A Novel Diagnostic Method on the Industrial Boilers Using Machine Learning: t-SNE, Auto-Encoder and LGBM

This study presents a machine learning (ML)-based diagnostic framework for industrial boilers, focusing on the reliable classification of normal and abnormal operating states. Experimental data were obtained from a 5-ton field demonstration boiler, including 28,943 samples collected under steady-state and load-swing conditions as well as three abnormal scenarios: fan inverter malfunction, air damper malfunction, and fuel gas damper malfunction. Data preprocessing was performed to normalize and structure multivariate signals, including pressure, temperature, water level, and oxygen concentration for ML implementation. Exploratory data analysis (EDA) and principal component analysis (PCA) were employed to examine distributional characteristics, while t-distributed stochastic neighbor embedding (t-SNE) and autoencoders were applied to enhance visualization and feature extraction. The Light gradient boosting machine (LGBM) served as the primary classification algorithm. Results demonstrate that PCA and t-SNE provided partial separation between normal and abnormal data, but overlapping distributions limited clear boundary identification. Autoencoders improved clustering, showing more distinct separability between operating states. Using LGBM, the model achieved the lowest classification adequacy of 99.64%. This work demonstrates the feasibility of ML-based diagnostics for industrial boilers and underscores the potential of ensemble models such as LGBM for high-accuracy abnormality detection. Future research will expand on this foundation, ultimately supporting the development of commercial-level diagnostic systems.