The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibrat...The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.展开更多
Today there is a big interest in reducing the maintenance costs and in increasing the reliability of machines in continuous operation. Therefore, maintenance on condition is used. State-of-the-art is a trend analysis ...Today there is a big interest in reducing the maintenance costs and in increasing the reliability of machines in continuous operation. Therefore, maintenance on condition is used. State-of-the-art is a trend analysis and a fault prediction made only based on sensor signals and stochastic methods. The identification possibilities of this technique are limited. A new concept for model-based monitoring has been developed for more detailed fault identification. The developed concept determines the condition of a machine after the occurrence of a fault. The concept is based on a simulation including various faults and an optimization tool. The development of a cost function and the optimization is one of the challenges of such a concept. Using an AMB rotor system with an auxiliary bearing, the new concept of model-based monitoring is investigated using experiments and the optimization is discussed in this paper.展开更多
基金Projects(51375484,51475463)supported by the National Natural Science Foundation of ChinaProject(kxk140301)supported by Interdisciplinary Joint Training Project for Doctoral Student of National University of Defense Technology,China
文摘The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.
基金supported by a fellowship within the Postdoc-Programme of the German Academic Exchange Service (DAAD)
文摘Today there is a big interest in reducing the maintenance costs and in increasing the reliability of machines in continuous operation. Therefore, maintenance on condition is used. State-of-the-art is a trend analysis and a fault prediction made only based on sensor signals and stochastic methods. The identification possibilities of this technique are limited. A new concept for model-based monitoring has been developed for more detailed fault identification. The developed concept determines the condition of a machine after the occurrence of a fault. The concept is based on a simulation including various faults and an optimization tool. The development of a cost function and the optimization is one of the challenges of such a concept. Using an AMB rotor system with an auxiliary bearing, the new concept of model-based monitoring is investigated using experiments and the optimization is discussed in this paper.
