Detection and analysis of downtimes
With the WinCC/DowntimeMonitor, the machine data management software, downtimes in machine-oriented or line-oriented production facilities can be detected and analyzed centrally. For individual units, machines or entire production lines, the following specific parameters can be derived from this.
OEE (Overall Equipment Efficiency),
MTBF (Mean Time Between Failures)
MTFF (Mean Repair Time)
and other so-called Key Performance Indicators (KPI).
In doing so, production equipment can be defined individually by plant
Complete transparency about the plant and machinery as the basis of optimizing plant productivity, this means:
- Avoiding disturbances and bottlenecks
- Increasing availability
Deriving specific parameters (KPI)
Integrating appropriate display instruments (controls) in WinCC process pictures
Can be used for individual machines up to complete production facilities
Distributing evaluations to different people across the Web
Design and functions
Error cause analyses provide information about the frequency and duration of machine or plant downtimes. Corresponding indicators can easily be integrated into the WinCC process screen.
In the DowntimeMonitor, the time model of the production equipment is determined from the production times, maintenance times and downtimes. Via a shift calendar, the shifts can also be included in the analysis. All plant statuses relevant for the analysis are parameterized in a detailed reason tree. The acquired data provides information about the efficiency of individual machines and entire production plants. The transparency of the data makes it possible to quickly respond to malfunctions and to take corrective measures, which again increases the machine availability.
All analysis results are integrated into the WinCC screens in the form of controls. Here, several indicators are distinguished between
Gantt- and Pareto-charts
Bar or column charts
Trends or tables
The displayed data can be processed with WinCC and the WinCC options, and be distributed to different persons via Web.