Automation, IoT, Technology & Digital Media

Petasense Vibration Mote IoT Sensors for Predictive Maintenance

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Maintenance in production world is moving towards predictive from the preventive mode. Why wait till an incident occurs and demands a cure? Petasense Vibration Mote IoT Sensors target to achieve it in a simple manner. If you intent to develop a new connected vibration sensor which monitors industrial machinery and sends data in a timely manner to the cloud so that plan managers are able to reduce maintenance costs and downtime then you must learn about Petasense. This is a Silicon Valley startup aiming to bring the Internet of Things (IoT) to the industrial machinery spectrum. It aims to connect industrial machines to the IoT so as to create and analyze data that helps in intelligent predictions. This will help any industry to substantially improve plant productivity.

The first product in that regard is the Petasense Vibration Mote which runs through a battery. In fact, it is a wireless vibration sensor that smartly monitors the health of rotation machines like motors, compressors, and pumps. The Mote assimilates vibration data and delivers it wirelessly to the Petasense Cloud. Subsequently their machine-learning software, on the basis of this data, predicts failure on the basis of vibration analysis. Unplanned downtime because of equipment failure is one of the biggest risks for manufacturing plants. Predictive maintenance can help in reducing downtime significantly. But the issue is its complexity and cost to implement. In normal circumstances, data collection for vibration analysis is a manual activity.

Petasense is a revolution in Predictive Maintenance

This manual process to collect data is not only an expensive proposition but it also is insignificant in order to conduct a meaningful analysis. That is why its deployment is not feasible to cover all industrial equipment for predictive maintenance. That is where the Petasense solution comes into the picture. It not only simplifies the process of capturing useful data but also reduces the cost by 80%. For instance, in the US alone, there are more than 250,000 manufacturing plants. The solution can increase productivity substantially and also extend the life of machinery thus reducing the capital investment of manufacturing enterprises.


Petasense Motes

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