Predictive maintenance

In modern medicine, diagnostics is often done by sophisticated and expensive devices. Keeping them operational is crucial for giving patients access to the best medical care available. Any downtime hampers both the quality of treatment process and the overall profitability of the healthcare provider.

The challenge
An international manufacturer of medical devices sought to reduce the downtime in its devices with predictive maintenance tools.

The solution
deepsense.ai’s researchers built a model that pores over historical data, searching for anomalies and signs of a breakdown before one occurs.

A key challenge in building the algorithm is to precisely model the physics of processes occurring inside the device. Our solution is monitoring the device with IoT to harvest the data on the machine’s condition. The solution then applies the physics and historical data-based model to incoming data to predict how long the device will continue to run properly.

The solution can be applied in machine-reliant industries, where breakdowns bring operations to a halt and hamper overall company performance.

The effect
The predictive maintenance solution built on deepsense.ai’s model reduced breakdown-related downtime by more than 15%.