In this paper, we evaluate multivariate pattern matching methods for the Tennessee Eastman (TE) challenge process. The pattern matching methodology includes principal component analysis based similarity factors and dissimilarity factor of Kano et al., that compare current and historical data. In our similarity factor approach, the start and end times of disturbances are not known a priori and t...

In this work we investigate the matter of “secure control” – a novel research direction capturing security objectives specific to Industrial Control Systems (ICS). We provide an empirical analysis of the well known Tennessee Eastman process control challenge problem to gain insights into the behavior of a physical process when confronted with cyber-physical attacks. In particular, we investigat...

A feed-forward neural network is proposed for monitoring operating modes of large scale processes. A Gaussian hidden layer associated with a Kohonen output layer map the principal features of measurements of state variables. Subsets of selective neurons are generated into the hidden layer by means of self adapting of centers and dispersions parameters of the Gaussian functions. The output layer...

In the recent years, deep learning has been widely used in process monitoring due to its strong ability extract features. However, with increasing layers of network, compression features by model will lead loss some valuable information and affect model’s performance. To solve this problem, a fault detection method based on discriminant enhanced stacked auto-encoder is proposed. An network stru...

Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) introduced extract low- high-order from raw data. Support vector description (SVDD), can deal p...

Abstract With the rapid development of deep learning methods, variational auto-encoder (VAE) has been utilized for nonlinear process monitoring. However, most VAE-based methods hardly consider inner independent and related relationship each variable. To overcome this problem, a novel VAE named variable information concentrated (IRVIC-VAE) is proposed. concentrate information, loading weight mat...

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems different critical infrastructures, addressing cyber-security challenges these systems is vital for assuring their reliability and secure operation presence malicious cyber attacks. Towards this end, developing a testbed generate real-time data-sets infrastructure that would be utilized validation attack detection...

The focus of this work is on Statistical Process Control (SPC) a manufacturing process based available measurements. Two important applications SPC in industrial settings are fault detection and diagnosis (FDD). In work, deep learning (DL) methodology proposed for FDD. We investigate the application an explainability concept (explainable artificial intelligence (XAI)) to enhance FDD accuracy ne...

Chemical processes usually exhibit complex, high-dimensional and non-Gaussian characteristics, the diagnosis of faults in chemical is particularly important. To address this problem, paper proposes a novel fault method based on Bernoulli shift coyote optimization algorithm (BCOA) to optimize kernel extreme learning machine classifier (KELM). Firstly, random forest treebagger (RFtb) used select ...