A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

No abstract

In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user's motion intention. In this paper, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the endeffector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user's motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.

No abstract