Electrical Tracking Dataset

Note: This is a preliminary version of the electrical tracking dataset. We are planning to release the first version in April 2023.

Electrical Tracking

Electrical tracking is a cause of fire accidents of home electrical appliances. Adhered dusts or moisture on electrical poles of electric outlets or substrates cause a discharge spark. Repeated electrical sparks gradually carbonize an insulating material between the electrical poles, and then a fire occurs along the carbonized conductor. We are working on an early detection of such tracking phenomena by machine learning techniques.

Dataset

This electrical tracking dataset includes time-series current and voltage data of various home electrical appliances. It consists of normal and abnormal datasets. The normal dataset consists of time-series data of home electrical appliances which are operating normally. The abnormal one consists of those when tracking phenomena are induced as “abnormal behaviors” during the operation. That is, their operational states change from normal to abnormal in the abnormal time-series data. The state changes are labeled manually in the datasets.

Number of home electrical appliances 14 (each contains 3 different models)
Number of time-series data 390 (normal: 195, abnormal: 195)
Length of each time-series data 300.3sec
Sampling frequency 20kHz
Data format CSV

Registration, Terms & Conditions, and Download









    [Researcher Full Name] (the "Researcher") has requested permission to use the electrical tracking dataset (the "Dataset") at Keio University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
    1. Researcher shall use the Dataset only for non-commercial research and educational purposes.
    2. The Dataset production team, Keio University, and Japan Science and Technology Agency (JST) make no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
    3. Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify the Dataset production team, Keio University, and JST, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset, including but not limited to Researcher's use of any copies of derivative works that he or she may create from the Dataset.
    4. Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions.
    5. Keio University reserve the right to terminate Researcher's access to the Dataset at any time.
    6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.


    The electrical tracking dataset is available HERE.


    Acknowledgements: This work was partially supported by JST CREST Grant Number JPMJCR20F2, Japan.