predicted value g/kg-air
VI data were collected at Kawasaki Koro Tunnel, Japan.
The Variation (= difference of measured and predicted values)
Is indicative of “normal distribution”.

5. Illustrative Example of Sensor Failure Detection
Collected TC, AV and VI data from Kawasaki Koro Tunnel and run the Method
ΔAV[m/s]
ΔVI [%]
10
5
0
-5
-10
20
10
0
-10
-20
12:00
14:00
3σ
3σ
16:00
18:00
∆AV(=AVmeasured-AVpredicted); ∆VI (=VImeasured-VIpredicted)
20:00
The sensor failure is detected at time 01:00.
22:00
00:00

5. Illustrative Example of Sensor Failure Detection

5. Illustrative Example of Sensor Failure Detection
Pictures indicating maintenance team running “unimog” machine and
washing 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.

VI値 VI[%] [%]
5. Illustrative Example of Sensor Failure Detection
100
80
60
40
20
Visual Inspection of Installation
取付状況の目視点検
Procedure to restore VI sensor
センサ調整状況とVI計測値
Cleaning of Cleaning of Manual
Transmitter/ 投受光部 Reflector リフレクタ 手動校正 Calibration
Receiver レンズの清掃 Lens 反射鏡の清掃
Glass
0
23:35 23:45 23:55 0:05 0:15 0:25
Time 時刻
0:35 0:45 0:55 1:05

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!