Računalniško podprto vzdrževanje
Computerized Maintenance Management System (CMMS) is also part of the Green Twin application. Any user, if allowed, can report a fault. In some cases, even the IoT devices can report a fault, e.g., if energy consumption drops, if predictive maintenance sensors are installed, if readings from smart electricity meters show unusual data, if a water meter shows low flow or any flow during non-working hours, or if a machine has some controller and it is connected to Green Twin.
The system automatically or manually creates work orders on the site or remotely, with automatic delivery of work orders to contractors and maintenance crews. It creates a digital history service book and plans and budgets for maintenance. It automates periodic services or service requests based on preventive, predictive or planned requests. It can also close a work order.
Example: Seven lights in an office are using 350 watts of power. Power drops to 250 watts. The application knows that two lights have malfunctioned and sends a work order to an outsourced or employed electrician with information on the building or office and the type of light bulbs that need replacement. After the lights are back in service and the consumption is back to 350 watts, the application automatically closes the work order and marks it “job completed”.
Use Predictive Maintenance Features and Hardware
Have a Digital Service Book of All Assets
Use Preventive Maintenance Features and Hardware
Green Twin Can Use Data for Machine Learning or AI
In most cases, employees will report faults, and Green Twin can act as a help desk. All the reports are sorted and gathered and then sent to (a) the head of maintenance, (b) directly to the company in charge, e.g., an outsourced IT company, or combined. Since every object in Green Twin has its own ID, there is no chance for one fault to be reported more than once. In other applications, this usually causes problems when employees see the fault and they all report it.
Regardless of to whom the fault is sent, it is logged in the system and the head of maintenance, the head of a certain location or both are always notified. In cases where a report is created by the system, it will also be closed by the system. When a lightbulb or toilet tank is replaced, the system will see that normal consumption values are being restored and will close the WO of the maintenance company and notify the person in charge inside your company. This saves a lot of time for both maintenance and management personnel.
Preventive maintenance tasks can be automatic; based on time, machine work hours, or mileage, the system will create WO and send it to the correct maintenance person or company. All reported faults, open WOs, WOs in progress and closed WOs are seen on the dashboard. Of course, different settings can be applied, so the head of maintenance sees all, the employee sees what he or she reported, the electrician sees WOs that are assigned to him and so on.
Some faults can be piled up for later. E.g., if there are stains on walls that need painting, the system or the head of maintenance can put them in a bin. Once the bin is full enough, all faults are addressed with one sweep. Of course, the crew that will perform the painting has all the locations on the map, so they don’t miss any. If walls are properly described within the asset management module, the crew will also know which color to bring, how tall the ladders must be and so on. They can automatically get instructions about how to behave at your company, where to check in, when to come, what the security measures are, who to report to, where to park, etc.
Doing maintenance with Green Twin is effective, accurate, traceable, easier and cheaper for all parties.
Install our predictive maintenance sensors, which will let you know in advance when a certain part of the machine will fail. That way your maintenance team, outsourced or your company’s, can order and get spare parts and organize repair to avoid production stall.
Sensors measure sound and vibration starting at 0.1 MHz and detect any unusual sound or movement of rotating parts, CNC machines, compressors, pumps, electric motors, diesel engines or any other moving part. With machine learning, over time, the system will be able to pinpoint the exact fault and calculate the time before breakdown, cost and repair time.
By connecting clients, machines and devices, the product or application will automatically create work orders based on the machine’s working hours or other parameters to prevent machine failure and exclude human factors.
Apart from buildings, machines also have a digital service book to calculate maintenance costs per product and to plan investments when maintenance costs start to get too high compared to a new asset. Another useful application is in fleet management (cars, trucks, buses), where service must be done based on kilometers driven (engine, tires), running hours or both. Combined with driver data, you will know driver habits, fuel consumption, etc.