Machines fitted with sensors have the ability to track many useful metrics, which can give us indications for their reliability and operating efficiency. However, it can prove difficult to make sense of this data in order to draw practical, actionable conclusions. A great deal of noise can exist in data, and there is a strong need to filter the mundane from the interesting. When it comes to data capture and analysis, we need to maximize the benefits while we work within practical network and processing bandwidth limitations.
We aim to make it simple to gather a wide variety of data about a machine, then take practical steps to triage, process, and extract value from the data. This leads to reductions in operating costs, pre-empting of expensive faults, and avoiding equipment downtime wherever possible. The connected enterprise will ultimately develop the ability to identify, correct, and redesign inefficient and error-prone equipment and processes to operate more reliably and cost-effectively.
Gathering data from Industrial machinery is a massive scale problem, compounded by the need to create immediate business value. We must identify machine-specific tolerances across time and procedural dimensions, then form consolidated recommendations for maintenance based on what is observed in the real-world.
Input: Sensors connected to machinery used in industries including manufacturing, power generation, transport, healthcare, agriculture, and construction.
Output: Baseline normal operating ranges, anomaly detection, pre-emptive maintenance recommendations, root cause diagnosis, and component-specific reliability information for future quality improvement.
As individuals increasingly connect various devices in their lives, the challenge for the manufacturer is to serve the individual at scale. By providing a means for OEMs to monitor and maintain their install base at scale, we make it possible to provide value-added services for the user.
Inputs: Consumer devices that collect and push data to the cloud, which can be stored in a range of formats. On top of this, they can include fault reports, usage and reliability data, and on-board diagnostic information.
Outputs: Pre-emptive maintenance recommendations, trends observed in the field, parts/systems/sub-systems reliability metrics, consumer-oriented alerts and recommendations, autonomous repair systems.
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