A test system comprises a plurality of test devices which are connected to a plurality of machines and which perform a basic test, and a test management device which is connected to the plurality of test devices via a network. The test management device includes an additional test setting part which sets an additional test for determining a cause of an abnormality when determining that the machine has the abnormality. The test management device includes an adjustment part which sets the test device that performs the additional test. The adjustment part determines that the basic test planned to be performed by the test device that performs the additional test is performed by the other test device.

Monitoring structure and a monitoring method for monitoring at least one step of a process performed on an industrial site is described, which allows prompt, reliable and efficient detection of disturbances based on readily available information, provided by the measurement results of the measurement devices involved in the performance of the step, and determined based on a model of the step, comprising primary models for the measurement devices comprising a basic function representing a time dependency of the measurement results of the device, a first property determined by best fitting the basic function to the measurement results obtained by the respective measurement device, a secondary model for each of the primary models, each comprising a second property given by a dispersion of residues between the measurement results obtained by the respective measurement device and the corresponding fitted function, and tertiary models for those pairs of measurement devices, which render correlated measurement results during faultless performance of the respective step, each tertiary model comprising a third property, given by a degree of correlation between simultaneously obtained measurement results of the respective pair of measurement devices during performance of the respective step, and a reference range for each of the properties, comprising a range within which the respective property is expected to occur during faultless performance of the step.

A monitoring control apparatus includes: a display controller to display an alarm screen for notifying a plurality of abnormalities occurring in a monitoring target device provided in an electric power system; a selector to select a first abnormality among the plurality of abnormalities; a degree-of-association calculator to calculate, for each of one or more other abnormalities except for the first abnormality among the plurality of abnormalities, a degree of association of the other abnormality with the first abnormality; and an extractor to extract the other abnormality as an associated abnormality associated with the first abnormality when the degree of association of the other abnormality is not less than a threshold. The alarm screen includes an associated alarm for notification of the extracted associated abnormality.

A system for cause and effect analysis for unsupervised anomaly detection is provided. The system accesses a connected system having a plurality of production and/or process lines. Each production line includes a plurality of operational assets. The processor is configured to access scheduling and production data corresponding to a plurality of products manufactured in each of the plurality of production lines. The processor is configured to access sensor data and asset configuration data and asset anomaly data corresponding to each of the plurality of operational assets. The processor is configured to analyze the sensor data, asset configuration data and asset anomaly data for each of the plurality of operational assets to generate an anomaly graph representation. The processor is configured to determine one or more anomalies and/or deviating events for the plurality of operational assets and associated causal inferences for the one or more anomalies and/or deviating events based on the generated anomaly graph representation.

A diagnostic device includes a storage unit that stores first information including a first abnormal event which occurred in the past in a plant, a first attribution event that is a cause of the first abnormal event, and a first occurrence probability of the first attribution event, in which a causal relationship between the first abnormal and attribution events is indicated by a tree structure, and second information including a second abnormal event which is supposed to occur in the plant but has not yet occurred, a second attribution event that is a cause of the second abnormal event, and a second occurrence probability of the second attribution event, in which a causal relationship between the second abnormal and attribution events is indicated by a tree structure; and an estimation unit that estimates the cause of the sign of the abnormality, based on the first and second information.

An example method for estimating vehicle repair costs includes receiving, via one or more sensors coupled to a damaged vehicle involved in a crash, crash information of the damaged vehicle, the crash information including vehicle operating information of the damaged vehicle; analyzing the crash information with respective collision data of a vehicle type that includes or is similar to the vehicle type of the damaged vehicle, the analyzing including comparing at least one of a velocity or an acceleration included in the vehicle operating information to vehicle operating characteristics indicated by the respective collision data of the vehicle type; determining one or more damaged vehicle parts of the damaged vehicle based on the analyzed crash information; determining a cost estimate to repair the damaged vehicle based on the one or more damaged vehicle parts; and transmitting an indication of the cost estimate.

An information processing apparatus detects an abnormality sign in a semiconductor manufacturing apparatus. The apparatus includes: a sensor data collector configured to acquire sensor waveform data with respect to a sensor value axis and a time axis measured by a semiconductor manufacturing apparatus that is executing a process according to a same recipe; a monitoring band calculator configured to calculate each monitoring band for the sensor value axis and the time axis used in a waveform monitoring method from a predetermined number or more of the sensor waveform data; and an abnormality sign detector configured to monitor a waveform of the sensor waveform data using each monitoring band for the sensor value axis and the time axis and detect an abnormality sign of the semiconductor manufacturing apparatus.

A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

Systems and methods include a computer-implemented method for providing a predictive pressure protection system. Flare sources, performance limits, and relationships between control valves and relief valves are established. A flare simulator is generated using piping isometric drawings. An emergency event is monitored, and information for the emergency event is filtered based on a control valve limit breach. Event start and finish time periods are divided into cases representing smaller time frames. Source max loads are determined for each case, and each case is run through the flare simulator. Flare/relief valve performance indicators are determined based on the source max loads after running each case.

Aspects of the present disclosure provide a method and apparatus for maintenance recommendations. Embodiments include receiving data related to a plurality of fault codes. Embodiments include determining, for each respective fault code of the plurality of fault codes, a unique subset of parameters for the respective fault code based on frequencies of the parameters within the plurality of fault codes in the data. Embodiments include receiving a plurality of fault records and a plurality of maintenance records. Embodiments include, for each respective maintenance record of the plurality of maintenance record, identifying one or more candidate fault records of the plurality of fault records and correlating a subset of the one or more candidate fault records with the respective maintenance record based on the unique subset of the parameters for the respective fault code corresponding to each respective candidate fault record of the one or more candidate fault records.