On-line condition monitoring and fault diagnosis in hydraulic system components using parameter estimation and pattern classification

Safety and functionality of a fluid power control system can considerably be increased by implementing predictive maintenance routines. Modern predictive maintenance practices are based on automatic condition monitoring and fault diagnosis of the system components.; The theme of this thesis is on automatic generation of fault symptoms in the form of qualitative variation of system physical parameters by on-line processing of low-quality raw sensor data. To accomplish this, a novel model-based methodology has been proposed that has integrated four levels of information processing in a structured hierarchy: (1) State/parameter estimation of the hydraulic system components using state-space models, stochastic signal processing techniques such as Kalman filtering, and raw sensor data from the hydraulic system. (2) Monitoring and change detection in the identified parameters of the system components, using statistical tests, such as sequential probability ratio test. (3) Generation of fault symptoms in the form of qualitative changes in the physical parameter values, such as "increased", "decreased", etc. (4) Fault recognition by fault symptom classification using neural network pattern classifiers. (5) Fault diagnosis maintenance aiding using knowledge-based expert systems.; Using a second-order linear system as an example, we have shown how each element of the proposed hierarchical methodology effectively processes the lower quality data received from the previous element and provides higher quality information for the next element in the hierarchy, so that an incipient fault or an abrupt failure can be successfully detected and diagnosed. The proposed fault detection and diagnosis (FDD) technique has also been applied on a real hydraulic test rig which has been built in the Robotics and Control laboratory, at UBC. The hydraulic test rig has a two-stage proportional directional flow control valve, which has been thoroughly modelled for simulation of faults.; Nonlinear state-space models have been developed for various hydraulic components, including the two-stage servovalve, a hydraulic cylinder, and a manipulator. Extended Kalman Filtering (EKF) is applied on the state-space models to get the parameter estimates. Only low-cost robust sensors such as pressure transducers and position sensors have been used for this purpose. More expensive or hard-to-measure states such as flow rates and orifice areas are predicted using novel state-space models.; One of the major achievements of this thesis has been incorporation of a novel state-space model for a valve orifice area that allows us not only to obtain accurate estimates of the flow rate through the valve, but also to detect several incipient faults and abrupt failures in the valve and its connecting ports. No a priori knowledge about the orifice profile or the spool deadband size is assumed. (Abstract shortened by UMI.)...