For large-scale industrial machine the advancement of Artificial Intelligence (AI) and sensor technology have transformed predictive maintenance approaches. To overcome these problems, this paper offers an AI-Driven Predictive Maintenance Framework that uses multisensory operational data for early defect detection and performance optimization. The raw input dataset fed to data exploration, cleaning and dimensionality reduction. Significant operational features are extracted from multisource sensor inputs via feature engineering and pattern recognition, while data imbalance is addressed with the SMOTE technique. The improved dataset is then sent through a hybrid Conformer-Attention model, which combines convolutional and transformer-style attention processes to capture both local and global temporal relationships. Finally, evaluation metrics are computed to ensure model’s dependability and performance which have the accuracy, precision, recall and F1-score of 99%. This AI-driven strategy increases machinery uptime, decreases maintenance costs and allows for proactive decision-making, all of which contribute to creation of intelligent and sustainable industrial systems.
Vinod Kumar Yarlanki, Vamshi krishna Kona, Manikanta Matam et al.· International Conference on...· 0 citations
For safe and cost effective design of geotechnical and rock engineering schemes, accurate Uniaxial Compressive Strength (UCS) prediction is crucial, especially in carbonate rock formations with considerable heterogeneity. For enhancing the precision and resilience of UCS prediction, this study proposes a hybrid intelligent modelling framework that combines Aquila Optimization (AO) with a Deep Feedforward Neural Network (DFNN). Comprehensive data collected from laboratory-tested carbonate rock samples are preprocessed, which includes handling missing values and data cleaning. To comprehend the behavior and correlations of input variables, exploratory data visualization and feature distribution analysis are carried out. The regression model is a deep feedforward neural network and important hyperparameters like number of hidden layers, neurons, learning rate and activation functions are optimally tuned using Aquila Optimisation Algorithm (AOA). Multiple regression metrics are used to quantitatively assess model performance from python software. The findings show that proposed framework attains minimum RMSE and MSE value of 0.0425,0.0018 is useful for rock mechanics and mining applications and gives a dependable, data-driven tool for UCS prediction in carbonate rocks.
J. M. Durga, Vinod Kumar Yarlanki, Dasari Appaji et al.· International Conference on...· 0 citations