The survey, conducted by Censuswide on behalf of Fluke, surveyed 600 respondents representing manufacturing firms across the Food & Beverage, Oil & Gas, Life Sciences, and Automotive industries ...

In the second in a series, MathWorks industry manager Philipp Wallner and product manager Eric Wetjen explore the steps needed to leverage the power of artificial intelligence (AI) in effectively ...

A healthy fleet is the key to a thriving logistics business. And you can’t keep a healthy fleet without regular vehicle maintenance. But with a few different types of maintenance at your disposal, ...

AI-driven predictive maintenance is shifting industries from reactive and schedule-based repairs to data-led interventions that cut downtime and costs. Leveraging IoT sensors, machine learning, and ...

Isn’t predicting future maintenance needs what many, if not all, fleet managers desire? We’re not just talking about getting advance warnings via remote diagnostic technology or how ongoing fluid ...

In its most simplistic sense, there are two types of maintenance: reactive and proactive. While reactive requires managing a failure after it occurs, the various proactive maintenance approaches allow ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...

Fine tuning IIoT predictive maintenance models According to Wael Elrifai, senior director of sales engineering and data science at Hitachi Vantara – the company’s IoT arm – one of the complexities of ...

As the world of industrial technology has become more competitive, companies have realized the importance of being able to adapt quickly to unpredictable changes in workflow and production. When ...

LONDON--(BUSINESS WIRE)--Predictive maintenance is undoubtedly one of the most talked-about topics in maintenance and asset management. To gauge where manufacturing companies currently stand with ...

Predictive maintenance aims to minimize unplanned downtime by using artificial intelligence (AI) and machine learning to identify impending problems, so they can be addressed before they take effect.