Discerning the operational state of a vehicle’s distributed electronic systems from vehicle network traffic for use as a fault detection and diagnosis tool

2014 ◽  
Vol 15 (3) ◽  
pp. 441-449 ◽  
Author(s):  
J. E. Taylor ◽  
M. Amor-Segan ◽  
G. Dhadyalla ◽  
R. P. Jones
Keyword(s):  
Fault Detection ◽  
Network Traffic ◽  
Fault Detection And Diagnosis ◽  
Electronic Systems ◽  
Diagnosis Tool ◽  
Detection And Diagnosis ◽  
Vehicle Network

Prognostication and Health Monitoring of Electronics in Implantable Biological Systems

2008 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Manish Kulkarni ◽  
Dhananjay Panchagade ◽  
Jeff Suhling ◽  
...  
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Solder Ball ◽  
Wavelet Packet ◽  
Fault Detection And Diagnosis ◽  
Electronic Systems ◽  
Energy Decomposition ◽  
Time Frequency ◽  
Auto Regressive ◽  
Detection And Diagnosis

In the present paper auto-regressive and time-frequency based techniques have been investigated to predict and monitor the damage in implantable biological electronics such as pacemakers and defibrillators. The approach focuses is on the pre-failure space and methodologies for quantification of failure in electronic equipment subjected to shock and vibration loads using the dynamic response of the electronic equipment. Presented methodologies are applicable at the system-level for identification of impending failures to trigger repair or replacement significantly prior to failure. Leading indicators of shock-damage have been developed to correlate with the damage initiation and progression in under variety of stresses in electronic systems. The approach is based on monitoring critical solder interconnects, and sensing the change in test-signal characteristics prior to failure, in addition to monitoring the transient strain characteristics optically using digital image correlation and strain gages. Previously, SPR based on wavelet packet energy decomposition and the Mahalanobis distance approach have been studied by the authors for quantification of shock damage in electronic assemblies [Lall 2006]. In this paper, Auto-regressive (AR), wavelet packet energy decomposition, and time-frequency (TFA) techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in implantable biological electronic systems. One of the main advantages of the AR technique is that it is primarily a signal based technique. Reduced reliance on system analysis helps avoid errors which otherwise may render the process of fault detection and diagnosis quite complex and dependent on the skills of the analyst. Results of the present study show that the AR and TFA based health monitoring techniques are feasible for fault detection and damage-assessment in electronic units. Explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, package falloff and solder ball failure.

Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units

2014 ◽  
Vol 39 ◽  
pp. 70-83 ◽  
Author(s):  
Ken Bruton ◽  
Paul Raftery ◽  
Peter O'Donovan ◽  
Niall Aughney ◽  
Marcus M. Keane ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Air Handling Units ◽  
Diagnosis Tool ◽  
Detection And Diagnosis
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