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

Sekar Kidambi Raju; Seethalakshmi Ramaswamy; Marwa M Eid; Sathiamoorthy Gopalan; Amel Ali Alhussan; Arunkumar Sukumar; Doaa Sami Khafaga

doi:10.3390/s23157011

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

Sensors (Basel). 2023 Aug 7;23(15):7011. doi: 10.3390/s23157011.

Authors

Sekar Kidambi Raju¹, Seethalakshmi Ramaswamy², Marwa M Eid³, Sathiamoorthy Gopalan², Amel Ali Alhussan⁴, Arunkumar Sukumar¹, Doaa Sami Khafaga⁴

Affiliations

¹ School of Computing, SASTRA Deemed University, Thanjavur 613401, India.
² Department of Maths, SASHE, SASTRA Deemed University, Thanjavur 613401, India.
³ Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt.
⁴ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Abstract

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.

Keywords: Industry 4.0; artificial intelligence; fault prioritization; hybrid CNNs; production improvements.

Grants and funding

PNURSP2023R308/Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R308), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.