Our advanced data analytic models implement the most efficient and accurate predictive maintenance solution that provides failure diagnostics and hybrid prognostics for the equipment with industry-proven reliability and accuracy. Our models enable the user to understand the causality relationship between the input indicators and the behaviour of the asset. Our solution, KASEM®, is equipped with a means to capture the knowledge of the experts in the industry to label the events, refine the models and take better maintenance actions.
Associating the engineering behind the equipment with the data to model its behaviour facilitates us in anticipating failures months in advance without the need to explore large amounts of data.
Algorithms to identify the equipment that is going to break, monitor the functional conditions of different susbsystems and, assess the health of the equipment.
Algorithms to perform in-depth analysis to validate the generated alerts, identify the relationship between the indicators and the alerts and, determine the root causes of failures.
Algorithms to estimate the remaining useful life (RUL) of the equipment by assessing the extent to which the equipment can deviate or degrade from its normal operating conditions.
Algorithms to aggregate the learnings from different assets of a fleet to better understand their behaviour and improve the predictive prognostics of the overall installation.