Enlyze sees industrial power consumption as a signal for early detection of impending plant failures and fluctuating product quality. A combination of own (measurement) hardware and the latest algorithms in the field of AI is used.
Various AI algorithms are used for data analysis, which ultimately issue interpretable recommendations for the workers. The data base provides machine parameters, read out from the control system, as well as high-resolution current signals, recorded by proprietary sensors.
With enLYZE’s solution, quality assurance can be integrated into the manufacturing process from a final statistical control (after completion of production) to a live quality assurance. According to the current state of the art, ongoing production processes are not monitored or optimized for their currently manufactured quality. As a rule, only the final product is checked for its quality, usually with a considerable time lag for the actual production. Inferences and improvements to the production process are then difficult to achieve. With the ENLYZE solution, quality problems and their causes are directly identified, so that countermeasures can be counteracted at an early stage and scrap is prevented. This shifts quality control from final control to the process and can be optimized during operation