Life-Cycle Cost Analytics 

• Recognize repair debt.

• Plan the device replacement schedule easily once the condition of devices is known.

• Extend the life cycle of devices.

• Detect device types that become too expensive to maintain.

Device Maintenance 

• Detect devices that are failing before it is too late.

• Find out devices that have the same fault condition.

• Reveal latent faults.

• Improve the accuracy of field work.

Electricity meters tie up a considerable amount of capital and incur ongoing maintenance costs.

The study examined the suitability of artificial intelligence and machine learning for determining the condition of electricity meters.

The device’s health index can be used to make investment decisions. Another application is maintenance troubleshooting automation.

The devices detect faults in many ways and the data is collected in a reading system. The information to be utilized is e.g. energy time series, hourly data, device logs, self-diagnostics, power failure data, and component age data.

In the model developed in the study, data is continuously collected from the last ten days of the device. Data is also collected on device changes. This information acts as a human response because it is a human decision to change the device. The data is designed and converted to conform to machine learning algorithms.