Extract, Transform, and Load Maintenance Data
This process is used to extract historical, current and setup data from the assets, work orders, and maintenance program tables.
The process then performs the data preprocessing and feature engineering. Finally, it loads the file to Oracle storage cloud service where it can be considered by the Perform Learning on Maintenance Data process.
You can access this report from the Scheduled Processes page.
When to Use
It is recommended to only use this process if you want to consider recommendations for maintenance program adjustments based on machine learning.
The historical maintenance data must be routinely extracted, transformed, and loaded into the IoT Analytics platform. Only once a representative sample has populated the data lake, would running the process to perform learning produce any meaningful recommendations.
It is recommended to run a full load the first time the process is run. Then, it should be run when enough historical data is ready for an incremental load into the IoT Analytics platform.
Privileges Required
- Privilege: MNT_EXTRACT_TRANSFORM_LOAD_MNT_DATA_PRIV
Specifications
Use these specifications when you run the scheduled process.
| Specification | Description |
|---|---|
| Job Type | On an adhoc basis, you can run at any time. |
| Frequency | Should only be run after sufficient data has accumulate Maintenance. Then incremental loads should be scheduled based on the volume of maintenance work orders. |
| Time of Day | Any time. |
| Duration | It could take a while depending on the volume of data being analyzed. |
| Compatibility | There should be only one instance of the job running at any one time. |
Parameters
You must define at least a range of Work Order numbers or dates to process the report.
| Parameter | Optional or Required | Description | Parameter Value | Special Combinations Required |
|---|---|---|---|---|
| Extraction Type | Required | You must select if a full or incremental load is required for the process. | The options are presented in an LOV. | None |
| Reliability Rate | Optional | Parameter that is considered by the learning algorithms in making maintenance program adjustments. | Positive numerical value. | None |
| Utilization Rate | Optional | Parameter that is considered by the learning algorithms in making maintenance program adjustments. | Positive numerical value. | None |
Troubleshooting Information
- Once submitted, you can view the status of the process on the Scheduled Processes UI. A successfully completed process, as well as any child processes, will end in Succeeded status.
- If issues are encountered, the process or any of its subprocesses may be Warning or Error. The specific validation errors and warning messages that prevented the process from completing successfully are displayed in the error log(s).
- Any interactive warning validations are NOT performed.
- When the process is submitted, you can Resubmit, Put on Hold, Cancel Process, Release Process as provided by the Scheduled Processes UI.