Physical sensors allow you to collect historical and current information on systems, sub-systems, components and even people. The aggregate of this information provides state information on processes.
This can be in industry, health, hospitality, utilities, education or transportation. Whatever the domain, the goal is to provide actionable alerts that enable intelligent decision-making for improved performance, safety, reliability or maintainability.
Alerts are either diagnostic or prognostic in nature in that they tell you the current status or an anticipated impending situation. They allow you to:
- Prevent something happening that might be costly or dangerous. For example, in manufacturing, significant damage to manufacturing
equipment, the products being fabricated or costly downtime. In healthcare, someone is about to fall.
- Reduce the need for costly preventative manual checking or over-zealous regular replacement. For example, in manufacturing, reducing the time and costs for maintenance of products or processes. In healthcare, reducing the need for wasted human effort monitoring patients who are ok the majority of the time.
The overall aim is to save human effort while also avoiding failure and significant disruptions. Achieving this using traditional algorithmic programming is difficult if not impossible due to:
- Noise in gathered data and the variance in environmental and operating conditions
- The possibility of false alarms due to the difficulty with dealing with uncertainties
- The scarce nature of intermittent events making them difficult to measure and hence predict
- The complexity of some processes having many process factors
- The closed nature of some existing systems that are already measuring but the data isn’t accessible
- The varying nature of scenarios and end-user requirements preventing standard solutions
AI machine learning with auxiliary sensors is ideal for making sense of such complexity.