The dashboard aims to improve operators ability to reduce costs and make long-term planning decisions based on in-depth data drawn from a range of critical sources. These include: condition-monitoring systems, estimated remaining useful life, and the lead-time of replacement parts.
The Wind Reliability Dashboard is an evolution of SKF’s current condition-monitoring and predictive maintenance tools. It extends existing capabilities by allowing data to be captured and analyzed from all forms of rotating systems in each turbine, from both SKF and other CMS providers.
“We have been working closely with the SKF engineers on every step of the project from the definition of functional needs, through the core development of the device, to real-life condition tests, in order to help SKF refine the dashboard model,” said Jérôme Gardyn, CMS Analyst at Boralex.
Typically, it keeps track of components actual and remaining service life, based on previous detection CMS cases. This creates a reliable component library, which allows the move to predictive maintenance via better risk management.
The dashboard also captures information from maintenance systems as well as from the supply chain. For example, it allows the matching of replacement spare parts lead-time with remaining useful life of the component.
Ultimately, it can provide external Systems like CMMS (Computerized Maintenance Management Systems) the key indicators for maintenance best practices, components MTBF (Mean Time Between Failure) & lead time.
“Wind-farm operators are under increasing pressure as growing numbers of turbines fall out of warranty and as market price for renewables gets more and more challenging,” said Jonathan Day, Analytics and Digitalization Development for SKF. “Identifying ways to reduce OPEX and protect margins is therefore crucial. This is the driving factor behind our Wind Reliability Dashboard, which will help each operator improve the efficiency of their business.”
Day added: “The dashboard allows operational and business management teams to communicate far more effectively about critical turbine maintenance and planning issues. It also makes it easier to identify and predict drive train issues, manage spare parts logistics and reduce risk, by enabling data driven decision-making.”
Jan Levander, Project Manager Supply Chain 4.0, commented: “We have in the dashboard also managed to add direct links into SKF supply chain availability information using Supply Chain 4.0 logic to ensure and optimize stock planning and to reduce overall cost.”
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