A real-world iiot dataset for predictive maintenance of metalworking fluids

Abstract

This article presents a multivariate time series dataset detailing the physicochemical degradation of an industrial metalworking fluid (MWF). The data were collected continuously over several months from a test tank under typical operational conditions at an industrial facility in Spain. Four critical variables were monitored using industrial-grade sensors: pH, temperature, concentration, and conductivity. The dataset is provided in five CSV files. The primary file, measures.csv, contains the preprocessed time series at a uniform 5-minute frequency, with authentic missing data gaps intentionally preserved to reflect real-world sensor and connectivity issues. The four additional files serve as a comprehensive benchmark for data imputation algorithms. Each of these benchmark files corresponds to a single variable and includes the original data alongside imputed values generated by five distinct methods: K-Nearest Neighbours (KNN), a hybrid model (HybridKCL), an LSTM-based Variational Autoencoder (LSTM-VAE), and both pre-trained and fine-tuned versions of the MOMENT foundation model. This resource enables researchers and practitioners to develop, validate, and compare predictive maintenance models, anomaly detection systems, and advanced imputation techniques. Furthermore, it serves as a valuable educational tool for addressing common challenges in industrial IoT data, fostering advancements in sustainable and efficient manufacturing.