- Anomaly detection
Facilities failure sign detection technology: Detects deviations from normal behavior
This technology can detect signs of failure, even in facilities data with complex changes resulting from controls.
- Based on discussions with the client, among facilities data with multi-dimensional timelines, we designate data from sensors and devices where failure signs often arise as “data requiring particular attention.”
- This technology uses AI to detect deviations from “normal behavior” in data requiring attention, and interprets these deviations as signs of failure.
- Because only normal data is needed for AI learning, the technology can be applied even in cases where there is no data on abnormalities.
- Because it adopts deep learning as an AI method, it can be applied even in cases where the facilities data demonstrates complex changes due to controls.
- Detect signs of failure in facilities and equipment; e.g., plants, manufacturing equipment, and building facilities
Benchmarks, strengths, and track record
- Accommodate even data with complex changes.
- There are examples of applications in products, and examples of PoC in detecting failure signs.
- Building new systems for detecting coolant leaks using AI: Toshiba’s analytics AI SATALYS™ offers total solutions, from data analysis to construction and operation (in Japanese)
- Press release: “A new heat source model that can build systems for detecting coolant leaks – Release of a new model for air-cooled heat pump type heat source units” (in Japanese)