You Cai Xu's research while affiliated with Kunming University of Science and Technology and other places

This paper is aimed at the security problems of elevator system.Fuzzy fault tree analytic method is applied to study the reliability of the elevator system.First of all, the elevator system is divided into four subsystems, and then the paper take elevator fall accident as the top event as a example, applying the ascending method for qualitative analysis to find out 7 minimum cut sets in the fault tree and 15 bottom event structure importance sorting. Finally, the fuzzy fault interval probability come out through introducing the concept of fuzzy number in fuzzy set theory, using the triangular fuzzy number to describe the probability of basic event occurrence in the fault tree, making an quantitative interval probability calculation in middle events and top events in the fault tree..The results show that the fuzzy fault tree is an effective and practical method for reliability analysis when we are difficult or unable to obtain the accurate probability of bottom events in terms of the elevator system. Compared with the traditional method of fault tree, the calculated result is more scientific and reasonable, closer to the engineering practice.

Empirical mode decomposition (EMD) can extract real time-frequency characteristics from the non-stationary and nonlinear signal. Variable prediction model based class discriminate (VPMCD) is introduced into roller bearing fault diagnosis in this paper. Therefore, a fault diagnosis method based on EMD and VPMCD is put forward in the paper. Firstly, the different feature vectors in the signal are extracted by EMD. Then, different fault models of roller bearing are distinguished by using VPMCD. Finally, an simulation example based on EMD and VPMCD is shown in this paper. The results show that this method can gain very stable classification performance and good computational efficiency.

The tradition elevator risk assessment model depends on the expert experience, which causes that the assessment process takes a long time. To deal with this problem, this paper proposes a new risk assessment model which is based on fuzzy analytic hierarchy process (F-AHP) and artificial neural network (ANN). This model is applied to the risk-assessment of elevators. The results show that the assessment time is shorter and the accuracy is not lower, in comparison with the traditional model.

As a new nonlinear and non-stationary signal analysis method,local mean decomposition (LMD) has a good adaptability. We decompose the original non-stationary acceleration vibration signals into several stationary production function (PF).But performing LMD will produce end effects which make results distorted. A hidden Markov model (HMM)-based speech recognition system for Chinese spell.After analyzing reasons for end effects of LMD in detail,a new method based on weighted matching similar waveform was proposed.Experiments in speech recognition to the production function as the training model, the more traditional identification method to identify higher rates. LMD is an effective method. It is feasible to extract the feature from speech signals with LMD.

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.