The blessings of explainable AI in operations & maintenance of wind turbines

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

Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines

As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catastrophic failures in sub-components (gearbox, generator, etc.). Wind turbines have multiple sensors embedded within their sub-components which regularly measure key internal and external parameters (generator bearing temperature, rotor speed, wind speed etc.) in the form of Supervisory Control & Data Acquisition (SCADA) data. While existing studies have focused on applying ML techniques towards anomaly prediction in turbines based on SCADA data, they have not been supported with transparent decisions, owing to the inherent black box nature of ML models. In this project, we aim to explore transparent and intelligent decision support in O&M of turbines, by predicting faults and providing human-intelligible maintenance strategies to avert and fix the underlying causes. We envisage that in contributing to explainable AI for the wind industry, our method would help make turbines more reliable, encouraging more organisations to switch to renewable energy sources for combating climate change

Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, including neural networks, operate as black boxes: despite performing suitably well as predictive models, they are not able to identify causal associations within the data. For data-driven system to approach human-level intelligence in generating effective maintenance strategies, it is integral to discover hidden knowledge in the operational data. In this paper, we apply deep learning to discover causal relationships between multiple features (confounders) in SCADA data for faults in various sub-components from an operational turbine using convolutional neural networks (CNNs) with attention. Our technique overcomes the black box nature of conventional deep learners and identifies hidden confounders in the data through the use of temporal causal graphs. We demonstrate the effects of SCADA features on a wind turbine's operational status, and show that our technique contributes to explainable AI for wind energy applications by providing transparent and interpretable decision support

This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy

We explore the recently released ChatGPT model, one of the most powerful conversational AI models that has ever been developed. This opinion provides a perspective on its strengths and weaknesses and a call to action for the AI community (including academic researchers and industry) to work together on preventing potential misuse of such powerful AI models in our everyday lives

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines

© 2020 IEEE. Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs. Machine learning has been applied to fault prediction in wind turbines, but these predictions have not been supported with suggestions on how to avert and fix faults. We present a data-to-text generation system utilising transformers for generating corrective maintenance strategies for faults using SCADA data capturing the operational status of turbines. We achieve this in two stages: a first stage identifies faults based on SCADA input features and their relevance. A second stage performs content selection for the language generation task and creates maintenance strategies based on phrase-based natural language templates. Experiments show that our dual transformer model achieves an accuracy of up to 96.75% for alarm prediction and up to 75.35% for its choice of maintenance strategies during content-selection. A qualitative analysis shows that our generated maintenance strategies are promising. We make our human- authored maintenance templates publicly available, and include a brief video explaining our approach

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines

The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are promising sources of clean energy, but regularly suffer from failures and down-times, primarily due to the complex environments and unpredictable conditions wherein they are deployed. While various studies have earlier utilised machine learning techniques for fault prediction in turbines, their black-box nature hampers explainabil-ity and trust in decision making. We propose the application of causal reasoning in operations & maintenance of wind turbines using Supervisory Control & Acquisition (SCADA) data, and harness attention-based convolutional neural networks (CNNs) to identify hidden associations between different parameters contributing to failures in the form of temporal causal graphs. By interpreting these non-obvious relationships (many of which may have potentially been disregarded as noise), engineers can plan ahead for unforeseen failures, helping make wind power sources more reliable

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine sub-components, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy

SERO-PREVALENCE OF BOVINE BRUCELLOSIS IN WEST BENGAL, INDIA: A 15 - YEARS STUDY

Bovine brucellosis is an important zoonotic disease caused by Brucella abortus. The distribution of bovine brucellosis in West Bengal has not been reported so far. Here, a longitudinal epidemiological study was conducted from April 2002 to March 2016 to determine the distribution, pattern and trend of bovine brucellosis in different organized and unorganized farms of West Bengal, India. Bovine serum samples were examined for identification of brucellosis by Rose Bengal Plate Test and indirect ELISA. Results envisaged that the prevalence of brucellosis is 11.12 times higher in organized farm (average 6.6%) compared to rural areas (average 0.6%) with overall prevalence was 3.9%. The highest and lowest level of sero-positivity was observed in 2015 and 2011 with 9.8% and 0.5% respectively. In reference to the year 2002, trend of brucellosis was decreasing up to 2013 in a fluctuating manner (odd ratio ranging from 1.7 to 2.3); however, it again increased in 2014 and 2015 with final decrease in 2016. In organized farms, highest (12.6%) and lowest (0.6%) level of prevalence was recorded in 2015 and 2011 respectively. In rural areas, the highest and lowest level of prevalence was observed in 2010 and 2008 with 1.8% and 0.15% respectively. Chi square statistics revealed that location of cattle (χ2 =713.8, df=1, p<0.001) and year of sample collection (χ2 = 468.6, df=14, p<0.001) contributed significantly to increasing or decreasing sero-positivity. A univariate logistic regression analysis also revealed that location of the animals either in organized farm or in rural areas and year(s) of sampling have statistically significant (p<0.001) effect on individual animal level sero-positivity. The results suggested that brucellosis is endemic and moderately distributed in different regions of West Bengal including Burdwan, Nadia, Paschim Medinipur, Murshidabad and Hoogly districts