The client is based out of Singapore and needed a solution that can solve the problem of manual maintenance and inspection data of rigs and pipes of Oil and gas industries through automation. As manual inspection processes across various rigs and pipes needed to be tracked effectively, and they needed an efficient digital solution.
A condition-based inspection process was in place, where inspection data is collected via a tablet inspection application, on offshore platforms, remote yards and workshops across the world and made available to asset owners through a web-based platform. The program, therefore, requires a back-end application so that legacy data, manual data entry and editing can be achieved.
The project is to develop a solution for predictive analysis of the inspected data which is collected by the different set of teams and project engineers for the maintenance of rigs in the oil and gas industry. The stack is developed based on the different concept of AI and machine learning technologies with the following features:
- Development of a web application for the Super Admin to manage rigs/ clients/ service providers(Admin) and Sub-assembly for rigs.
- The team can record the data after inspection for the predictive analysis.
- The data after the inspection is stored in the database for analysis using a web application interface.
- Upload the legacy data into the system.
- Current inspection data can be added through the mobile inspection application and verified by the engineer when required.
- Data can be viewed by the client via the web-based application.
- Reports can be generated by the client through the front-end web-based application.
