A novel scheme for multivariate statistical fault detection with application to the Tennessee Eastman process

Abstract

<p style='text-indent:20px;'>Canonical correlation analysis (CCA) has gained great success for fault detection (FD) in recent years. However, it cannot preserve the prior information of the underlying process. To cope with these difficulties, this paper proposes an improved CCA-based FD scheme using a novel multivariate statistical technique, called sparse collaborative regression (SCR). The core of the proposed method is to take the prior information as a supervisor, and then integrate it with CCA. Further, the <inline-formula><tex-math id="M1">\begin{document}$ \ell_{2,1} $\end{document}</tex-math></inline-formula>-norm is employed to reduce redundancy and avoid overfitting, which facilitates its interpretability. In order to solve the proposed SCR, an efficient alternating optimization algorithm is developed with convergence analysis. Finally, some experimental studies on a simulated example and the benchmark Tennessee Eastman process are conducted to demonstrate the superiority over the classical CCA in terms of the false alarm rate and fault detection rate. The detection results indicate that the proposed method is promising.</p>

Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

“…The correlation algorithm can provide a practical basis for fault diagnosis based on ensuring the correlation analysis of signal detection data. The FP Growth algorithm is often used in data mining processing because of its fewer traversal times and the ability to compress the data, but its application effect will be affected by the data type [2]. Therefore, it is studied to improve the traditional FP Growth algorithm and discretize the weight value of the text feature based on considering the type and characteristics of the railway fault causes, to ensure the objectivity and standardization of data processing, and adaptively set the confidence and support to reduce the non-objectivity of manually set parameters, to detect and analyze the railway signal fault problems with the improved FP Growth algorithm.…”