Description:
An end-to-end realtime predictive maintenance solution using Azure Machine Learning, IoT Hubs, Stream Analytics, and Power BI.

When deployed, our solution’s machine learning algorithms are able to predict machine failure rates due to long term heat exposure, providing insight into regulating the operational environment. Manufacturers can better plan for system downtime (reducing waste and increasing worker productivity). The solution can be incorporated into existing environments, leveraging current data collection systems, or add new monitoring equipment as needed.

Value
Estimates the useful life of an electrical motor based on various factors.

• Can help reduce machine failure rates
• Can help shift maintenance from reactive to proactive
• Can reduce unnecessary downtime
• Can expand machine lifespan
• Data is shared in realtime between the shop floor and top floor.

Target Industry:
Manufacturing
· Chemical & Agrochemical
· Discrete Manufacturing

Realtime Analytics - Distributed IoT ClientsKiZAN Technologies

Realtime Analytics - Distributed IoT Clients

KiZAN Technologies

Realtime Analytics - Distributed IoT Clients

KiZAN Technologies

Realtime Analytics - Distributed IoT Clients
KiZAN Technologies