Dr Joyjit Chatterjee's Qualifications (3)

Bachelor of Technology in Electronics and Communication Engineering
BEng

Status	Complete
Part Time	No
Years	2014 - 2018

Postgraduate Diploma in Research Training
PGDip

Status	Complete
Part Time	No
Years	2018 - 2021
Awarding Institution	University of Hull

PhD Computer Science
PhD / DPhil

Status	Complete
Years	2018 - 2021
Project Title	The blessings of explainable AI in operations & maintenance of wind turbines
Project Description	Title: The blessings of explainable AI in operations & maintenance of wind turbines Author: Chatterjee, Joyjit Awarding Body: University of Hull Current Institution: University of Hull Date of Award: 2021 Availability of Full Text: Access from EThOS: Immediate download. Please login to continue. Access from Institution: http://hydra.hull.ac.uk/resources/hull:18495 Abstract: Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support. This thesis revolves around three key strands of XAI - DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change.
Awarding Institution	University of Hull