Temporal clustering network for self-diagnosing faults from vibration measurements
This addresses the need for automated fault detection in operating machinery to prevent catastrophic failures, though it appears incremental as it builds on existing methods like CNNs and clustering.
The paper tackles the problem of self-diagnosing faults in machinery by processing vibration measurements to detect fault onset, using a Temporal Clustering Network (TCN) that combines 1D-CNNs, unsupervised learning, clustering, and statistical analysis without requiring labeled data.
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.