A knowledge driven approach to aerospace condition monitoring

Aircraft operators are continually striving to reduce both the amount and the cost of aircraft maintenance. Whilst at the same time ensuring that the aircraft safety, reliability and integrity are not compromised. One solution which has seen a lot of attention is known as condition monitoring. The aim of condition monitoring is to develop the ability to detect, diagnose and locate damage, even predicting the remaining useful life of the structure or system. There are difficulties associated with developing aerospace condition monitoring which transcends technical, financial and regulatory. Aerospace legislation requires that any decisions on maintenance, safety and flightworthiness to be auditable and data patterns to relate to known information. The use of data, physical models and knowledge approaches can individually produce reliable health related decisions, but the fusing of these different solutions within an appropriate framework will enhance the intelligence in the decision making process. This paper reviews such a framework and design methodology being used for the development of knowledge based condition monitoring systems for aircraft landing gear actuators.

[1]  Stephen J. Engel,et al.  Prognostics, the real issues involved with predicting life remaining , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[2]  Cheng-Ching Yu,et al.  Model-Based Approach for Fault Diagnosis. 1. Principles of Deep Model algorithm , 1994 .

[3]  Jie Lu,et al.  A state-based knowledge representation approach for information logical inconsistency detection in warning systems , 2010, Knowl. Based Syst..

[4]  Zone-Ching Lin,et al.  Establishment of transverse beam engineering knowledge coding of door-shaped structure and case-based similarity method , 2010, Knowl. Based Syst..

[5]  Safak Kiris,et al.  A knowledge-based scheduling system for Emergency Departments , 2010, Knowl. Based Syst..

[6]  S. Sitharama Iyengar,et al.  Foundations of data fusion for automation , 2003 .

[7]  Mohammad Hamiruce Marhaban,et al.  FPGA-Based Fuzzy Logic: Design and Applications – a Review , 2009 .

[8]  Vito Di Gesù,et al.  A fuzzy approach to the evaluation of image complexity , 2009, Fuzzy Sets Syst..

[9]  Roozbeh Razavi-Far,et al.  Fuzzy logic based fault diagnosis of a PWR nuclear power plant , 2009 .

[10]  Andrew Starr,et al.  A review on the optimisation of aircraft maintenance with application to landing gears , 2010, WCE 2010.

[11]  P. Moeinzadeh,et al.  A Combined Fuzzy Decision Making Approach to Supply Chain Risk Assessment , 2009 .

[12]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[13]  C.S. Byington,et al.  A model-based approach to prognostics and health management for flight control actuators , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[14]  J. Dunsdon,et al.  An Open System Architecture for Condition Based Maintenance Overview , 2007, 2007 IEEE Aerospace Conference.

[15]  Andrew Starr,et al.  A Review of data fusion models and architectures: towards engineering guidelines , 2005, Neural Computing & Applications.

[16]  Andrew Starr,et al.  An intelligent health monitoring framework for a motor-driven actuator , 2009 .

[17]  Etienne E. Kerre,et al.  Defuzzification: criteria and classification , 1999, Fuzzy Sets Syst..

[18]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .