Chuanhai Chen

Bio: Chuanhai Chen is an academic researcher from Jilin University. The author has contributed to research in topics: Computer science & Reliability (statistics). The author has an hindex of 7, co-authored 65 publications receiving 171 citations. Previous affiliations of Chuanhai Chen include Chinese Ministry of Education.

New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions

Abstract: Deep learning models are widely used in fault diagnosis to learn hierarchical representations from collected signals. However, most of the models depend considerably on the assumption that training (source domain) and test (target domain) data sets are from the same feature distribution. This assumption is difficult to meet in practical scenarios of industrial applications because the working conditions of rotating machinery change with different machining tasks and labelled data with fault information are difficult and expensive to collect. Therefore, a novel fault diagnosis method, called multilayer adaptation convolutional neural network (MACNN), is constructed to solve the above-mentioned problems. The method regards raw temporal signals as input and uses wide kernels following a multiscale convolutional module to capture low-frequency features at multiple scales in shallow layers. Then, small convolutional kernels are used to implement multilayer nonlinear mapping in deep layers. Adaptive batch normalisation and multi-kernel maximum mean discrepancy are combined to reduce the feature distribution discrepancy in shallow and deep layers of the model, respectively, which improves the domain adaptation capability of the model. The proposed method is validated through 12 fault diagnosis experiments. The average 99.21% diagnosis precision demonstrates the reliability and stability of the method under different working loads.

Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS

Abstract: Failure mode and effects analysis (FMEA) is a widely used technique for identifying, evaluating, and eliminating potential failures in production, system, and process. The traditional FMEA ranks th...

Load spectrum generation of machining center based on rainflow counting method

Abstract: Reliability bench tests and probability design method are two important means to improve the reliability of machine tools, while the load spectrum of machine tools is the foundation of reliability bench tests and probability design. According to the load spectrum, the actual working conditions can be simulated in laboratories. A dynamic load spectrum generation method is proposed to establish a representative load spectrum. Firstly, the cutting load measuring system is established based on the characteristics of the cutting loads, and then the actual cutting experiments designed by the orthogonal experimental method are conducted on the basis of the typical cutting conditions in laboratories. Secondly, the counting method of the cutting loads cycles is presented based on the dynamic load characteristics of a machining center. And loads cycles are counted by the proposed counting method, and then a rainflow matrix is formed. Thirdly, in order to improve the precision of the load spectrum the extrapolation of the loads is carried out using the parametric extrapolation method. Then the probability distribution functions of the mean and amplitude of the cutting loads are provided by the K-S goodness-of-fit test method. The case study indicates that the radial force, axial force, and cutting torque of the tested machining center follow gamma, normal, and Weibull distributions with different parameters, respectively. Finally, the joint distribution function of the mean and amplitude of the radial force, axial force, and cutting torque is obtained by using a combination of statistical analysis method, and the two-dimensional load spectrum of the MC is compiled.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Risk Evaluation in Failure Mode and Effects Analysis Based on D Numbers Theory

Abstract: Failure mode and effects analysis (FMEA) is a useful technology for identifying the potential faults or errors in system, and simultaneously preventing them from occurring. In FMEA, risk evaluation is a vital procedure. Many methods are proposed to address this issue but they have some deficiencies, such as the complex calculation and two adjacent evaluation ratings being considered to be mutually exclusive. Aiming at these problems, in this paper, A novel method to risk evaluation based on D numbers theory is proposed. In the proposed method, for one thing, the assessments of each failure mode are aggregated through D numbers theory. For another, the combination usage of risk priority number (RPN) and the risk coefficient newly defined not only achieve less computation complexity compared with other methods, but also overcome the shortcomings of classical RPN. Furthermore, a numerical example is illustrated to demonstrate the effectiveness and superiority of the proposed method.

A review of prognostics and health management of machine tools

Abstract: This paper presents a survey of the applications of prognostics and health management maintenance strategy to machine tools. A complete perspective on this Industry 4.0 cutting-edge maintenance policy, through the analysis of all its preliminary phases, is given as an introduction. Then, attention is given to prognostics, whose different approaches are briefly classified and explained, pointing out their advantages and shortcomings. After that, all the works on prognostics of machine tools and their main subsystem are reviewed, highlighting current open research areas for improvement.