Reliability evaluation method and algorithm for electromechanical product

The reliability of electromechanical product is usually determined by the fault number and working time traditionally. The shortcoming of this method is that the product must be in service. To design and enhance the reliability of the electromechanical product, the reliability evaluation method must be feasible and correct. Reliability evaluation method and algorithm were proposed. The reliability of product can be calculated by the reliability of subsystems which can be gained by experiment or historical data. The reliability of the machining center was evaluated by the method and algorithm as one example. The calculation result shows that the solution accuracy of mean time between failures is 97.4% calculated by the method proposed in this article compared by the traditional method. The method and algorithm can be used to evaluate the reliability of electromechanical product before it is in service.

[1]  Xiaoping Du,et al.  Reliability sensitivity analysis with random and interval variables , 2009 .

[2]  Hong-Chan Chang,et al.  Composite reliability evaluation model for different types of distribution systems , 2003 .

[3]  Youmin Zhang,et al.  A revisit to block and recursive least squares for parameter estimation , 2004, Comput. Electr. Eng..

[4]  Ralf B. Bergmann,et al.  On the origin of logarithmic-normal distributions: An analytical derivation, and its application to nucleation and growth processes , 2008, 0807.0396.

[5]  Li Jie,et al.  PROBABILITY DENSITY EVOLUTION EQUATIONS — A HISTORICAL INVESTIGATION , 2009 .

[6]  Željko Marušić,et al.  MAINTENANCE RELIABILITY PROGRAM AS ESSENTIAL PREREQUISITE OF FLIGHT SAFETY , 2009 .

[7]  Michael Tortorella,et al.  Improved reliability-prediction and field-reliability-data analysis for field-replaceable units , 2002, IEEE Trans. Reliab..

[8]  Rakesh Sehgal,et al.  Reliability evaluation and selection of rolling element bearings , 2000, Reliab. Eng. Syst. Saf..

[9]  Seung-Woo Lee,et al.  Assessment of the tool post reliability of a high-stiffness turning machine , 2007 .

[10]  Jan Sijbers,et al.  Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls , 2013, NeuroImage.

[11]  K. Kromp,et al.  Statistical properties of Weibull estimators , 1991 .

[12]  Paul S. Ray,et al.  Control Charts for Monitoring Field Failure Data , 2006, Qual. Reliab. Eng. Int..

[13]  Ping Jiang,et al.  Real-time reliability evaluation based on damaged measurement degradation data , 2012 .

[14]  Kuolung Hu,et al.  A new approach to system reliability , 2001, IEEE Trans. Reliab..

[15]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[16]  Leung Kit-Nam Francis OPTIMAL REPLACEMENT POLICIES DETERMINED USING ARITHMETICO-GEOMETRIC PROCESSES , 2001 .

[17]  Jeff Tian,et al.  Better Reliability Assessment and Prediction through Data Clustering , 2002, IEEE Trans. Software Eng..

[18]  Hui Li,et al.  INVESTIGATION OF SEISMIC DAMAGE OF CABLE-STAYED BRIDGES WITH DIFFERENT CONNECTION CONFIGURATION , 2009 .

[19]  M T Todinov Reliability Analysis Based on the Losses from Failures , 2006, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  Hong-Zhong Huang,et al.  Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment , 2013, Int. J. Syst. Sci..

[21]  Xufang Zhang,et al.  Reliability sensitivity-based correlation coefficient calculation in structural reliability analysis , 2012 .

[22]  Yu Yang,et al.  Reliability Prediction Methods and Application of Large Capacity Generating Units , 2011 .