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In this paper, a hybrid model of regularized Long Short-Term Memory (LSTM) and autoencoder for fault detection in automated production systems is proposed. The presented LSTM autoencoder is used as a stochastic process model, which captures the normal behavior of a production system and allows to predict the probability distribution of sensor data. Discrepancies between the observed sensor data and the predicted probability density distribution are detected as potential faults. The approach combines the advantages of LSTMs and autoencoders: The correlations between individual sensor signals are exploited by an autoencoder, while the temporal dependencies are captured by LSTM neurons. A key challenge in training such a process model from historical data is to control the information passed through the latent space of the autoencoder. Different regularization methods are investigated for this purpose. Fault detection with the proposed LSTM autoencoder has been evaluated on the use case of an industrial penicillin production, achieving significantly improved results in comparison to the baseline LSTM.

Fault detection in automated production systems based on a long short-term memory autoencoder