A multi-objective optimal sensor placement method based on quantitative evaluation of fault diagnosability is proposed. Fault diagnosability evaluation is the basis of fault diagnosis, and insufficient sensor point information is the main reason for the low quantitative evaluation index of fault diagnosability. Therefore, a method to improve the fault diagnosability of a system by adding soft and hard sensors is presented. However, increasing sensors is limited by the constraints of cost, reliability and complexity. In view of this and based on ensuring the fault diagnosability, a multi-objective optimization method for the sensors is proposed to improve system reliability and promote development towards stability, efficiency and economy.

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Fault diagnosability is the basis of fault diagnosis. Fault diagnosability evaluation refers to whether there is enough measurable information in the system to support the rapid and reliable detection of a fault. However, due to unavoidable measurement errors in a system, a quantitative evaluation index of system fault diagnosability is inadequate. In order to overcome the adverse effects of measurement errors, improve the accuracy of the quantitative evaluation of fault diagnosability, and improve the safety level of the system, a method for a permissible area analysis of measurement errors for a quantitative evaluation of fault diagnosability is proposed in this paper. Firstly, in order for the residuals obey normal distribution, a design method of the permissible area of measurement errors based on the Kullback–Leibler divergence (KLD) is given. Secondly, two key problems in calculating the KLD are solved by sparse kernel density estimation and the Monte Carlo method. Finally, the feasibility and validity of the method are analyzed through a case study.

In this paper, the problem of robust integrity considering actuator structural failures for a class of uncertain nonlinear networked control system (NNCS) subject to actuator saturation is investigated. By utilizing the delay-dependent Lyapunov method and convex combination expression of input saturation function, the less conservative stability criteria and the approach of robust fault-tolerant state feedback controller are derived for the control problem. By introducing a definition of the fault-tolerant attraction domain and solving an optimization problem with LMIs constraints, the maximum fault-tolerant attraction domain can be estimated. The key features of the proposed approach include the use of a tighter bounding technique for reducing conservativeness and the introduction of augmented matrix items into the Lyapunov-Krasovakii functional for reducing computational demand. Finally, an example is used to illustrate the effectiveness and the feasibility of proposed approach.

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This study investigates the research on nickel-cobalt-copper productive collaboration and intelligent decision-making technology for symbiotic coupling enterprises in the Gansu Province of China. The aim is to address the problems of low resource utilization efficiency, weak production collaboration, and an insufficient intelligent decision-making level in the nonferrous metallurgy industry. First, the present situation of nickel-cobalt-copper industry chain-level collaboration in the agglomeration area is analyzed extensively, and the corresponding problems are proposed. Second, the functional framework of productive collaboration and intelligent decision-making is presented from the industrial chain and industrial agent levels. In addition, the design methods of various balance strategies in the production collaboration within the industrial agent are provided. These can realise the daily balance of material, metal, and energy data in an individual industrial agent. Finally, with regard to intelligent decision-making at the industrial chain level, six key measures surrounding different themes are provided to support the implementation of productive collaboration and intelligent decision-making in the nonferrous metallurgy agglomeration area.

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In this paper, a method of incipient fault diagnosis and amplitude estimation based on Kullback–Leibler (K–L) divergence is proposed. An incipient fault is usually regarded as the precursor of a significant system fault, but due to a low amplitude and non-obvious characteristics, it is easy for such a fault to be hidden by disturbance and noise. Based on this and considering the sensitivity of the K–L divergence method in data feature extraction, a method of diagnosing incipient faults is designed. In order to consider the safety performance and lay a foundation for the fault tolerance of the system, an amplitude estimation method for incipient faults is also proposed. By mapping the characteristic change in the residual data to the numerical change in the K–L divergence, the amplitude of the incipient fault can be measured with high sensitivity. Considering the generality of the method, a Gaussian mixture model is used to model the residual data in order to increase the accuracy of fault amplitude estimation. Finally, the effectiveness of the proposed method for incipient fault diagnosis and amplitude estimation is verified by experiment.