Machinery Maintenance AI Agent

The Machinery Maintenance AI Agent applies AI to machinery maintenance in construction and other plant-intensive industries, as part of DANAOS ProjectVIEW AI. It aims to flag abnormal fuel use, idle time and maintenance risk across the fleet, using machinery, fuel and usage data in ProjectVIEW ERP. Then it recommends maintenance or reallocation. The plant and machinery manager approves every work order.

The agent uses the data in the Machinery Management module of ProjectVIEW ERP, where each machine’s cost goes to WBS elements and Cost Codes.

• Fleet register, hours and allocation to projects
• Fuel issues and consumption
• Maintenance history, schedules and spare parts
• Machine cost booked to activities
• Telematics or IoT data, where connected

The agent follows the ProjectVIEW AI agent cycle. Applied to machinery:

1. Detect: find machines with fuel use, idle time or hours outside their normal pattern.
2. Investigate: compare with similar machines, the activity they work on and their maintenance history.
3. Explain: state the likely cause, for example a machine idling on site or overdue servicing.
4. Recommend: propose a service, an inspection, a reallocation or a return to yard.
5. Request approval: send the recommendation to the plant and machinery manager.
6. Execute: after approval, create the work order or reallocation in ProjectVIEW ERP.
7. Audit: record the finding, the decision and the outcome.

Because machine data links to activities, the agent can also report fuel use and CO₂ per activity for ESG reporting.

Main inputs, outputs and decision points:

The Machinery Maintenance AI Agent works inside ProjectVIEW ERP governance, following the chain Identity → Role → Permission → Data → AI → Action. It sees only the data the user’s role allows and proposes only actions that role could take. AI should operate inside enterprise governance, not around it.

• Consequential actions always need human approval.
• Also, the system records every recommendation, approval and change for audit.
• Data stays in the client’s private environment and never goes to public consumer AI services.

Illustrative example. A contractor runs a fleet of excavators and trucks across three sites, including Project F.

The agent notices that one excavator on Project F shows fuel use well above similar machines on comparable work, while another machine on the same site has logged many idle hours.

It explains both findings, recommends an inspection of the first machine and moving the idle one to a site that has requested capacity. The plant manager approves the inspection work order, the project managers agree the transfer. Finally, the system logs both actions.

The agent serves the people who own plant and equipment:

• Plant and machinery managers responsible for availability, maintenance and fleet cost
• Project managers and ESG teams who need machine cost and emissions per activity

It applies to construction and mining and quarrying, where equipment is a major part of operating cost.