Abstract
The ability to predict the maintenance needs of machines is generating increasing interest in a wide range of industries as it contributes to diminishing machine downtime and costs while increasing efficiency when compared to traditional maintenance approaches. Predictive maintenance (PdM) methods, based on state-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques, are heavily dependent on data to create analytical models capable of identifying certain patterns which can represent a malfunction or deterioration in the monitored machines. Therefore, a realistic and representative dataset is paramount for creating, training, and validating PdM techniques. This paper introduces a new dataset, which integrates real-world data from home appliances, such as refrigerators and washing machines, suitable for the development and testing of PdM algorithms. The data was collected on various home appliances at a repair center and included readings of electrical current and vibration at low (1 Hz) and high (2048 Hz) sampling frequencies. The dataset samples are filtered and tagged with both normal and malfunction types. An extracted features dataset, corresponding to the collected working cycles is also made available. This dataset could benefit research and development of AI systems for home appliances' predictive maintenance tasks and outlier detection analysis. The dataset can also be repurposed for smart-grid or smart-home applications, predicting the consumption patterns of such home appliances.