SKF has acquired Presenso Ltd., a company that develops and deploys artificial intelligence (AI)-based software for improving machine performance. Presenso's AI capability enables production plants to find and act on anomalies that were previously undetectable, automatically and without the need to employ additional data scientists. Presenso's solution is used by industrial plants to increase production output and revenue by reducing the incidence of unplanned asset downtime. Presenso, located in Haifa, Israel, built its solution based on innovations in the field of Automated Machine Learning or AutoML. AutoML accelerates the rate of AI deployment, enabling plants to scale industrial analytics across a large asset base.

Analysts that cover topics related to Industry 4.0 tend to publish bullish and aggressive forecast of the financial impact of digitalization. Perhaps it is hard to cut through the noise in the analyst / thought leadership community with a moderate or nuanced outlook. Either way, as an observer of Maintenance 4.0, reality is not matching the hype. Many industrial plants are struggling with pedestrian challenges of implementation and are not achieving the growth and profitability that shareholders and senior management may expect. Has the Maintenance 4.0 revolution slowed?

Industry 4.0 is characterized by applying cloud and cognitive computing to current automated and computerized industrial systems resulting in the ability to create smart factories that monitor physical processes, identify issues or optimizations, and perform iterative refinement or proactive maintenance and updates. A recent study was released by Emory University and Presenso called The Future of IIoT Predictive Maintenance. The study is focused on predictive maintenance current state, implementation, resulting impact, and future needs identified within smart factories. Over 100 operations and maintenance professionals across Europe, North America, and Asia Pacific participated. The results showed that while there was good satisfaction with existing predictive maintenance environments, the modeling and machine learning aspects are lagging behind where spreadsheet based statistical modeling has not been replaced by more advanced capabilities.

Any fan of recent sci-fi movies and TV has likely seen some genius designer model an amazing new invention virtually before actually building the thing in the physical world. This is not as much a future thing as it may seem. High-tech industries have lately become infatuated with the idea of "digital twins." A digital twin is an exact virtual replica of a physical device. It's a computer model that operates identically to the physical version.

Brakes squeal, hard drives crunch, air conditioners rattle, and their owners know it's time for a service call. But some of the most valuable machinery in the world often operates with nobody around to hear the mechanical breakdowns, from the chillers and pumps that drive big-building climate control systems to the massive turbines at hydroelectric power plants. That's why a number of startups are working to train computers to pick up on changes in the sounds, vibrations, heat emissions, and other signals that machines give off as they're working or failing. The hope is that the computers can catch mechanical failures before they happen, saving on repair costs and reducing downtime. "We're developing an expert mechanic's brain that identifies exactly what is happening to a machine by the way that it sounds," says Amnon Shenfeld, founder and CEO of 3DSignals, a startup based in Kfar Saba, Israel, that is using machine learning to train computers to listen to machinery and diagnose problems at facilities like hydroelectric plants and steel mills.