LSTM-Based Stacked Autoencoders for Early Anomaly Detection in Induction Heating Systems

Mohammed H. Qais, Seema Kewat, Ka Hong Loo, Cheung Ming Lai, Aldous Leung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Due to the contactless operation of cookware on induction heating systems, the temperature of the cookware is measured remotely using thermal sensors placed on the center of the coil. Hence, the measurement error of these sensors increases if the cookware placement is not centered on the top of the coil. Therefore, this study presents a new data-driven anomaly detection method to detect overheated cookware using the thermal sensor of the case temperature of the inverter module. This method utilizes the long short-term memory (LSTM)-based autoencoder (AE) to learn from large training data of temperatures of cookware and the inverter. The learning of the LSTM-AE model is achieved by minimizing the residual error between the input and reconstructed input data. Then, the maximum residual error can be set to be a threshold value between the normal and abnormal operation. Finally, the learned LSTM-AE model is tested using new testing data that include both normal and abnormal cases. The testing results revealed that the LSTM-AE model can detect cookware overheating by using the inverter temperature only. In addition, the LSTM-AE model can detect the faults in the inverter side, such as poor air ventilation and a faulted cooling fan. Furthermore, we utilized different deep learning algorithms, such as the recurrent neural network (RNN) and the fully connected layers, in the internal layers of the AE. The results demonstrated that the LSTM-AE could detect anomalies earlier than the other models.

Original languageEnglish
Article number3319
JournalMathematics
Volume11
Issue number15
DOIs
Publication statusPublished - Aug 2023

Keywords

  • anomaly detection
  • autoencoder
  • deep learning
  • induction heating system
  • LSTM

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)

Fingerprint

Dive into the research topics of 'LSTM-Based Stacked Autoencoders for Early Anomaly Detection in Induction Heating Systems'. Together they form a unique fingerprint.

Cite this