Papers by Author: Jian Hong Yang

Abstract: The characteristic of cyclical impact is reflected on the signal of rolling bearing in fault condition. The carrier frequency is modulated by times of the failure frequencies. When the traditional cyclical spectrum density (CSD) method is used to analyze the signal, all the modulation frequencies will be demodulated in the cyclic frequency spectrum. In this case, it is difficult to recognize the fault type of the bearing. Therefore, a new cyclical spectrum density method based on the kurtosis energy (CSDK) is proposed. The kurtosis of every cyclic frequency’s slice is used as the weight coefficient of the cyclic frequency’s energy accumulation to extract fault feature effectively. The proposed method has greatly reduced times of the harmonic frequencies’ effect in traditional CSD method. The analysis of the signal gathered from the outer rolling bearing of blast furnace belt cylinder shows that the fault feature extracted by the new method is more clear and accurate than CSD method.

Abstract: In order to solve the time delay problem between the process parameters and the quality indicators in the modeling processes, a method of time delay estimation on COREX parameters is proposed based on Dynamic Time Warping (DTW) algorithm. The method solves the problem existing in the conventional methods which demand the number of calculating sample to be same. Taking the real field data from Baosteel COREX-3000 as the research object, the DTW distances between the process parameters and the quality indicators are calculated, and then the delay time is estimated. The real field data are used for verification, the results show that the proposed method can estimate the time daley effectively, and the prediction accuracy of model which used time delay estimation becomes higher. It provides an effective measure for model preprocessing.

Abstract: Aiming at the problems, such as difficulties in deployment and immovability during roller changing, which exist in traditional cold rolling backup roller bearing temperature monitoring technologies, a new temperature remote monitoring method based on wireless sensor network (WSN) is proposed. With a structure design of high water resistant and anti-vibration level, the monitoring sensor node can adapt to hot, humid and vibrating working environment. Autonomous power supply and wireless communication facilitate the installation of sensor nodes and bring good movability to sensor nodes. The topologic structure of the WSN is multi-layered and cluster-based. The cluster layer is mesh style, which can guarantee valid data communication while the sensor nodes moving into and out from the network during backup roller changing. Inside a cluster, star style network topology and the time division multiple access (TDMA) communication protocol are used to reduce node energy consumption and to prolong the network life. Remote communication between WSN and enterprise local area network is realized through a multi-hop relay tactic. Practical application in industrial fields shows that the proposed method is efficient to deal with the harsh and complicated operation environment, which is of great significance to monitoring bearing working condition accurately and in time.

Abstract: Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

Abstract: Aimed at the problem of low resolution and cross term interference of the traditional time-frequency analysis methods, a new time-frequency filtering method based on generalized S transform is proposed. The method is extended under the premise of the linearity, lossless invertibility, high time-frequency resolution of S transform. On the basis, a coefficient which is direct to the signal energy distribution is introduced. In this way, the resolution of the S transform can be adjust adaptively. Eventually, this method is applied to the time-frequency filtering. The results of simulation and faulty bearing show that the proposed methodology can achieve good effect of noise reduction, and be more suitable for the non-stationary characteristics of vibration signals.

Abstract: Selecting feature in slow-speed and heavy-load equipments has always been a difficult problem. A new feature selection method based on Laplacian Score is used to Acoustic Emission signal. The more capable of describing the sample clustering property, the more important the selected feature is. The method is a ‘filter’ and unsupervised feature selection method which is just dependent on the space distribution of the sample instead of classifier. Therefore, the method enjoys a simple algorithm and low complexity. The effectiveness of the method is verified by the AE datasets from the bearings of a blast furnace’s belt conveyor.

Abstract: In COREX processes, the cold gas is produced in melter gasifier, after being cooled and dust controlled, blown into the blast furnace and used in the reduction reaction. The cold gas content plays a key role in the reaction of lump ore and pellets reduction. A prediction model of COREX cold gas content of carbon dioxide is proposed based on modified orthogonal signal correction partial least squares algorithm (MOSC-PLS). Firstly, the input and output variables of the model are selected according to the COREX processes principle. Secondly, MOSC algorithm is used to preprocess the data, in order to remove the irrelevant information between the input and output variables of the model. Finally, prediction model is built based on PLS. The real field data of cold gas content of carbon dioxide from Baosteel COREX are used for verification. The results show that MOSC-PLS has an advantage over the orthogonal signal correction partial least squares (OSC-PLS) in prediction accuracy. Thus the necessary decision supports and analysis tools for the cold gas content control are provided.

Abstract: A method for setting up flexible and web based remote laboratory for mechatronics system experiment course is proposed. Unlike most current remote laboratories, in which the experiment procedures are always fixed, the new remote laboratory provides more flexibility to students in designing and testing programs and watching experiment results remotely via network. The proposed remote laboratory is implemented through windows remote desktop and web service technology, and also includes a course web site which is established for experiment selection and management. The whole new remote laboratory platform is easy to develop and maintain, and has been used during teaching practice of mechatronics system experiment course in University of Science and Technology Beijing, covering experiments of PLC-based stepper motor control and DAQ card based condition monitoring.

Abstract: The application of AE measurement for condition monitoring of the slow-speed and heavy-load equipment is gaining ground. However, different AE features are sensitive to fault in varying degrees when they are employed in the trend analysis. Therefore, in this paper the focus is on the selection of the appropriate AE features for the trend analysis. First the AE features are ranked by Laplacian Score method according to their importance, and then a new feature index is obtained by the fusion of the features with their ranking scores, which serve as weight coefficient in this condition. The degradation data of the bearings in the belt conveyor are used to prove that the proposed method is effective.

Abstract: In order to monitor nonlinear production process effectively, multivariate statistical process control based on kernel principal component analysis is applied to process monitoring and diagnosis. Squared prediction error (SPE) statistic of the kernel principal component analysis (KPCA) model is used for process monitoring, and the fault causes of the production process could be tracked by the methods of data reconstruction and the optimal neighbor selection strategy. Simulation data and Tennessee Eastman process data are used for model validation, as a result the proposed method has better performance on abnormality detecting, compared with multivariate statistical process control based on linear principal component analysis. What is more, the causes of the faults are tracked effectively, thus the production process can be adjusted to prevent substandard products.