Healthcare Imaging Systems of Royal Philips N.V. are essential for the diagnosis and treatment of patients in hospitals and private clinics. Due to the large costs involved it is not economically feasible to implement backup systems. Therefore system uptime has to be maximized, planned downtime has to be minimized and unplanned shutdown has to be prevented. To cope with the exploding cost of healthcare, the cost of ownership has to be reduced, which also implies that maintenance budgets are under pressure. In response Philips Healthcare has developed maintenance services for hospitals based on remote monitoring of their systems.
The main challenge is to retrieve, store and analyse large amounts of data from globally distributed systems such that predictive maintenance can be offered instead of maintenance at fixed time intervals. Furthermore an alerting system is necessary when the online big data analysis detects a threat of shutdown.
Due to the large purchase cost and the cost of housing unplanned shutdown has a large impact on patients who do not get the care and on the hospital. The Healthcare Imaging Systems of Royal Philips N.V. will use the MANTIS Architecture for equipment asset optimization, thereby aiming to move from a reactive to proactive and predictive maintenance.
Main challenge: getting from large amounts of data to accurate and precise failure detection and prediction.
The objective is to accurately predict upcoming failures by mining large amounts of data from heterogeneous systems distributed globally, such that maintenance can be timely scheduled or in urgent cases the responsible person can be alerted.
Every Healthcare Imaging Systems of Royal Philips N.V. contains many sensors and generates large log files daily. Since these systems are heterogeneous by nature the first challenge to address is to optimize logging such that data mining success can be optimized (anamnesis). The next challenge is to make all data available worldwide in the cloud. Once the data is centrally available it has to be translated to behavioral models and consolidated in a limited set of relevant parameters (translation). This translation requires significant computing power and storage space (infrastructure). Next, the obtained parameters have to be analysed with respect to the maintenance challenges (analytics) and the results have to be visualized for end-users (visualization).