Analysis of the operations of 58 gliders during the last 2 years Dr. Mario P. Brito, Dr. David Smeed and Prof. Gwyn Griffiths (retired) National Oceanography Centre, Southampton, UK -1-
Aim of the Project To devise a framework for risk informed decision making in glider operation To improve glider coverage for science missions To provide a deeper understanding of the factors that effect glider deployment risk To inform the glider user community of risk and reliability techniques tailored to glider deployment To extend state of the art in risk quantification and modelling techniques -2-
Online Survey 1. Vehicle identifier. 2. Mission number or other mission identifier. 3. Date of the start of the mission. 4. Type of vehicle: SLOCUM G1 (shalow, deep), SLOCUM G2 (shalow, deep), Seaglider 1000m or Spray. 5. Mission type: shelf deployment, shelf edge deployment or deep ocean deployment 6. Mission i length thin days. 7. Mission depth in metres. 8. Answer did the mission end in failure? If yes, specify type of failure. 9. Answer: Did the glider fail initial test? -3-
Participating countries ntries Number of en -4-
NERC N Participating Centres 70 60 50 40 30 20 10 0 CNRS CSIC OGS AWI PLOC OCAN UEA OC C UCY IFM GEOM OMAR HZG NURC SAMS SA -5-
Glider types 25 20 Number of vehic cles 15 10 5 0 Seaglider Slocum G1 Slocum G1 Slocum G2 Slocum G2 1000m deep shallow deep shallow -6-
Operating Environments 70 60 50 Shelf deployment Deep ocean deployment Shelf edge deployment Vehicle Seaglider 1000m Slocum G1 shallow 40 Slocum G1 deep 30 Slocum G2 shallow 20 10 Slocum G2 deep Median mission endurance [days] 64 8 19 18 12 0 Seaglider 1000m Slocum G1 shallow Slocum G1 deep Slocum G2 shallow Slocum G2 deep -7-
Failure data Seaglider 1000m Slocum G1 shallow Slocum G1 Deep Slocum G2 shallow Slocum G2 deep Total endurance[days] 2514 772 1461 188 550 Number of Aborts due to failures 19 13 23 3 5 Abort rate (per day) 0.00756 0.0168 0.0157 0.0159 0.00909 Number of losses 7 2 1 0 0-8-
16 14 12 10 8 6 4 Failure Modes Count 2 0 Leak Power/Battery Buoyancy pump Collision- vessel Sciencee sensor Iridium comms Unknown Attitude control Collision - seabed Dataa logging Glider ecovered Command/Contr Onboard software Air bladder leak Rudder broken Argos Fin locked at a Digifin not Roll motor -9-
Lifetime Analysis: People - Kaplan Meier estimator Lives Age at death or loss to study 10 20 30 40 50 60 70 80 The loss-to-study subjects are censored. They are included in each interval up to their last recorded age, but not counted as deaths. The Kaplan Meier nonparametric estimator: Emigrated S ˆ( t) = t < t i n i n i d i Lost contact n i is the number of lives at risk, and d i the number of deaths in each interval. -10-
10 1.0 Lifetime Analysis: Kaplan Meier estimator Probability of survival 0.8 S ˆ( t ) n i d n i = ti <t i ni Time ti di Ci (ni-di)/ni ^ S(t) 11 0 0 0 1 1 11 10 1 0 (11-1)/11 = 0.91 0.909 10 17 0 1 (10-0)/10 = 1 0.91 9 20 1 0 (9-1)/9 = 0.88 0.81 8 45 1 0 (8-1)/8 = 0.71 0.71 7 50 2 0 (7-2)/7 = 0.50 0.50 0.6 5 60 1 0 (5-1)/5 = 0.8 0.40 4 61 0 1 (4-0)/4 = 1 0.40 0.4 3 65 1 0 (3-1)/3 = 0.67 0.27 2 67 1 0 (2-1)/2 = 0.5 0.13 0.2 1 72 1 0 (1-1)/11)/1 = 0 0 0.0 10 20 30 40 50 60 0 Time (years) 70 Lifetime table -11-
Probability of Mission Ending in Failure Surv viving 1.0 0.9 08 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 20 40 60 80 100 120 140 160 180 Endurance [days] Risk profile for the Seaglider 1000m -12-
Probability of Mission Ending in Failure Slocum G1 Surviving 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 10 20 30 40 50 60 Endurance [days] Surviving 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 10 20 30 40 50 60 70 80 Endurance [days] Risk profile for the Slocum G1 Shallow glider on the left, Slocum G1 Deep on the right. -13-
Probability of Mission Ending in Failure Slocum G1 Surviving 1.0 0.9 0.8 07 0.7 0.6 0.5 0.4 0.3 02 0.2 0.1 0.0 0 100 200 Endurance [days] Surviving 1.0 0.9 0.8 07 0.7 0.6 0.5 0.4 0.3 02 0.2 0.1 0.0 5 10 15 20 25 30 35 40 45 50 Endurance [days] Risk profile for the Slocum G2 Shallow glider on the left, Slocum G1 Deep on the right. -14-
Surviving Survival for deep and shallow gliders 1.0 1.0 0.9 0.9 0.8 0.8 g0.7 0.6 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 10 20 30 40 50 60 Endurance [days] g Survivin 0.5 0.4 0.3 0.2 0.1 0 100 200 Endurance [days] Survival for Shallow and deep gliders Shallow glider Deep pglider MTBF (days) 60 104 Failure rate 0.0167 0.00960-15-
Coverage Estimation Psurv = P N ( N 1) = 1 P ( N,1) ( ) M ( N = 1 p( t) ) M, N N number vehicles M number of missions Psurv probability of survival p(t) probability of loss for endurance t -16-
Ocean Coverage - Shallow gliders -17-
Ocean coverage Deep gliders -18-
Conclusions A risk informed mission planning will not only reduce operational costs but it will also increase our confidence that mission requirements will be met A formal approach for quantifying the coverage that can be achieved with a fleet of gliders has been presented. There is room for undersea glider reliability growth Interaction with manufactures will hopefully see undersea gliders achieving the same level of reliability growth as that observed for APEX floats -19-