Workforce transformation technology handles complex operating conditions across massive sensor data, contributes to efficient plant operation and maintenance.
TOKYO—Toshiba Corporation (TOKYO: 6502) has developed a new anomaly detection AI that enables large-scale industrial plants to overcome a widespread and growing challenge: using a small workforce to achieve constant and effective monitoring of thousands of sensors, and the accurate detection of anomalous signals hidden among small variations in sensor values. The AI can currently be applied to power and industrial plants that use pumps to move fluids, and is the first of its kind*1 to realize high-level accuracy in detecting anomalies in the complex interactions between plant operating conditions and massive sensor values. Toshiba will present the technology at the 21st IEEE International Conference ICDM2021 LITSA Workshop on Data Mining on December 7.
At the core of the AI is Toshiba’s “Two-stage Auto-encoder”, a proprietary deep learning model that delivers highly accurate forecasts of sensor values in normal operating conditions; it detects anomaly hidden in massive sensor data by identifying deviations in actual values from the forecast values.
Industrial plants that must move fluids with pumps, such as power plants and water treatment facilities, use sensors to detect values for both small and large fluctuations in operation. Relatively small, rapid fluctuations appear simultaneously on a few sensors as a result of pump vibration or a local temperature change. Large fluctuations that occur on numerous sensors, with a larger amplitude and slower cycle, reflect changes in power and plant operation.
Commenting on the new AI, Susumu Naito, senior research scientist at Toshiba’s Corporate Research & Development Center, said: “The key factor behind the success of this technology is the deep and extensive knowhow Toshiba has gained from many years of experience in the energy and infrastructure business. We applied this to the design of two deep learning models, one for each fluctuation characteristic, and secured very high-level precision in predicting normal sensor values. These are compared with actual values to detect anomalies.”
Tests of the AI on the open datasets of the Water Distribution (WADI) testbed*2 confirmed the highest level of detection accuracy in the industry, a 12% improvement against prior art*3. In another test, Toshiba also verified that the AI can recognize and report anomalous signs a full 6.8 days earlier than possible with manual monitoring by a trained operator. Early detection of anomalies allows for condition-based maintenance, and contributes to efficient plant operation and maintenance.