With the rise of AI in predictive maintenance, companies are able to get data from assets in an easier way through industrial IoT solutions. We explore the use of artificial intelligence in industry 4.0.
From corrective to preventive, and to predictive maintenance. What are the pros and cons of each strategy? Get an overview of the practical and mental hurdles manufacturers need to overcome when adopting predictive monitoring.
At the heart of this rebranding is a change of the company name to Sensorfy, a fresh visual identity, and a strengthened value proposition to deliver the most accurate predictive monitoring solutions for industrial companies.
Making the transition to predictive maintenance – though worth it - can seem overwhelming at first. In this blog we’ll walk you through the 8 most common challenges that come with implementing predictive maintenance.
When implementing a predictive maintenance strategy there are many things to take into consideration. Assets and sensors are the core of industrial monitoring. Next steps are to collect, process and analyze the measured data.
Predictive maintenance is the industrial future and an important development for manufacturers to consider. Knowing the added value of implementing a condition based monitoring strategy is therefore of vital importance.
When it comes to the maintenance of the assets in your organization, there are several strategies to choose from. Whether you are an OEM or run a smaller workshop: maintenance is on your radar continuously.
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