Abstract: An information processing method for detecting conditions of a plurality of manufacturing devices respectively used by a plurality of entities includes acquiring sensor value data obtained by detecting physical quantities related to the manufacturing devices; individually storing collected sensor value data in a plurality of databases; generating by machine learning a plurality of learning models based on the stored sensor value data; and calculating a condition of the manufacturing device of one of the entities by inputting sensor value data acquired from the manufacturing device of the one of the entities to the one or more of the learning models selected by the one of the entities.

Abstract: An abnormality detection system includes an abnormality detection apparatus that detects an abnormality of a production device, a control device that performs operation control of the manufacturing device and transmits operating data, a sensor that detects a physical quantity related to operation or a product of the manufacturing device and outputs sensor value data, and a diagnostic apparatus. The abnormality detection apparatus determines a presence or an absence of an abnormality of the manufacturing device based on the operating data transmitted from the control device and the sensor value data output from the sensor and transmits the determination result to the control device. The abnormality detection apparatus further transmits the operating data and the sensor value data to the diagnostic apparatus.

Abstract: An abnormality detection system includes an abnormality detection apparatus that detects an abnormality of a manufacturing device, a control device that performs operation control of the manufacturing device and transmits operating data, and a sensor that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs sensor value data. The abnormality detection apparatus receives operating data transmitted from the control device, acquires sensor value data output from the sensor, calculates statistics of the sensor value data acquired, determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received, and transmits a determination result and the statistics to the control device.

Abstract: A computer program causing a computer to execute processing of detecting an abnormality of an industrial machine having a movable part is provided. The computer executes the processing oft acquiring physical quantity data on a time-series basis output from a sensor detecting a physical quantity related to a motion of the movable part; converting the physical quantity data on a time-series basis acquired to a time-series data image representing the physical quantity data; inputting the time-series data image converted to a learning model to calculate a feature of the time-series data image, the learning model being trained with a feature of the time-series data image related to the movable part in a normal condition; and determining a presence or an absence of an abnormality of the industrial machine based on the calculated feature.

Abstract: Faults in rotating machines can be diagnosed or detected using two radio frequency (RF) sensing modes. RF sensing phenomenon can be used to detect the presence of undesirable behavior in rotating machines including excessive bending, vibration, eccentricity, torsion, and longitudinal strain. RF based sensors represent a non-invasive solution. The sensing modes are based on RF metamaterials and Doppler effect influence and radar cross section evaluation all coupled with a machine learning algorithm. The system is based on monitoring resonance shift, negative permeability and return loss magnitudes. Electromagnetic numerical simulations showed a significant change in those magnitudes upon applied mechanical strains as compared to original reference unstrained cases. Metamaterial texturing design can be controlled by controlling the cells scale and substrate materials.

Abstract: Systems, devices, and methods for monitoring the health of a mechanical system that includes a rotating shaft as disclosed can measure various parameters of the rotating shaft to assess health and performance of the mechanical system. A measuring device can rotate with the rotating shaft and can allow for strain to be measured in tension, which can provide for accurate rotating shaft parameter measurements with low cost and simple installation. The measuring device can include a connector that can couple to the rotating shaft, a bridge that can couple to the connector, and a strain-measuring sensor associated with the bridge such that the strain sensor can measure deformation of a portion of the bridge that can deform with rotation of the rotating shaft. The measuring device can be designed to amplify the strain experienced by the rotating shaft which can reduce noise in the strain measurement.

Abstract: An anomalous scenario synthesizer apparatus includes a rotatable shaft configured to be rotationally driven about a rotation axis, a data acquisition system operably associated with the rotatable shaft and configured to measure attributes of the rotatable shaft, and a dynamic anomaly generator operably connected to the rotatable shaft. The dynamic anomaly generator is configured to generate at least one anomaly in the rotatable shaft while the rotatable shaft is rotating, and is configured to generate at least one dynamic label for each anomaly while the rotatable shaft is rotating. The dynamic label for each anomaly includes at least one descriptor corresponding to the anomaly that describes the anomaly such that a machine learning method may utilize the descriptor for machine learning.