  As a substantial amount of multivariate time series data is being produced by
the complex systems in Smart Manufacturing, improved anomaly detection
frameworks are needed to reduce the operational risks and the monitoring burden
placed on the system operators. However, building such frameworks is
challenging, as a sufficiently large amount of defective training data is often
not available and frameworks are required to capture both the temporal and
contextual dependencies across different time steps while being robust to
noise. In this paper, we propose an unsupervised Attention-based Convolutional
Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding
(ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time
series. The framework starts by pre-processing and enriching the data, before
constructing feature images to characterize the system statuses across
different time steps by capturing the inter-correlations between pairs of time
series. Afterwards, the constructed feature images are fed into an
attention-based ConvLSTM autoencoder, which aims to encode the constructed
feature images and capture the temporal behavior, followed by decoding the
compressed knowledge representation to reconstruct the feature images input.
The reconstruction errors are then computed and subjected to a
statistical-based, dynamic thresholding mechanism to detect and diagnose the
anomalies. Evaluation results conducted on real-life manufacturing data
demonstrate the performance strengths of the proposed approach over
state-of-the-art methods under different experimental settings.
