This paper focuses on the issue of outlier detection for time series in the process industry. Considering the characteristics of time series in process control systems, such as high non-linearity, strong noise and the special relationship between the input and output of the controlled object, a new outlier detection algorithm is proposed. The algorithm adopts an improved Radial Basis Function Network to construct the model of the controlled object and an Auto-Regression Hidden Markov Model to detect outliers. Unlike many conventional outlier detection methods, this algorithm does not need any prior data and can detect outliers accurately without preselecting the threshold. The proposed detection algorithm is validated by the application to the electrode regulator system of an arc furnace and comparison with Takeuchi's auto-regressive model detection approach.

No abstract

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.