Unsupervised Classification and Remaining Useful Life Prediction for Turbofan Engines Using Autoencoders and Gaussian Mixture Models: A Comprehensive Framework for Predictive Maintenance

Unsupervised learning has emerged as a pivotal methodology in scenarios where labeled data is scarce, expensive, or impractical to obtain. This article presents a robust framework combining autoencoders and Gaussian Mixture Models (GMMs) for unsupervised classification and Remaining Useful Life (RUL) prediction in mechanical systems, focusing on turbofan engines. The methodology addresses critical gaps in predictive maintenance by overcoming the reliance on labeled data, commonly a bottleneck in industrial applications, and effectively capturing subtle, high-dimensional degradation patterns to enable both robust unsupervised health state classification and accurate RUL estimation. A detailed mathematical foundation, implementation in MATLAB, and empirical validation are provided alongside discussions on hyperparameter tuning, computational complexity, and comparative analysis with traditional methods. The article concludes with practical insights into industrial applications and future research directions.