A Strategy for Reliability Evaluation and Fault Diagnosis of Autonomous Underwater Gliding Robot based on its Fault Tree

A Strategy for Reliability Evaluation and
Fault Diagnosis of Autonomous Underwater
Gliding Robot based on its Fault Tree
Koorosh Aslansefat
Gholamreza Latif-Shabgahi and Mojtaba Kamarlouei
Email: k_Aslansefat@sbu.ac.ir
Third International Conference on Robotics, Automation and Communication Engineering (ICRACE 2014)
Dubai, UAE, October 24, 2014

Topics
 Autonomous Underwater Gliders (AUGs)
 Fault Tolerance and its Necessity, Attributes and Solutions
 Static Fault Tree, Assumption and Modelling
 Faults in AUG’s Components
 A Strategy to Construct AUG's Fault Tree
 Reliability Evaluation of AUGs
 Conclusion
2

Autonomous Underwater Gliders (AUGs) 3
An Autonomous Underwater Glider (AUG) is a special type of
Autonomous Underwater Vehicle (AUV) that uses small changes
in its buoyancy in conjunction with wings to convert vertical
motion to horizontal, and thereby propel itself forward with very
low power consumption.
 It can be use for long range survey (thousands of kilometers of range)
 Extending ocean sampling missions from hours to weeks or months
 Providing data on temporal and spatial scales
 Much more costly to sample using traditional shipboard techniques
(typically cost $100,000)
The benefits of using AUGs

Fault Tolerance and Necessity
4
Applications
Safety
Critical
Environment
Critical
Budget
Critical
Fault tolerance is the property that enables a system to
continue operating properly in the occurrence of the failure of
its components

Fault Tolerance and Attributes
 Reliability
 Availability
 Safety
 Performability and etc.
5
Generally, in fault tolerance such parameter need to measured:
 Failure Rates
 Repair Rates
 MTTF, MTBF and etc.
Fault tolerant system evaluated by attributes such as:

Fault Tolerance and Modelling
6
Several methods have been developed for Reliability Evaluation
State Space Methods
 (Semi) Markov Model
 Petri-Nets
Combinatorial Methods
 Reliability Block Diagram (RBD)
 Fault Tree
Numerical and Simulation-Based Methods
 Monte Carlo

Static Fault Tree
A Fault Tree (FT) illustrates the ways
through which a system fails. It states
different ways in which combination of
faulty components (called the "Basic
Events") result in an undesired event in the
system (called the "Top Event"). In this
model, basic events are connected to each
other through logical gates forming upper-
level intermediate events
7
(a)(b)(c)
(d)(e)

Fault Tree Assumption
 The occurrence of more than one fault at the same time is not
allowed and the common cause failures (CCFs) are ignored.
 On-time repairing of vehicle's components is not allowed.
 Dynamic characteristics such as functional dependency, components'
priority and the use of spares are not applied in the model.
 The system components failure rate obeys exponential distribution
function
8

Fault Tree Solutions
9
1
AND
i
iQ Q

 
 
1
1 1OR i
i
Q Q

  
From probability theory, the output probability of AND & OR gates is
calculated as follows:

Faults in AUG’s Components
In this paper, 9 subsystems and their faults have been considered.
 Power system
 Leak detection system
 Diving system
 Environment detection
 Collision avoidance
10
 Computer system
 Propulsion system
 Communication system
 Navigation system

Navigation System Faults
In AUGs, the GPS antenna is usually located on
one of the horizontal wings. Whenever the
vehicle comes to the surface, through its 90
degree rolling movement the antenna locates in
the highest place from water surface in order to
receive the data. In some other types of these
vehicles there is a flap behind the main rudder
where the antenna is located. If this system or
the rolling system confronts any problem,
vehicle's location recognition becomes hard and
the probability of vehicle’s loss is increased. It
is obvious that any malfunction in GPS and its
processor cause the vehicle to get lost.
11

A Strategy to Construct AUG's Fault Tree
The tree is constructed in five steps as follows:
1) Determine the level of faults occurrence (sensor, sub-subsystem, and subsystem).
2) Determine the contribution of each component faults in its upper level faults, and
then use appropriate gate (AND and OR gate) to construct subsystem or main
system’s fault tree.
3) Consider each subsystem fault tree as a module of main fault tree.
4) Find available probability of each component's failure.
5) Construct the main fault tree and evaluate system’s reliability
12

