Predictive Maintenance

Automation

A simple experiment on Predictive Maintenance and IA2IA

It is not easy to find your way in the sea of technological solutions related to predictive maintenance. As Product Marketing Manager, it is my job to understand and test the real potential of the tools I am dealing with, in this case Yokogawa’s wireless sensors, called “Sushi Sensors”, which monitor parameters such as vibration, pressure and temperature. Read the blog article with the title “A simple experiment on Predictive Maintenance and IA2IA” here.

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OpreX Roboter
Automation, Optimisation

Smart Maintenance – Artificial Intelligence (AI) technologies neatly streamline all areas of the value chain in companies

The ideal solution is achieved through the tactical combination of Smart Manufacturing and Smart Maintenance. Smart, innovative technologies such as smart sensors, edge computing, big data and artificial intelligence (AI) ensure that all assets in production operate at 100% efficiency.  For example, assets are now equipped with industrial smart sensors such as the Sushi Sensor for data acquisition. These IIoT sensors transmit the data to computer-aided maintenance management systems. These systems use advanced analysis options such as AI or cloud-based machine learning to determine the complete data pool in order to ultimately deliver results in real time that can be called up at any time from a smart device. In this way, anomalies can be quickly detected and immediately communicated to the appropriate employees. The tasks of Smart Maintenance include. monitoring and knowing the condition of the assets. In addition, there is the dedicated planning of maintenance and the improvement of the performance and effectiveness of the resources. 

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Optimisation

Predictive maintenance – between hype and reality

Predictive Maintenance is one of the most frequently mentioned topics of digitization in the industrial environment. On the one hand, it is felt to be one of the saviours that brings enormous cost savings, on the other hand, the industry 4.0 index shows that predictive maintenance falls far short of expectations. Here, promises and reality seem to diverge widely. What is the reason for this?

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