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Dataset for identifying maintenance needs of home appliances using artificial intelligence
Tiago Fonseca1, Pedro Chaves1, Luis Lino Ferreira1
1School of Engineering of the Polytechnical Institute of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto Porto, Portugal.
Data in Brief
|April 3, 2023
Summary
A new dataset of home appliance data supports predictive maintenance (PdM) using AI. This resource aids in developing algorithms to detect malfunctions and predict energy consumption.
Area of Science:
- Engineering
- Computer Science
- Data Science
Background:
- Predictive maintenance (PdM) reduces machine downtime and costs compared to traditional methods.
- AI and IoT systems are crucial for PdM, requiring realistic datasets for model development.
- Existing datasets may not adequately represent real-world home appliance failures.
Purpose of the Study:
- Introduce a novel dataset for developing and validating PdM algorithms for home appliances.
- Provide a resource for AI-driven predictive maintenance and outlier detection in appliances.
- Enable research into smart-grid and smart-home energy consumption prediction.
Main Methods:
- Collected real-world electrical current and vibration data from refrigerators and washing machines.
- Acquired data at both low (1 Hz) and high (2048 Hz) sampling frequencies.
- Filtered and tagged data samples with normal and malfunction types, including extracted features.
Main Results:
- A comprehensive dataset of home appliance operational data, including failure modes, is now available.
- The dataset facilitates the training and testing of AI models for PdM.
- Extracted features corresponding to working cycles are provided for advanced analysis.
Conclusions:
- The new dataset is valuable for advancing AI in home appliance PdM and outlier detection.
- This resource can be repurposed for smart-grid and smart-home energy usage prediction.
- Availability of this dataset will accelerate research and development in related fields.
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