Unsupervised Anomaly Detection for Humidity Sensor Data
(2022) DABN01 20221Department of Statistics
Department of Economics
- Abstract
- Detecting anomalies in loT sensor devices for humidity is crucial for the maintenance and safety of households and buildings. An Auto-Encoder (AE) can detect anomalies in sensor data. However, the reality is that most sensor data is unlabelled, while it is recommended to use labelled data when training the model for anomaly detection with AE. Therefore together with Sensative AB we developed a Long-Short-Term Memory (LSTM)-AE model that is tested with labelled data and later applied to unlabelled humidity series. We found a detection threshold of 99.5% in the experiment with labelled data is optimal when using LSTM-AE on sensor data. Based on the experiment, a 99.5% threshold is applied to unlabelled humidity series, detecting four... (More)
- Detecting anomalies in loT sensor devices for humidity is crucial for the maintenance and safety of households and buildings. An Auto-Encoder (AE) can detect anomalies in sensor data. However, the reality is that most sensor data is unlabelled, while it is recommended to use labelled data when training the model for anomaly detection with AE. Therefore together with Sensative AB we developed a Long-Short-Term Memory (LSTM)-AE model that is tested with labelled data and later applied to unlabelled humidity series. We found a detection threshold of 99.5% in the experiment with labelled data is optimal when using LSTM-AE on sensor data. Based on the experiment, a 99.5% threshold is applied to unlabelled humidity series, detecting four collective anomalies in humidity series 1217 and two collective anomalies in humidity series 2595 with LSTM-AE. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9086768
- author
- Manasboonpermpool, Pinyapat LU and Baars, Dirk Willeboord LU
- supervisor
- organization
- course
- DABN01 20221
- year
- 2022
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- LSTM, Auto-Encoder, Anomaly Detection, Time series, Deep Learning
- language
- English
- id
- 9086768
- date added to LUP
- 2022-10-10 08:42:56
- date last changed
- 2022-10-10 16:03:26
@misc{9086768, abstract = {{Detecting anomalies in loT sensor devices for humidity is crucial for the maintenance and safety of households and buildings. An Auto-Encoder (AE) can detect anomalies in sensor data. However, the reality is that most sensor data is unlabelled, while it is recommended to use labelled data when training the model for anomaly detection with AE. Therefore together with Sensative AB we developed a Long-Short-Term Memory (LSTM)-AE model that is tested with labelled data and later applied to unlabelled humidity series. We found a detection threshold of 99.5% in the experiment with labelled data is optimal when using LSTM-AE on sensor data. Based on the experiment, a 99.5% threshold is applied to unlabelled humidity series, detecting four collective anomalies in humidity series 1217 and two collective anomalies in humidity series 2595 with LSTM-AE.}}, author = {{Manasboonpermpool, Pinyapat and Baars, Dirk Willeboord}}, language = {{eng}}, note = {{Student Paper}}, title = {{Unsupervised Anomaly Detection for Humidity Sensor Data}}, year = {{2022}}, }