Improved Performance Analysis of Mechanical Failure Detection in Industrial Machines Based on a Hybrid Deep Learning Model - Tanuja Alekhya, Konduru (Digital Repository of Theses, 2025)
In today’s fast-paced industrial landscape, ensuring uninterrupted machine performance has become a critical business priority. Mechanical failures—especially in key components like bearings—can cause significant operational downtime, reduce production efficiency, and inflate maintenance costs. Traditional maintenance strategies such as corrective and preventive maintenance are increasingly inadequate in meeting the demands of modern Industry 4.0 frameworks. These approaches either react post-failure or operate on fixed schedules that overlook the real-time condition of equipment. Consequently, there is an urgent need for intelligent, scalable, and cost-effective solutions capable of predicting failures before they occur. This research presents an advanced predictive maintenance framework powered by a hybrid deep learning model that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Feedforward Neural Networks (FNN). The model was developed and validated using a real-world industrial dataset comprising time-stamped sensor readings that reflect both operational and environmental conditions. Key features such as air quality, temperature, rotational speed, footfall, and voltage were analyzed to train the model. The hybrid architecture enables the system to capture both spatial patterns and temporal sequences, delivering superior accuracy and robustness compared to traditional machine learning methods. Empirical evaluation demonstrated that the hybrid model outperforms classical classifiers including Perceptron, Naive Bayes, K-Nearest Neighbors, and Support Vector Machines. It achieved an accuracy of over 90%, along with high precision, recall, and F1-scores, ensuring minimal false positives and negatives. The model’s design also supports scalability and real-time deployment in industrial environments, offering a cost-effective solution for reducing unplanned downtimes and enhancing asset utilization. From a business administration perspective, the study delivers strategic value by aligning predictive maintenance with digital transformation objectives. It enables data-driven decision-making in areas like maintenance planning, budgeting, risk mitigation, and supply chain reliability. Furthermore, it contributes to the growing academic discourse by integrating deep learning methodologies into practical maintenance frameworks and validating them using real-world industrial data. The study concludes that hybrid AI models represent the next frontier in industrial automation and reliability engineering. Their adoption can empower organizations to transition from reactive to proactive maintenance regimes, extend the lifecycle of critical assets, and drive operational excellence in a highly competitive and sustainability-conscious global economy. Keywords: Fault Detection, Deep Learning, CNN, LSTM, Feedforward Neural Network (FNN), Maintenance, Industrial Machinery.