  There is a need to build intelligence in operating machinery and use data
analysis on monitored signals in order to quantify the health of the operating
system and self-diagnose any initiations of fault. Built-in control procedures
can automatically take corrective actions in order to avoid catastrophic
failure when a fault is diagnosed. This paper presents a Temporal Clustering
Network (TCN) capability for processing acceleration measurement(s) made on the
operating system (i.e. machinery foundation, machinery casing, etc.), or any
other type of temporal signals, and determine based on the monitored signal
when a fault is at its onset. The new capability uses: one-dimensional
convolutional neural networks (1D-CNN) for processing the measurements;
unsupervised learning (i.e. no labeled signals from the different operating
conditions and no signals at pristine vs. damaged conditions are necessary for
training the 1D-CNN); clustering (i.e. grouping signals in different clusters
reflective of the operating conditions); and statistical analysis for
identifying fault signals that are not members of any of the clusters
associated with the pristine operating conditions. A case study demonstrating
its operation is included in the paper. Finally topics for further research are
identified.
