Enhancing Production System Performance: Failure Detection and Availability Improvement with Deep Learning and Genetic Algorithm

A crucial component of industrial operations is the detection of production system failures, which aims to spot any problems before they get worse. By applying cutting-edge methods like deep learning and genetic algorithms, failure detection accuracy may be improved, allowing for preemptive actions to reduce downtime and maximize system availability. These methods improve reactivity to possible errors and solve dynamic issues, which enhances the overall efficiency and reliability of production systems. This study offers a novel method for improving the availability and failure detection of production systems using deep learning techniques and genetic algorithms in a data-driven strategy. The goal of the project is to provide a complete framework for efficient failure detection that incorporates deep learning models, particularly Convolutional Neural Network (CNN) Autoencoder. Furthermore, system configurations are optimized through the use of genetic algorithms, improving overall availability. The suggested model is able to identify complex patterns and connections in the data by being trained on a variety of datasets that contain information about equipment failure. The incorporation of genetic algorithm guarantees flexibility and resilience in system setups, hence augmenting total availability. The study presents a proactive and flexible approach to the dynamic issues encountered in industrial environments, providing a notable breakthrough in failure detection and availability improvement. The proposed model is implemented in Python software. It achieves an astounding 99.32% accuracy rate, which is 3.58% higher than that of current techniques like CNN-LSTM (Long Short-Term Memory), Bi-LSTM (Bi-directional Long Short-Term Memory), and CNN-RNN (Recurrent Neural Network). The data-driven approach's high accuracy highlights its efficacy in forecasting and avoiding problems, which minimizes downtime and maximizes production efficiency.