Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis
This is an incremental improvement for industrial process monitoring, enabling better analysis of sensor data to identify system states.
The paper tackled the problem of analyzing large-scale industrial sensor signals by proposing a recurrent auto-encoder model that summarizes sequential data into fixed-length vectors for partial reconstruction, using a rolling window approach to handle unbounded time series, and applied visualization and clustering to reflect operating states.
Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the decoder output so that it performs partial reconstruction. The fixed-length vector therefore represents features in the selected dimensions only. In addition, we propose using rolling fixed window approach to generate training samples from unbounded time series data. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed using additional visualisation and unsupervised clustering techniques. The proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose, where clusters of the vector representations can reflect the operating states of the industrial system.