Condition monitoring is the application of digital-transformation technology to track machine utilization rate and other parameters that lead to an optimized production cycle. Condition monitoring utilizes digital industrial solutions such as IIoT devices and edge-computing devices to track machine performance, as well as the immediate factory-floor environment.
When key performance indicators or parameters such as overall equipment efficiency levels, overall operations effectiveness, total effective equipment performance, or throughput quality are available, this means condition monitoring is being discussed. Condition monitoring also provides the foundation for applying predictive maintenance and conditional management strategies.
3D Real-Time Model
Data capturing and analytical tools such as edge computing hardware and IoT solutions are constantly deployed within facilities to implement diverse data-driven policies. The digital twin brings all the data captured by these technologies under one virtual environment to mirror the exact and current operational status of an industrial facility.
With a digital twin, an enterprise gets a virtual mirror of its factory operations in real-time. Data-capturing tools feed the digital twin with the real-time status of shop-floor operations. The digital twin can then be leveraged in diverse ways to improve current production performances and drive Industrie 4.0 growth.
The enhanced virtualization of factory-floor data simplifies Industrie 4.0 strategy sessions for both technical and non-technical operators.
Near real-time optimization decisions can be taken as long as accurate data is constantly fed to the digital twin.
The ability to transmit results from the digital twin to smart devices, web-based HMIs, and smartphones also eases access to factory-floor data while supporting remote monitoring initiatives.
Add a dimension to your dashboards
CORVINA is ready to use 3D dashboards. In addition to the classic 2D Synoptic tool, a fully navigable 3D Synoptic is now available.
Digital twins have never been so realistic and accurate and it is now possible to monitor your machine continuously and immediately identify the fault for quick intervention.
MaaS | Machine as a Service
The office equipment, jet engine and medical device industries have all been using the machine as a service model. Rolls Royce, for example, offers clients options that involve paying for a fixed hour of operation of a machine, rather than purchasing the machine.
The adoption of machines as a service model is definitely something both machine manufacturers and factory owners should consider in order to increase their revenue and profits. “Smart” factories of the future look set to adopt the machines as a service model increasingly, since it lowers capital expenditure, the factory can be set up and production can start quickly. Machine manufacturers benefit from this model since they can receive a consistent income stream from their machines and increase their market share.
Machine manufacturers considering the machines as a service model should firstly ensure that their machine is designed to deliver the needed data to both the machine manufacturer and the clients using the machines. OEMs should incorporate the latest IoT technology into their machines so that they can monitor the real-time usage of their machines effectively, and the product outputs. OEMs should spend time thinking about the percentage they are going to claim, from the production outputs/time in use of their machinery. Furthermore, they should also discuss the predictive maintenance and repair options with their clients, and provide packages to accommodate predictive maintenance.
Predictive maintenance involves taking a preemptive approach to discovering faults in, maintaining, and repairing factory equipment before it fails. As with every Industrie 4.0 concept, this approach also relies on data capture and analytics. Thus, the ability to understand the historical and current data a shop floor asset produces is the foundation for automating maintenance tasks at both component and assembly level.
Although predictive maintenance is an Industrie 4.0 concept, it still applies to both legacy and relatively new assets. Predictive maintenance has the ability to reduce downtime by approximately 20% while the option of automating the process can further increase this percentage and reduce unplanned downtime.
Scheduling and Planning
Production planning and scheduling are commonly considered synonyms of the same industrial scheduling activity.
However, it is necessary to point out that there are differences that distinguish these two concepts: the most relevant difference between scheduling and planning is that the former defines who and when operations will be performed, while the latter determines what to do and in what quantity. Scheduling operates on mainly temporal aspects, acting on resources and production systems, while planning operates mainly on aspects related to what to produce.
Although they are different processes, they operate in synchrony with regard to production scheduling. Since one depends on the other, it is important to ensure that the planning component is executed accurately to create an efficient production schedule. Bridging the gap in terms of discrepancies between the two processes will ensure efficient results for the company as a whole. Planning and scheduling are essential for those whose goal is to have a complete production plan and utilise their resources to their full potential.
Achieve greater flowability in the aggregation of data, providing business stakeholders with a complete overview of the production situation, enabling decision-making processes based on reliable and up-to-date data.
Working in a 'what-if' perspective by running simulations of future production scenarios, based not only on stock orders and/or production constraints predetermined, but also on demand forecasts. Simulate delivery scenarios optimised by customer priority, product type, warehouse stock.
Maximizing production performance
Increasing the availability of production systems, optimising tooling times. Optimising resources in the performance of operational activities and main functions defined by the production manager and energy consumption.
Near Real Time
Working on near real-time data. Depending on alarms or unforeseen events, in real time, CORVINA can reschedule activities.