This article is devoted to providing a method for quantifying fault diagnosability of nonlinear networked control systems under weak transmission conditions. The receiving signals of fault detection and isolation systems are characterized under packet dropouts and communication constraints with the help of Bernoulli random variables and a round-robin protocol. Then, the statistical characteristics of system dynamics under the impact of different faults are characterized by defined statistical functions combining probability density functions. Furthermore, a quantitative fault diagnosability evaluation framework for nonlinear networked control systems is established based on Takagi–Sugeno fuzzy models and Kullback–Leibler divergence. Moreover, an evaluation metric is proposed, which can be applied to system dynamics following both Gaussian distribution and mixed distribution consisting of Bernoulli distribution and Gaussian distribution, eliminating the constraint of assuming purely Gaussian distribution. In addition, a necessary and sufficient condition is given to reveal the relations between the qualitative fault diagnosability evaluation results and the proposed metric.

Credit evaluation of customer is an important task for gas companies to achieve marketing management, and is of great significance to improve the economic efficiency of enterprises. By analyzing various factors influencing gas customer credit, a gas customer credit index hierarchy is established. Then, based on the credit index hierarchy, this paper proposes a hybrid credit evaluation method by cluster analysis and analytic hierarchy process(AHP). This method first divides gas customers into different groups by cluster analysis, then determines the credit index weight by AHP, and finally evaluates gas customer credit rating by combining the above two results. The empirical analysis of the actual data of a gas company shows that as a synthesis of customer data's statistical properties and gas professionals' actual work experience, this model can evaluate gas customer credit rating rationally and effectively.