Background Intracerebral hemorrhage (ICH) is a devastating disease, its mortality and disability rate are high. In China, hypertensive intracerebral hemorrhage (HICH) is responsible for 75% of all the cases of primary ICH. A lot of randomized controlled trials (RCTs) of traditional Chinese medicine (TCM) for treating HICH have been carried out. However, these RCTs have a lot of problems, such as heterogeneous outcomes, non-uniform point of measurement. These lead to systematic review/meta-analysis only can include a small number of studies. And outcome measures did not take the wishes of patients and other stakeholders into account. The aim of this study is to establish the core outcome set (COS) for future TCM clinical trials of HICH. Methods and analysis First, we will develop a long list of general outcomes by making systematic literature review and semi-structured interviews. Then healthcare professionals and patients with HICH will be invited to participate in two rounds of the Delphi survey to determine the importance of the outcome. Finally, a face-to-face consensus meeting will be conducted to determine the final COS of HICH, including what outcomes should be measured and when and how to measure the outcomes. Results We aim to develop a COS that includes TCM core syndrome for HICH to determine what outcomes should be reported and when and how to measure them. Conclusion By doing this, we can increase the reporting consistency and reduce the reporting bias in the outcome, which leads to the reuse of research data in meta-analysis and the making of informed healthcare decisions. Ethics and dissemination The entire project has received approval from the Ethics Committee of Xiyuan Hospital, China Academy of Chinese Medical Sciences. The final COS will be published and reported at the national and international conferences. Trial registration This study is registered with the Core Outcome Measures in Effectiveness Trials database as study 1475. Registered on December 2019.

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For a problem of mode mixing occurs in implementation process of local mean decomposition (LMD) method, an analytical method based on ensemble local mean decomposition (ELMD) and neural network is proposed to apply to fault diagnosis of rolling bearing, the vibrational signal of rolling bearing is decomposed into a series of product functions(PF) by ELMD method. The PF components which contain main fault information are selected to perform a further analysis. The kurtosis coefficient and energy characteristic parameters extracted from these PF components can be used as the input parameters of the neural network to identify the working status and fault types of rolling bearing. Through the analysis of rolling bearing with fault-free, inner-race fault and outer-race fault, the results indicate that the method based on ELMD and neural network has a higher failure recognition rate than the method based on wavelet packet analysis and neural network, and the working status and fault types of rolling bearing can be identified accurately and effectively.

For differences of time-domain energy distribution of different gear fault vibration signal, an analytical method based on local mean decomposition (LMD) and least squares support vector machine (LS-SVM) is proposed to apply to gear fault diagnosis. First vibrational signal of gear is decomposed into a series of product functions (PF) by LMD method. Then extracting energy characteristic parameters of PF components which contain main fault information to constitute a fault feature vectors, which is considered as input sample of well-trained LS-SVM, and then identifying working state and fault type of different gear can be identified accurately and effectively by diagnostic method based on LMD and LS-SVM.

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.