Use Case Details:
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Attributes | Description | Informational |
Type | New | New |
Industry Sector | Industrial IoT | |
Business driver | Predictive Maintenance | |
Business use cases | Many devices give off hints that they will need to have maintenance earlier than their schedule maintenance. Through Machine Learning (ML), we can create models that will allow us to know that a device will soon need maintenance. For many machines, we can gain a great deal of information on the health of the device by looking at the temperature of the device. This requires collecting the data and then sending it to a Historian or similar device. These data points can be sent to the cloud to be modeled. Other requirements
Other variations: Monitoring restricted spaces
| Predictive maintenance: There are many different types of models. For example, many models do not need to be done in real time. Thus, the data can be sent to the Cloud and processed. The data is not time critical, so if there is a delay in sending/receiving data, the data will need to be stored and then sent when the network is available. Yet, there are many scenarios, where real time or near real time is required. An example of this would be a machine reaching a maximum temperature. As it approaches this, we would want to send out a warning and then if it reached this critical temperature, the device needs to be shut down. For this type of scenario, there needs to be a server or space on the IoT gateway that can process the data in real time. |
Business Cost - Initial Build Cost Target Objective | ||
Business Cost – Target Operational Objective | ||
Security need | Because of the remoteness of the devices, need the ability to control ports (turn on/off) | |
Regulations | TBD | |
Other restrictions | ||
Additional details |
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