Predictive Maintenance and Asset Management

**Overview**
These examples showcase the power of Dataknobs AI-TWIN solution built using machine learning in predicting failures, assessing equipment health, and estimating the Remaining Useful Life (RUL) of various components. The solution's versatility is demonstrated through its application to diverse industrial devices, including engines, heaters, transformers, and electrical fault detection systems.

The solution further showcase predictive maintenance on CNC machine.

**Use cases**
Add use cases for the listing here.
- Optimizing maintenance schedules:
- Prioritize maintenance resources
- Improve budget planning - when to replace old device and plan to buy new
- Maximize asset utilization

**Product details**

- 1. Dataset about engine degrade. There are 26 columns related to cycle, operational settings and Sensor Values
2. Electrical Fault Detection about Transmission line
- 'G', 'C', 'B', 'A', 'Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc', ’fault_type’
’fault_type_code’
3. Heater health analysis
- 'Voltage_measured', 'Current_measured', 'Temperature_measured', 'Current_charge', 'Voltage_charge', 'Time', ‘Heater_Health’
4. Transformer fault prediction
- 'OTI', 'WTI', 'ATI', 'OLI', 'OTI_A', 'OTI_T', 'VL1', 'VL2', 'VL3', 'IL1', 'IL2', 'IL3', 'VL12', 'VL23',
'VL31', 'INUT','MOG_A'

For more details, refer to the embedded notebook.

**CNC Machines**
CNC predictive maintenance solution is based on machine learning models built on a precisely produced dataset that accurately represents real-world machining circumstances.
This dataset, created by the School of Engineering - Technology and Life, correctly measures critical variables such as air temperature, process temperature, rotational speed, torque, and tool wear. Our system effectively predicts CNC machine failures using Random Forest, Decision Tree, and Stochastic Gradient Boosting algori

**Additional Insights**
For more information see
https://www.dataknobs.com/products/ai-twin/

you can see output at
https://ai-twin.dataknobs.com/

Slides - https://www.dataknobs.com/solutions/asset-management/

The previous examples demonstrate various scenarios where sensor data is used to monitor the degradation of devices. This degradation information is then employed to determine the health status and estimate the remaining useful life of the devices.