DISRUPTIVE TECHNOLOGIES
ARTIFICIAL INTELLIGENCE
TRANSFORMS ENGINEERING by William Yong, Managing Director, Southeast Asia, Water and Narsingh Chaudhary, Execu ve Vice President, Asia Power Business, Black & Veatch Harnessing the power of compu ng, Big Data and advanced algorithms, Ar ficial Intelligence presents new possibili es for today’s engineers. Governments in Asia perceive Artificial Intelligence (AI) as a core pillar of their smart city strategies and growing investments in the concept indicate its vast potential. Singapore launched its National Artificial Intelligence Strategy and committed over SGD 500 million to fund activities related to AI under the Research, Innovation and Enterprise 2020 Plan. The Philippines is working with the Asian Institute of Management (AIM) Aboitiz School of Innovation, Technology and Entrepreneurship (ASITE) to prepare a roadmap which will position the country as an AI leader in Southeast Asia. Malaysia has set up a USD 1 billion AI park to boost research and development in the country. AI is not a new concept. What is new, however, is its value proposi on. Rapid developments in compu ng power, Big Data, Machine Learning (ML) and advanced algorithms have boosted the poten al of AI. AI and ML technologies offer the ability to process and u lise vast amounts of data. That ability allows power and water facili es, for example, to move beyond the descrip ve analy cs in use currently. Descrip ve analy cs uses data to understand past incidents and trends. Over the years, data mined from different power and water equipment has largely been unstructured. Today, power and water leaders can leverage AI technology by layering ML capabilities over the data collected, manipulate it, find patterns, and transform the data into insights that can anticipate required action. Automating business processes, gaining insight through data analysis, and engaging with customers and employees are some business priorities that can be addressed by AI. AI can enable and enhance predictive analytics which establishes what is likely to happen, and move towards prescriptive analytics which suggests actions based on the predictions. One example of predictive analytics is predictive maintenance. AI can predict the state of the equipment in advance so that maintenance can be scheduled. This addresses sustainability and reliability issues of facil-
Mr William Yong
Mr Narsingh Chaudhary
ities like water networks and power grids, while mitigating costly outages and other equipment failures. AI can also be trained to support prescrip ve analy cs. Prescrip ve analy cs enables autonomous management, where machines act on the informa on the AI has extracted. Autonomous management con nuously refines and amends the way it responds to the need to act.
First steps Industry response to AI has been positive. Pressured by limited capital, rising customer expectations, and growing commitments to sustainability and reducing resource consumption, asset-heavy facilities, like utilities, are adopting data analytics to improve different stages of the infrastructure lifecycle. Simplifying Asset Performance Management (APM) is one priority Asian utilities are considering. They are teaming up with software partners, like Black & Veatch’s subsidiary, Atonix Digital, to implement APM software that improves processes ranging from health-monitoring of critical assets, preventing failures, and improving the operational efficiency of facilities such as power generation, industrial, and water and wastewater treatment plants. By focusing on core areas including risk assessment, investment planning, data management, performance analysis, and monitoring and diagnostics, software such as Black & Veatch’s ASSET360 platform can promote innovation across the entire infrastructure lifecycle, through its modular, extensible architecture, and its seamless integration of functions and tasks across all specialised modules. THE SINGAPORE ENGINEER February 2020
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