Abstract
Cyber-Physical Systems (CPS) in heavy industry are a combination of closely integrated physical processes, networking, and scientific computing. The physical production process is monitored and controlled by the CPS in question, through advanced real-time networking systems, where high-precision feedback loops can be changed when the overgrid of cooperative computing and communication components that make up the industrial process is required. These CPS operate independently but integrate interaction capabilities as well as with the external environment, creating the connection of the physical with the digital world. The outline is that the most effective modeling and development of high-reliability CPS are directly related to the maximization of the production process, extroversion, and industrial competition. In this paper, considering the high importance of the operational status of CPS for heavy industry, an innovative autonomous anomaly detection system based on unsupervised disentangled representation learning is presented. It is a temporal disentangled variational autoencoder (TDVA) which, mimicking the process of rapid human intuition, using high- or low-dimensional reasoning, finds and models the useful information independently, regardless of the given problem. Specifically, taking samples from the real data distribution representation space, separating them appropriately, and encoding them as separate disentangling dimensions create new examples that the system has not yet dealt with. In this way, first, it utilizes information from potentially inconsistent sources to learn the right representations that can then be broken down into subspace subcategories for easier and simpler categorization, and second, utilizing the latent representation of the model, it performs high-precision estimates of how similar or dissimilar the inputs are to each other, thus recognizing, with great precision and in a fully automated way, the system anomalies.
Highlights
Heavy industry includes bulky products, complex equipment, and specialized facilities, such as high-tech machine tools and large-scale electromechanical infrastructure, which are involved in the synthesis of chaotic processes
Security and Communication Networks the operational status of the Cyber-Physical Systems (CPS) is the detection of anomalies [5]. e detection of anomalies is the process of finding occurrences or behaviors that do not fit the expected pattern of a given process, whereas an anomaly is an observation that deviates so far from prior observations that it raises suspicions that it was generated by a separate mechanism
Summarizing this work, an innovative autonomous anomaly detection system based on temporal disentangled variational autoencoder (TDVA) is proposed, analyzed, and tested. e proposed algorithm, which was tested and proved to be superior to its competitors, creates flexible disentangling representations, properly separating the distributions of data sets, recognizing with great accuracy and in a fully automated way the anomalies that exist in data sets. e use of Variational autoencoder (VAE) somehow imposes a kind of experience on the structure of the Latent Space, ensuring the smooth transition between different pockets of the data space, discovering inherent differences related to anomalies, while allowing the coding of multiple concepts of similarity or difference with simple and categorical way. is structure is absent in conventional autoencoders, as in general unsupervised learning systems
Summary
Heavy industry includes bulky products, complex equipment, and specialized facilities, such as high-tech machine tools and large-scale electromechanical infrastructure, which are involved in the synthesis of chaotic processes. The view that what is considered normal today may not be normal in the future or any other environment is another important parameter of difficulty in how to detect anomalies Characteristic of this is the fact that most of the industrial systems change over time under the influence of various factors, constantly creating new states of readjustment of their normal operation. The accuracy in the observance of the time constraints, which is a result of special programming of the CPS modules, can maximize the results of the production process In this sense, recognizing the need to use CPS in heavy industry and the vulnerabilities that characterize the chaotic and heterogeneous environment in question, there is a need to create automated and generally autonomous intelligent systems that can adequately model the problem of industrial environment anomaly recognition. One of the most reliable techniques that can be used effectively on largescale data to model anomalies, even if they are new and unknown, is the variational inference [12]
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