Improving dam safety using artificial intelligence is the challenge that Hydro-Québec and researchers from Polytechnique Montréal have taken up by developing modern tools for analyzing auscultation data.
Instrumental monitoring of Hydro-Québec's retaining structures is a key element of its dam safety program. To enhance its capabilities, Hydro-Québec and Polytechnique Montréal have pooled their expertise to develop advanced auscultation data analysis tools that surpass anything used in the industry. These tools, based on machine learning algorithms, enable early and automatic detection of anomalies, as well as offering the possibility of modeling the dependency between several time series, thus maximizing the information extracted from a data set. Thanks to their ability to adapt to new data, they minimize false anomaly detection. Finally, the most recent advances open the door to continuous processing of large volumes of data.
After a successful deployment of these tools in its internal dam monitoring systems, Hydro-Québec is now working on a large-scale implementation for real-time analysis of all instrumental data from its dams. In addition to major technical gains in terms of speed and accuracy of anomaly detection, time savings are anticipated by limiting false alarms and focusing efforts on real problems. Once fully deployed, this new tool will act as a 3rd eye on the entire fleet.
As part of this project, the algorithms are developed as open-source software and made available to the entire community.
