Aircraft Operational Reliability - A Model-based Approach Kossi - PowerPoint PPT Presentation

Aircraft Operational Reliability - 
 A Model-based Approach 
 Kossi Tiassou, 
 Mohamed Kaâniche, Karama Kanoun, Chris Papadopoulos, Christel Seguin Project: @Most Formal Methods Forum, January 26, 2016

Context ☞ Growing interest in air Freight transportation ☞ Competitiveness Passenger ☞ Enhance service delivery and minimize operation and maintenance costs Contribution: Reinforce the role of dependability assessment in aircraft operation Context & Objectives 2

Aircraft Dependability Modeling & Assessment Common practice: during system design and development Support for System ⇒ Safety and availability architecture definition oriented models Long-term objectives Future: usable during system operation - in addition - Adjust aircraft operation Models for assessment in according to the current ⇒ operation operational conditions and changes Short-term objectives Context & Objectives 3

Objectives: Dependability Assessment in Operation ☞ Whenever necessary → Continue Assessment to Re-assessment → → Plan maintenance support mission → Mission interruption definition Unforeseen event: Mission Mission Mission - Failure end - Mission re-definition planning start ☞ To avoid as much as possible disruptions/interruptions Delay, Cancellation, In-flight turn back, Diversion ⇒ Evaluate the probability to operate without operational disruption/ interruption until a given time or location Context & Objectives 4

Means ☞ Develop a model-based dependability assessment framework usable in operation ☞ Forecast operational reliability with regard to disruptions caused by failures and maintenance issues Operational Dependability Measures • System Reliability, SR(t): Probability to meet minimum requirements related to the system, during flight duration • Mission Reliability, MR(t): Probability to achieve a specific mission without interruption Context & Objectives 5

Dependability Modeling Model calibration & analysis Model M Content Measure definition Dependability analysis specialist During the In Operation design phase Event / Change Model content M2 M0 M1 definition Modeling Specialist and Operators and Maintainers System Builders Context & Objectives 6

To Achieve the Objectives ☞ Identification of relevant information for the model construction ☞ Modeling basis that facilitates: • Model construction • Model update in operation ☞ Validation on case studies Context & Objectives 7

Outline � Relevant Information Identification � Modeling Approach: Meta Model and Stochastic Model � Stochastic Modeling in the Context of @Most � Case Study Stochastic Model • Results • 8

Mission & Mission Dispatch Flight achievement Flight Ground phase phase Mission = sequence of flights ☞ Mission Dispatch Decision ..... Dispatch ? Dispatch ? 1 Relevant Information Identification 9

Next Flight Dispatch Decision All Ok Go Goif -o Operational Acceptable? Dispatch Limitation Feasible? Goif Failure status Goif- m Maintenance Procedures Corrective NoGo Actions Delay or Cancellation 1 Relevant Information Identification 10

Relevant Information - 1 All Ok Go Goif -o Operational Acceptable? Dispatch Limitation Feasible? Goif Failure status Goif- m Maintenance Maintenance Procedures System component state Corrective NoGo Actions Requirements Delay or Cancellation 1 Relevant Information Identification 11

Relevant Information - 2 MR Mission dependent Information Mission Ground Flight 1 Ground Flight 2 Ground … Flight n Profile Requirements Min_Sys_R M_Prof_ R Mainte nance Aircraft systems System Behavior Subsystem Subsystem Subsystem Component failure modes, rates etc SR Core Information 1 Relevant Information Identification 12

Outline � Relevant Information Identification � Modeling Approach: Meta Model and Stochastic Model � Stochastic Modeling in the Context of @Most � Case Study Stochastic Model • Results • 13

Changes and modeling constraints ☞ Changes to be Taken into Account • Changes in the states of the system components § Failure, Maintenance activities • Failure distributions of the components • Mission profile ☞ Modeling constraints • Model construction during the design and development phase • Model update in operation by non-modeling specialist 2 Modeling Approach: Meta Model and Stochastic Model 14

Implementation Diagnosis Mission profile & Prognosis & maintenance data no$fica$on Assessment manager Model Processing SR(t) Processing Module Model update interface MR(t) Stochastic Model Configura$on data Petri Net, AltaRica, SAN Operational dependability model 2 Modeling Approach: Meta Model and Stochastic Model 15

Model Construction and Update Process up-to-date data Meta- model Stochastic Up-to-date Model Modeling Model Tuning Model Aircraft Petri Net, AltaRica, SAN specific Information 2 Modeling Approach: Meta Model and Stochastic Model 16

Benefits of the Meta-model ☞ Abstracts and structures model content ☞ Aircraft families A340 Stochastic Model AltaRica A380 A320 Stochastic Meta- Stochastic Model model Model SAN SPN Model generation 2 Modeling Approach: Meta Model and Stochastic Model 17

