The massive, beautiful tree canopies in the Western U.S., which may grow perilously close to power lines, can quickly spark destructive wildfires. In fact, 70% of electrical outages are caused by vegetation, and this number has increased by 19% year over year from 2009-2020. The second-largest wildfire in California's history, The Dixie Fire, sparked when power lines came into contact with a fir tree. Could AI-driven solutions help prevent wildfires before they start by analyzing the tree growth that can spark them? Hitachi Energy, the Zurich, Switzerland-based global technology company, says yes. Hitachi Energy, formerly known as Hitachi ABB Power Grids (the name was changed last October) is currently focused on "powering good for a sustainable energy future."
When Jacqui Coleman heard that Australia's largest coal-fired power station was to close seven years earlier than planned, she initially didn't believe it. Coleman is a retail worker in Dora Creek, the closest suburb to the Eraring power station on the shores of Lake Macquarie in New South Wales. For years, she has been selling pies, coffees and sandwiches to some of the hundreds of workers who pass through the News'n' More grocery store on either side of a shift. On Thursday morning, Origin Energy announced it was bringing forward the station's closure to 2025. Many workers at the site first learned their jobs were to be terminated seven years early when they heard it reported on the radio.
Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.
Steady advances in machine vision techniques such as convolutional neural networks powered by graphics processors and emerging technologies like neuromorphic silicon retina "event cameras" are creating a range of new predictive monitoring and maintenance use cases. We've reported on several, including using machine vision systems to help utilities monitor transmission lines and towers linked to wildfires in California. Now, AI software vendor Ignitarium and partner AVerMedia, an image capture and video transmission specialist, have expanded deployment an aircraft-based platform for detecting railway track obstructions. The AI-based visual "defect detection" platform incorporates Ignitarium's AI software implemented on Nvidia's edge AI platform used to automatically control onboard cameras. The system is designed to keep cameras focused on the track center during airborne inspections.
Industries with distributed fixed assets--be they telecommunication broadband or railway networks, wind turbines or drilling facilities, elevators and escalators or washing machines--share specific challenges when it comes to maintenance. As the assets are distributed throughout a region, there is usually no dedicated maintenance team per asset. To the contrary, maintenance workers cover whole areas, travel to the assets' various locations, and bring the appropriate instructions, spare parts, and tools. Maintenance costs typically range between 20–60 percent of opex spend, depending on industry, asset type, and capex spend--an opportunity that has only been a minor priority over the past couple of years. At the same time, ensuring high levels of asset availability and system reliability is a key priority for operations leaders. Often, regulations severely penalize shortfalls (eg, of power transmission and distribution), breakdowns incur high revenue losses (eg, for wind turbines), or breakdowns result in high safety and environmental dangers (eg, in drilling facilities).
Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions. Recently, a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, has been discussed in literature, by some of the authors. Two applications emerged that are very relevant: energy management and business process management. We illustrate a set of guide lines to determine how to apply linear defeasible logic to those contexts.
Rolnick, David, Donti, Priya L., Kaack, Lynn H., Kochanski, Kelly, Lacoste, Alexandre, Sankaran, Kris, Ross, Andrew Slavin, Milojevic-Dupont, Nikola, Jaques, Natasha, Waldman-Brown, Anna, Luccioni, Alexandra, Maharaj, Tegan, Sherwin, Evan D., Mukkavilli, S. Karthik, Kording, Konrad P., Gomes, Carla, Ng, Andrew Y., Hassabis, Demis, Platt, John C., Creutzig, Felix, Chayes, Jennifer, Bengio, Yoshua
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
Add this to the list of feats 3D printers can perform: Fully functioning robots made out of liquid. Heres more proof that drones-as-a-service are gaining ground. Sharper Shape, which sells drone-based automated asset inspection solutions, just announced 3.25 million in new funding led by Straightforward Capital, a European venture capital firm with experience in the energy sector. As I've written, Sharper Shape is using LiDAR, along with analysis tools and long drone flights, to build 3D maps identifying trees, which utility companies can use to measure imminent threats. Fallen trees and swinging branches are a major threat to power lines.