The application of artificial intelligence to fault detection in hydraulic cylinder drive systems.
An expert system approach to fault diagnosis of fluid power circuits
is considered with emphasis on leakage flow detection, and for valvecontrolled
cylinders. Two test rigs were used, one being a solenoid-valve
controlled cylinder operated directly and in an open-loop mode, the other
being a servo-valve controlled actuator operated by microcomputer and in
a closed-loop mode. Both systems incorporated the use of on-line
dynamic data, and for the closed-loop case operation and fault diagnosis
was integrated into an automated procedure.
Flow leakage detection was considered a priority, and an alternative
approach using displaced volumes was successfully implemented. The
research work concentrated initially on the use of an expert system and the
establishment of an appropriate knowledge base using a hybrid reasoning
approach. This approach was found to be excellent for single-fault
conditions but could not differentiate components of multiple-fault
conditions, other than that they existed, due to the use of a minimum
number of flow sensors.
Additional techniques were then considered for the closed-loop
control system utilising steady-state position error, time series analysis,
and Artificial Neural Networks. It was found that the consideration of
steady-state error gave information complementary to the existing
knowledge base but could not give any additional information. The use of
an artificial neural network was found to give more information with
regards to multiple-fault conditions, resulting in a percentage probability
for each fault combination.