Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear sy...Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.展开更多
A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault d...A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.展开更多
文摘Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.
文摘A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.
