TITLE:
Fusion of Model-Based and Data Driven Based Fault Diagnostic Methods for Railway Vehicle Suspension
AUTHORS:
Abdulai Ayirebi Ankrah, James Kuria Kimotho, Onesmus Mutuku Muvengei
KEYWORDS:
Railway Vehicle, Suspension System, Hybird Model, Fault Detection, Support Vector Machine
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.12 No.3,
August
13,
2020
ABSTRACT: Transportation of freight and passengers by train is
one of the oldest types of transport, and has now taken root in most of the developing
countries especially in Africa. Recently, with the advent and development of
high-speed trains, continuous monitoring of the railway vehicle suspension is
of significant importance. For this reason, railway vehicles should be
monitored continuously to avoid catastrophic events, ensure comfort, safety,
and also improved performance while reducing life cycle costs. The suspension
system is a very important part of the railway vehicle which supports the
car-body and the bogie, isolates the forces generated by the track unevenness
at the wheels and also controls the attitude of the car-body with respect to
the track surface for ride comfort. Its reliability is directly related to the
vehicle safety. The railway vehicle suspension often develops faults; worn
springs and dampers in the primary and secondary suspension. To avoid a
complete system failure, early detection of fault in the suspension of trains
is of high importance. The main contribution of the research work is the
prediction of faulty regimes of a railway
vehicle suspension based on a hybrid model. The hybrid model framework
is in four folds; first, modeling of vehicle suspension system to generate
vertical acceleration of the railway vehicle, parameter estimation or
identification was performed to obtain the nominal parameter values of the
vehicle suspension system based on the measured data in the second fold,
furthermore, a supervised machine learning model was built to predict faulty
and healthy state of the suspension system components (damage scenarios) based
on support vector machine (SVM) and lastly, the development of a new SVM model
with the damage scenarios to predict faults on the test data. The level of
degradation at which the spring and damper becomes faulty for both primary and secondary suspension system was
determined. The spring and damper becomes faulty when the nominal values
degrade by 50% and 40% and 30% and 40% for the secondary and primary suspension
system respectively. The proposed model was able to predict faulty components
with an accuracy of 0.844 for the primary and secondary suspension system.