UUV and Sensor Health Monitoring

Transcription

1 UUV and Sensor Health Monitoring Daniel Brown Jodi Miller Sonar Research and Development Division 26 April 2012

2 Closed loop health monitoring allows the system to adapt the mission outcome based on measured health Open Loop Health Monitoring Logging for post mission analysis Logging for in mission operator analysis Closed Loop Health Monitoring Embedded analysis for mission abort Embedded analysis for sensor adaptation Embedded analysis for UUV mission adaptation 2

3 Three key lessons were learned in developing health monitoring for UUV based sonar sensors UUV health metrics must be built from detailed sensor performance models Understand the cost of false positives and false negatives in a closed loop system Present results in a way that gives situational awareness 3

4 A combination of direct and derived health metrics must be used to measure UUV and Sensor health Direct UUV health metrics Leak, temperature, depth, battery capacity, Direct Sensor health metrics Voltage, current, impedance, temperature, hardware state, data storage capacity, Derived UUV health metrics Derived metrics must be built from detailed models of sensor performance. Radiated acoustic noise, EMI levels, bus voltage, vehicle network load, vehicle stability 4

5 Multiple health monitoring opportunities are present in a typical UUV mission Pre launch Post launch Sonar Mapping Health Monitoring Opportunity Sonar Inactive 5

6 The mission profile will determine the structure of the health monitoring state diagram 6

7 Launch and recovery complexity is a major consideration in the health monitoring approach Single Operator Launch and Recovery Multiple Operator Launch and Recovery RHIB Support Complex Launch and Recover 7

8 Understand the costs associated with false positives and false negatives from your embedded tests Must consider the CONOPS and the hardware High cost (risk/time) launch and recover Pre launch built in test Post launch tests need a low false positive rate Long ingress/egress Pre transit built in test Post transit tests need a low false positive rate Must detect all catastrophic faults Low false negative rate 8

9 Developing health monitoring for two similar systems yields results that differ due to mission CONOPS 9

10 System 1 is a very shallow water survey system with a medium range high resolution imaging sonar Sensor: Imaging SAS system Simple launch and recovery Mid weight UUV Lightweight crane Short ingress/egress Typically less than 20 minutes In mission connectivity Regular surfacing for GPS updates permits RF status 10

11 System 2 is a shallow to deep water survey system with a long range high resolution imaging sonar Sensor: Imaging SAS Complex launch and recovery Heavy weight UUV Large crane and multiple operators required Long ingress/egress Possibly > 2 hours In mission connectivity Limited ACOMMS, typically none 11

12 Three key lessons were learned in developing health monitoring for UUV based sonar sensors UUV health metrics must be built from sensor performance models Understand the cost of false positives and false negatives in a closed loop system Present results in a way that gives situational awareness 12

13 Synthetic aperture sonar (SAS) systems are used to image the seafloor SAS sensors ensonify the bottom to port and starboard Imagery generated measures the reflectance of objects and the sea floor 13

14 Coherent combination of sequential pings requires the UUV motions to be small and accurately measured Calculate Range Migration Curve Apply Delays to Raw Data Coherently Combine Channels 14

15 Sensor data is converted into well focused imagery when the UUV motion is within specification Out of specification motion produces blurred imagery 15

16 SAS imaging performance models dictate UUV stability requirements UUV Parameter Value Sensor Parameter Maximum Roll [deg] 2.0 Ensonification requirements Maximum Pitch [deg] 7.5 Near range aspect loss Maximum Yaw [deg] 9.0 Yaw analysis result Maximum Pitch Rate [deg/s] 1.25 Phase stability Maximum Yaw Rate [deg/s] 0.75 Phase stability Mean Altitude [m] 10 Long range shadow aspect Maximum Speed [kts] 3 System maximum mapping range Speed Standard Deviation [kts] 0.15 Area coverage rate Individual UUV stability parameters are derived to satisfy different sensor parameters 16

17 The sensor s requirements on UUV stability must be derived considering the CONOPS of the mission Long range SAS systems have more restrictive motion tolerances Short range SAS systems can tolerate larger vehicle motions 17

18 Three key lessons were learned in developing health monitoring for UUV based sonar sensors UUV health metrics must be built from sensor performance models Understand the cost of false positives and false negatives in a closed loop system Present results in a way that gives situational awareness 18

19 The very shallow water system s CONOPS permit a higher false alarm rate Ease of recovery reduces the false positive cost False positive flexibility allows us to minimize false negatives Wireless connectivity allows the system to surface and communicate regularly 19

20 The very shallow water system s CONOPS permit a higher false alarm rate Launch/recovery and ingress/egress gives a high false positive cost False negatives on critical parameters must be minimized Lack of data exfiltration makes the design of the fault detector challenging 20

21 Three key lessons were learned in developing health monitoring for UUV based sonar sensors UUV health metrics must be built from sensor performance models Understand the cost of false positives and false negatives in a closed loop system Present results in a way that gives situational awareness 21

22 Post mission visualization of health monitoring logs provides a rapid summary of mission quality 22

23 Health parameters are measured against metrics developed for the specific system and CONOPS Vehicle speed too high on transit legs Green Pass Yellow Marginal Red Fail Good performance on survey legs 23

24 Rapid overview is helpful in diagnosing system and sensor related problems Western edge of many circles exhibits marginal performance Environmental impact on autopilot suspected (e.g., current or wave direction) 24

25 Three key lessons were learned in developing health monitoring for UUV based sonar sensors UUV health metrics must be built from sensor performance models Understand the cost of false positives and false negatives in a closed loop system Present results in a way that gives situational awareness Questions? 25