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

Mohammed H. Qais (), Seema Kewat, Ka Hong Loo, Cheung-Ming Lai and Aldous Leung
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Mohammed H. Qais: Center for Advances in Reliability and Safety, Hong Kong, China
Seema Kewat: Department of Engineering and Physics, Karlstad University, SE-651 88 Karlstad, Sweden
Ka Hong Loo: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Cheung-Ming Lai: Center for Advances in Reliability and Safety, Hong Kong, China
Aldous Leung: German Pool (Hong Kong) Limited Engineering, Hong Kong, China

Mathematics, 2023, vol. 11, issue 15, 1-19

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.

Keywords: anomaly detection; autoencoder; deep learning; induction heating system; LSTM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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