Managing Variability and Reuse of Extra-functional Control Software in CPPS

Vogel‐Heuser, Birgit; Fischer, Juliane; Hess, Dieter; Neumann, Eva-Maria; Wurr, Marcus

doi:10.23919/date51398.2021.9474105

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…To help quality control staff members locate the origins of deviations and take the appropriate preventative or corrective measures, this study aims to suggest the development of a defect detection system that utilizes hybrid convolutional neural networks. This led to the creation of a neural expert system that is capable of intelligent real-time monitoring as well as predictive, corrective, and remedial diagnostic of process control in the manufacture of plaster [ 10 , 11 , 12 , 13 , 14 ]. We will be addressing the following key model components to build the suggested method for problem identification by the expert team and to provide feedback for the current forecasts: being able to recognize a range of industrial systems, both single and concurrent, both natural and artificial simultaneously observing and evaluating any anomalies in the X-bar and R charts.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Raju

Ramaswamy

Eid

et al. 2023

Sensors

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Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method’s success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component’s benefits to enhance the predictive model’s overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy.

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