Due to the importance of rotating machinery as one of the most widely used industrial element, development a proper monitoring and fault diagnosis technique to prevent malfunction and failure of machine during operation is necessary. This paper presents a method for gearbox fault diagnosis based on feature extraction technique, distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, the features of raw data are extracted through the wavelet packet transform (WPT) and timedomain statistical features. Secondly, the compensation distance evaluation technique is applied to select optimal feature via sensitivities ranking. Finally, the optimal features are input into the SVMs to identify different faults. The diagnosis result shows that the SVMs ensemble is able to reliable recognize not only different faults styles and severities but also the compound faults in high accurate rate.

Post-stroke spasticity seriously affects patients’ quality of life. Spasticity is considered to involve both neural and non-neural factors. Current clinical scales, such as the Modified Ashworth Scale and the Modified Tardieu Scale, lack reliability and reproducibility. These scales are also unable to identify the neural and non-neural contributions to spasticity. Surface electromyography and biomechanical and myotonometry measurement methods for post-stroke spasticity are discussed in this report. Surface electromyography can provide neural information, while myotonometry can estimate muscular properties. Both the neural and non-neural contributions can be estimated by biomechanical measurement. These laboratory methods can quantitatively assess spasticity. They can provide more valuable information for further study on treatment and rehabilitation than clinical scales.

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