Smart diagnostics based on energy consumption

Smart diagnostics allow the machine tool user or production engineer to compare energy usage profiles for the same component, programme number across a fleet of similar machine tools. Variations in energy consumption can quickly be identified and more importantly fault trees can be used to assist in the identification of factors that could be causing higher than normal energy consumption. Given that energy / power consumption is likely to vary (even within permissible limits) for all components, programme numbers naturally there will be a distribution (see figure 2). Simply to focus on energy / power consumption values greater than the mean would result in a huge demand for subsequent smart diagnostics and follow up remedial actions. A more efficient approach is necessary whereby those components / programme numbers that are consuming a relatively larger proportion of energy / power than others are prioritised for smart diagnostics, root cause identification and resulting actions. This process enables maximum energy / power reduction from envisaged limited operational resources. We recognise that the root cause for increased energy / power consumption could be attributable to a number of factors including but not limited to the normal wear-out of tools, drives, lead screws and inline linkages. Other factors may include off specification coolants, lubricants and machined raw materials. With this in mind, Monition have created a diagnostics authoring environment that will provide the machine tool manufacturer with the ability to create and deploy smart diagnostics based on the actual in service operating conditions. We are confident that this research and development has resulted in tangible intellectual property at the forefront of next generation diagnostics for reduced energy consumption. The real value in this solution is the ability to go beyond energy data collection and performance monitoring by taking positive steps to identify the root cause and remedial actions. The commercialisation of this technique will have a significant net reduction in machine tool energy consumption supporting sustainable “factories of the future” and reduced operating costs.