Performance Evaluation and Comparison of Multi - Objective Optimization Algorithms for the Analytical Design of Switched Reluctance Machines

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Ammonia storage and slip under steady and transient state in close-coupled SCR

The close-coupled selective catalytic reduction(cc-SCR)catalyst is an effective technology to reduce tailpipe NOx emission during cold start.This paper investigated the optimal ammonia storage under steady and transient state in the cc-SCR.The study showed that a trade-off between NOx conversion efficiency and ammonia slip is observed on the pareto solutions under steady state,and the optimal ammonia storage is calculated with ammonia slip less than 10μL/L based on the ChinaⅥemission legislation.The rapid temperature increase will lead to severe ammonia slip in the transient test cycle.A simplified 0-D calculation method on ammonia slip under transient state is proposed based on kinetic model of ammonia adsorption and desorption.In addition,the effect of ammonia storage,catalyst temperature and temperature increasing rate on ammonia slip are analyzed.The optimal ammonia storage is calculated with maximum ammonia slip less than 100μL/L according to the oxidation efficiency of ammonia slip catalyst(ASC)downstream cc-SCR.It was found that the optimal ammonia storage under transient state is much lower than that under steady state in cc-SCR at lower temperature,and a phase diagram is established to analyze the influence of temperature and temperature increasing rate on optimal ammonia storage.

Sensor Optimization Selection Model Based on Testability Constraint

Sensor selection and optimization is one of the important parts in design for testability. To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability, a novel sensor optimization selection model is proposed. Firstly, a universal architecture for sensor selection and optimization is provided. Secondly, a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability. Thirdly, a sensor selection and optimization model for prognostics and health management is constructed, which takes sensor cost as objective function and the defined testability indexes as constraint conditions. Due to NP-hard property of the model, a generic algorithm is designed to obtain the optimal solution. At last, a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform. The application results and comparison analysis show the proposed model and algorithm are effective and feasible. This approach can be used to select sensors for prognostics and health management of any system.