Predictive Maintenance in IoT Using NLP Techniques

In this thesis we study how NLP techniques can be integrated with Internet of Things (IoT) sensor data, to be used for predictive maintenance in industrial environments. The main objective was to build models that can predict whether an equipment will fail in the next 7 days using the iot_predictive_maintenance dataset. Sensor readings include temperature, vibration, humidity, and pressure etc. along with unstructured textual maintenance logs. Two sets of the feature were built: one containing only numeric sensor data and the other with features from TextBlob, namely TF- IDF vectors and polarity scores on sentiment, to create a feature set. Logistic Regression, Random Forest, Gradient Boosting, and XGBoost models were evaluated multiple times. It was found that models incorporating both sensor and NLP features significantly outperformed those based solely on sensor data. Among the evaluated models, the best performance was achieved by the XGBoost model using the combined feature set, which attained an accuracy of 0.973, an F1 score of 0.889, and a ROC AUC of 0.993. These results confirm that the textual in-formation in maintenance logs contains important failure aspects not expressed from numerical data alone. This work demonstrates practicality of fusion models in predictive maintenance, enabling a scalable and robust solution for smart manufacturing.