Example: FT construction of navigation subsystems
According to the first step, the level of navigation
subsystem's component is determined. In this
subsystem, four basic events exist each one of
which enables navigation subsystem and this is
the way to use OR gate
13
         2522
22 25
25
22
1 1 1 1 ... 1 1 1 ... 1Nav i
tt
i
P P P P e e  

          

A Strategy for Reliability Evaluation and Fault Diagnosis of Autonomous Underwater Gliding Robot based on its Fault Tree

Reliability Evaluation of AUGs
15

Reliability Evaluation of AUGs
16

Cut-Sequence in Fault Tree
17
CsIP(MCs)AUG Sub-Systems
4.452920.5637863Power Sys.
6.018510.7620066Leak Detection Sys.
6.957080.8808393Diving Sys.
6.805150.8616035Environment Detect Sys.
6.832430.8650568Obstacle Avoidance Sys.
6.956660.8807865Computer Sys.
7.616520.9643316Propulsion Sys.
7.213830.9133464Communication Sys.
4.913860.6221463Navigation Sys.
 
 
i
i
P MCs
MCsI
P TOP


Conclusion
 The literature suffer from AUGs fault tree construction and this paper
present a typical fault tree for AUGs
 Reliability of two type autonomous underwater robot (AUVs and AUGs)
have been compared and shows the reliability of AUGs is more of than
AUVs
 By means of cut-sequence analysis, failure bottleneck of AUGs such as …
and … have been shown.
18

Future Works
 The presented fault tree can be developed to use for fault diagnosis of AUGs
 This research can be extended for considering a dynamic behaviors of faults
such as priority and sequence dependency, spare, functional dependency and
repair.
 The other dependability attributes such as availability and safety can be
evaluated.
 It possible to use non-exponential failure distribution.
19

References
20
[1] A. Ortiz, P. Julian, B. Guillem and O. Gabriel, "Imroving the safety of AUVs," in OCEANS '99 MTS/IEEE, Seattle, WA, 1999.
[3] J. Strutt, "Report of the Inquiry Into the Loss of Autosub2 Under the Fimbulisen," National Oceanography Centre, Southampton, 2006.
[4] M. P. Brito, G. Griffiths and A. Trembranis, "Eliciting Expert Judgment on the Probability of Loss of an AUV Operating in Four Environments,"
National Oceanography Centrer, Southampton, 2008.
[5] G. Griffiths and A. Trembanis, "Towards a Risk Management Process for Autonomous Underwater Vehicle," in Masterclass in AUV Technology for
Polar Scienc, Southampton, Society for Underwater Technology, 2007, pp. 103-118.
[6] X. Bian, C. Mou, Z. Yan and J. Xu, "Simulation Model and Fault Tree Analysis for AUV," in International Conference on Mechatronics,
Changchun, 2009.
[7] M. P. Brito, D. Smeed and G. Griffiths, "Underwater Glider Reliability and Implications for Survey Design," Oceanic Technology, 2013.
[8] M. P. Brito, D. Smeed and G. Griffiths, "Analysis of the Operations of 58 Gliders During the Last 2 Years," National Oceanography Center and
Southampton University, Liverpool, UK, 2013.
[9] G. Griffiths, C. Jones, J. Ferguson and N. Bose, "Undersea gliders," Journal of Ocean Technology, vol. 2, no. 2, pp. 64-75, 2007.
[10] H. Xu, G. Li and J. Liu, "Reliability Analysis of an Autonomous Underwater Vehicle Using Fault Tree," in IEEE International Conference on
Information and Automation (ICIA), Yinchuan, 2013.
[11] G. Griffiths, L. Merckelbach and D. Smeed, "On The Performance of Three Deep-diving Underwater Gliders," in OCEANS 2007 - Europe,
Aberdeen, 2007.
[12] K. Aslansefat, G. Latif-Shabgahi and M. Kamarloie, "Faults Taxonomy in Autonomous Underwater Vehicle and Provide their Fault Tree," in 15th
Marine Industries Conference, Kish, Iran, 2013.

A Strategy for Reliability Evaluation and Fault Diagnosis of Autonomous Underwater Gliding Robot based on its Fault Tree

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