Example of Meta-model: System Components 2 Modeling Approach: Meta Model and Stochastic Model 18

From Meta-model to Stochastic Model ☞ Dynamic models – state-based models Petri Net C_state Exponential λ =v C_failure 2 Modeling Approach: Meta Model and Stochastic Model 19

Outline � Relevant Information Identification � Modeling Approach: Meta Model and Stochastic Model � Stochastic Modeling in the Context of @Most � Case Study Stochastic Model • Results • ¥ Conclusion and Perspectives 20

AltaRica and SAN Basic Component node Component C.power Predicate: ( status->Mark() =1) flow && power->Mark() stateOk : bool : out ; power: bool : in; Function: status ->Mark() =0; Status=ok state and power status : {ok,failed} ; Exponential: event λ =2.10-4 power λ =2.10 -4 failure failure, init status := ok ; status status=failed IGFailure failure trans Assert_ status=ok and power |- failure update -> status:=failed; IG_assert stateOk assert stateOk=Status stateOk=(status=ok); extern Predicate: law <event failure> = C.StateOK (stateOk->Mark()) != (status->Mark()=1) exponen$al(2.0E-4); Function: edon stateOk->Mark() = (status->Mark()=1); SAN model AltaRica model 3 Stochastic Modeling in the Context of @Most 21

Case Study: The Rudder Control Subsystem Control Lines SL S1 ServoCtrl_G PL1 P1 PL2 ServoCtrl_B Surface P2 PL3 ServoCtrl_Y P3 BCM BCL Min_Sys_R = (PL2 =ok ∧ BCL =ok ∧ BPS_B BPS_Y (PL1 =ok ∨ (PL3 =ok ∧ SL =ok)) ∧ (PL3 =ok ∨ (PL1 =ok ∧ SL =ok)) ∧ Initially: PL1, PL2, PL3 activated (SL =ok ∨ (PL1 =ok ∧ PL3 =ok)) After failures of P1, P2 and P3 : activation of S1 After failures of P1, P2, P3 and S1 : activation of BCM, BPS_B, BPS_Y 4 Case Study 22

Global Model Structure Mission Dependent Model Min_Sys_R = (PL2 =ok ∧ BCL =ok ∧ (PL1 =ok ∨ (PL3 =ok ∧ SL =ok)) ∧ Interface: Requirements expression (PL3 =ok ∨ (PL1 =ok ∧ SL =ok)) ∧ (SL =ok ∨ (PL1 =ok ∧ PL3 =ok)) PC BC SC Core Model 4 Case Study 23

Flying Taxing_to_Takeoff Landing In_Flight To_air Abort Diversion Flight Back To Ramp Diverted Phases MPR DiversionCondition AbortCondition To_ground Departure CP_Flight Ready Next Pending Estimated_duration Ground flight Departure preparation Landed Prof Next_flight Delay or Max_tolerated_time preparation cancellation Dispatchability Scheduled_ MProg maintenance Allow SM_Time Dispatch CP_M condition inhibitM No_Dispatch setM Ground Period Min_Sys_R Require_maintenance Unscheduled_maintenance 4 Case Study 24

The Core Model Not_Fulfilled Min_Sys_R CP Fulfilled IGN Internal IGFul Interface PL1 SL PL2 PL3 BCL PC SC BC Hyd P1 setPL1 P1_maintenance IGMPx IGPxF P1_failure CP PL1 IGPL1 ServoCtrl_G SCG_maintenance IGMP1 IGSCGF SCG_failure Elec Control line PL1 sub-model 4 Case Study 25

Assessment ☞ Parameter setting of model in operation ☞ All system components considered initially operational ☞ Exponential distribution for the failure events • Failure rates between 10 -6 /FH and 10 -4 /FH ☞ Deterministic durations for flight phases and ground activities 4 Case Study 26

Re-assessment During Missions ☞ Initial assessment & re-assessment after major changes • Failure - Maintenance • Distribution change • Mission profile changes Model update → Continue & → → Plan maintenance Initial re-assessment → Mission adjustment assessment Mission preparation Mission start Mission end Changes 4 Case Study 27

Initial Assessment MR(t) evaluated before the start of the mission Mission: 7 days, 4 flights/day, 3 hours each 1 0 0,995 0,99 0,985 0,98 Minimum Mission Reliability Requirement MMRR 0,975 0,97 0,965 0,96 1 2 3 4 5 6 7 day 4 Case Study 28

Failure of P1 after 4 days 1 0 0,995 0,99 0,985 0,98 0,975 MMRR 0,97 0,965 0,96 day 1 2 3 4 5 6 7 4 Case Study 29