Precisely detecting anomalies in process monitoring is beneficial to enhance the operation of the monitored process by avoiding catastrophic failures and reducing maintenance costs. Unsupervised deep learning techniques are increasingly popular because of their capacity to uncover relevant information from large and complex datasets without using labeled data. In this chapter, we review and evaluate the detection performance of recurrent neural networks (RNNs)-based approaches based on a multivariate time series. RNNs are a powerful tool to appropriately model temporal dependencies in multivariate time series data. We first offer a brief overview of RNNs, from the simplest RNNs with no memory states, to sophisticated architectures with several gates and memory components. Particularly, we focus on those that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a gated recurrent unit (GRU). We then present hybrid deep learning models that integrate the desirable features of RNNs and LSTM, which are capable of approximating complex distributions of deep belief networks and restricted Boltzmann machines. We then apply these models with numerous clustering algorithms for uncovering anomalies. We finally demonstrate these methods on real measurements of effluents from a coastal municipal wastewater treatment plant in Saudi Arabia.
|Original language||English (US)|
|Title of host publication||Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches|
|Number of pages||29|
|State||Published - 2021|