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As our fascination and fear of trending generative AI systems heat up, researchers focused on the industrial manufacturing realm are bolstering their AI toolkits-all the better to be part of the next wave of innovation. "AI really helps to make those design cycles faster, and the output-fingers crossed-better", said Boris Scharinger, senior innovation manager, Siemens, during a presentation. [Extracted from the article]

The article offers information on empowering of stronger vision for net-zero reality. Topics include information on how managers are constantly battling to improve efficiency and become more sustainable; how effective asset management tools can help decision-makers; and how utilities under pressure from consumers, regulators, and investors.

The article emphasizes the critical importance of annual turbine oil analysis for energy businesses. Turbines are expensive equipment, and regular testing of turbine oil is necessary to prevent degradation and unexpected downtime. The article lists several tests that should be included in an annual turbine analysis, such as acid number, color, demulsibility, foam, Fourier-transform infrared (FTIR), Karl Fischer water, membrane patch colorimetry, particle count, rotating pressure oxidation, ruler, rust A, spectroscopic analysis, and viscosity. The results of the analysis should be carefully examined, and any issues should be addressed promptly to ensure the optimal performance of the turbine. [Extracted from the article]

Sensata Technologies has introduced the wireless 6VW series IoT sensor, which uses AI and machine learning to improve predictive maintenance of rotary assets. The sensor collects data on vibration, temperature, and acoustics to detect potential faults and optimize maintenance workflows. It can also do electrical detection and works with a range of rotating assets. The sensor uploads data to Sensata's cloud-based platform for analysis, and alerts can be sent to mobile phones and via email. This technology can help reduce unplanned downtime and improve overall equipment effectiveness. [Extracted from the article]

The article discusses how sophisticated analytics build sustainability for renewable diesel projects. Topics include public and private sectors around the world are seeking to develop and optimize methods to create diesel fuel without using crude oil-derived feedstocks; and how producers are applying to petrochemical processes as they develop new processes for manufacturing renewable diesel.

The article focuses on Prognostics and Health Management (PHM) capabilities being pushed by application of Machine Learning (ML) and Artificial Intelligence (AI) for health monitoring of components of aircraft. Topics discussed include change in data revolution seen in engine health monitoring, collection of digital data in digital twin that represents physical object characteristics, and AI/ML models built through data exploration.

Picture this: A hurricane strikes a coastal town and destroys the power distribution network. By coupling this data with custom software solutions, you are then able to identify failing assets or even predict the remaining useful life of equipment or solar battery storage. [Extracted from the article]