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In the energy/utility context, conditional monitoring is one of the most important processes in the daily operation & maintenance of the equipment. With more and more IoT sensors being deployed on the equipment, there is an increasing demand for machine learning-based anomaly detection for conditional monitoring. In this talk, I will discuss a method we designed for anomaly detection based on a collection of autoencoders learned from time-related information. This talk will cover the whole end-to-end flow on how this method is designed, and some energy specific use cases will be used to demonstrate its performance.


Yiqun Hu is currently the Director, Data & AI at SP Digital and is responsible for driving the initiatives of data & AI for the whole SP Group. His team has built and manages the group's big data infrastructure and deployed production-ready AI solutions to transform the utility industry.

Before joining SP Group, Yiqun had experiences in leading data/AI teams in several industries, applying data science and machine learning to bring real impact to several organizations including a global payment company (PayPal), an e-commerce company (eBay) as well as a leading financial institute in Asia (DBS).

Besides his experience in the industry, Yiqun also spent close to a decade in the academic R&D space as an AI researcher. He has published over 40 scientific papers in flagship international AI conferences/journals, i.e. TPAMI/TIP/TM, CVPR/ICCV/ECCV/ACMMM etc, as well as one book chapter. His publications have been cited over 1,700 times in other scientific publications.

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