Pellet grills and associated methods of operation are disclosed. An example pellet grill includes a burn pot configured to combust pellet fuel received within the burn pot. The pellet grill further includes an auger configured to deliver the pellet fuel to the burn pot. The pellet grill further includes an auger motor operatively coupled to the auger. The pellet grill further includes one or more processors configured to determine whether a shutdown sequence of the pellet grill has been initiated. The one or more processors are further configured, in response to determining that the shutdown sequence has been initiated, to command the auger motor to reverse a direction of rotation of the auger. The reversal is to cause pellet fuel which the auger has not yet delivered to the burn pot to be purged in a direction away from the burn pot.

A plant management method includes: acquiring correlation information indicating a correlation between a component subjected to a cyberattack and a component to be possibly affected by the cyberattack when a plant including a plurality of components is subjected to the cyberattack; and zoning the plurality of components on the basis of the correlation information.

An information processing device includes: a machine learning model selection part configured to select a machine learning model appropriate for a data set used for learning of the machine learning model; a calculation part configured to perform an optimization calculation by using the selected machine learning model to calculate process conditions that can achieve a target process result, predicted values of a process result corresponding to each of the process conditions, and reliability of the predicted values; a process condition selection part configured to select, among the process conditions that can achieve the target process result, one or more process conditions according to the predicted values of the process result and the reliability of the predicted values; and a display controller configured to display the selected process conditions, the predicted values of the process result corresponding to each of the selected process conditions, and the reliability of the predicted values.

According to one or more embodiments of the disclosure, a device in a network obtains parameters for entropy testing of industrial equipment that controls a physical process. Entropy is added to commands sent to the industrial equipment during the entropy testing. The device receives packets that were generated during the entropy testing of the industrial equipment and include sensor data regarding the physical process. The device determines whether the sensor data is inconsistent by analyzing the sensor data using a machine learning model that models the physical process. The device initiates a corrective measure, when the sensor data is determined to be inconsistent.

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

The system and method include obtaining fleet operating data from a fleet of machines and a machine operating data set from an individual machine. The fleet operating data includes a plurality of fleet operating data codes and fleet time data. The machine operating data includes at least one machine operating data code. A diagnostic system server is configured to establish a correlation for individual data codes and groups of data codes of the plurality of fleet data codes based and to compile one or more of the individual data codes and one or more of the groups of data codes into a plurality of diagnostic entries in a database. The diagnostic system analyzes the at least one machine operating data code to determine if an alert is associated with the at least one machine data code and provide any associated alerts to a user.

A diagnostic apparatus includes circuitry that acquires a detection result of a time-varying physical quantity generated by a machine that performs a plurality of processes; generates a determination result of the processing based on the detection result; and outputs, to the machine, batch determination information in units of a plurality of same type processes performed by the machine. The batch determination information indicates whether at least one of the plurality of same type processes is determined as abnormal. Based on the batch determination information, the machine performs an action.

The disclosure provides an automobile diagnosis method, apparatus and system. The method is applicable to a terminal device. The terminal device is communicatively connected to an automobile and includes virtual machine software and pieces of original instrument software. The virtual machine software runs virtual machines. The method includes: determining a piece of original instrument software for automobile diagnosis from the pieces of original instrument software; determining a virtual machine for running the piece of original instrument software from the virtual machines; acquiring fault data of the automobile; and controlling the virtual machine to run the piece of original instrument software, so that the piece of original instrument software analyzes the fault data to perform diagnosis for the automobile. The implementation is applicable to diagnosis of automobiles of many types, has an enhanced universality and improves stability of automobile diagnosis.

Disclosed is an integrity index detecting method for a device by means of a control output signal including an integrity information collecting step S10 of measuring and collecting a time interval between a control output signal and a subsequent control output signal; a defect information collecting step S20 of measuring and collecting a time interval between a control output signal and a subsequent control output signal; a setting step S30 of setting an integrity reference value and a defect reference value for the time interval between the control output signals; a detecting step S40 of measuring and collecting the time interval value between a control output signal and a subsequent control output signal; and an outputting step S50 of outputting the integrity index value detected in the detecting step S40 to provide the integrity index value to the manager.

A method for a device, including an integrity information collecting step S10 of measuring and collecting a time consumed from a start time of one operation to an end time in a normal state of a device; a defect information collecting step S20 of measuring and collecting a time consumed from a start time of one operation to an end time in a state of a device; a setting step S30 of setting an integrity reference value and a defect reference value for the time consumed for one operation of the device; a detecting step S40 of measuring and collecting a time value consumed from a start time of an operation of the device to an end time in real time and detecting an integrity index value of the device; and an outputting step S50 of outputting the integrity index value.