A diagnosis device stores a model used for diagnosing the condition of an industrial machine in a storage unit, acquires data related to the condition of the industrial machine, and based on the acquired data, determines the condition of the industrial machine by using the model stored in the storage unit. Then, in response to detecting that a component of the industrial machine has been replaced based on the acquired data and the data related to the determined condition of the industrial machine, the diagnosis device adapts the model stored in the storage unit to the condition of the industrial machine whose component has been replaced.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

A control layer automation device comprises a processor, one or more control layer applications, a database, a wireless interface, a device memory. Each control layer application is configured to perform a discrete set of automation functions. The database comprises a plurality of operator device identifiers and the wireless interface allows the one or more control layer applications to communicate with a plurality of operator devices via the plurality of operator device identifiers. The device memory comprises the one or more control layer applications. The control layer application manager is configured to manage execution of the one or more control layer applications on the processor.

Described in detail herein is an automated fulfilment system including a computing system programmed to receive requests from disparate sources for physical objects disposed at one or more locations in a facility. The computing system can combine the requests, and group the physical objects in the requests based on object types or expected object locations. Autonomous robot devices can receive instructions from the computing system to retrieve a group of the physical objects and deposit the physical objects in storage containers.

Devices, methods, and systems for determining the cause of a fault in a heating, ventilation, and air conditioning (HVAC) system are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to receive operational data associated with an HVAC system, receive control logic associated with a controller of the HVAC system, determine a cause of a fault occurring in the HVAC system based, at least in part, on the operational data associated with the HVAC system and the control logic of the controller of the HVAC system, and provide the cause of the fault occurring in the HVAC system to a user.

Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

The technology disclosed in the present specification is embodied as a support device for supporting maintenance on an apparatus including an expendable component. The support device includes at least one computer. The at least one computer executes: a process of acquiring an index indicative of a consumption level of the expendable component; a process of, at a time point when the index indicative the consumption level reaches a predetermined threshold, specifying a necessary period of time required for replacement of the expendable component at the time point; and a process of restricting an operation of the apparatus when a remaining period of time of the expendable component, the remaining period of time being associated with the threshold, is shorter than the necessary period of time required for the replacement.

Computer-implemented methods for configuring an Industrial Internet of Things (IIoT) edge node in an IIoT network to perform one or more functions, comprising: performing a situation analysis to determine a required change in one or more of an analytical model, a runtime component, and a functional block of the IIoT edge node based on a change in the one or more functions; and automatically provisioning a new or updated functional module to the IIoT edge node, based on the situation analysis, the new or updated functional module including one or more components, wherein each component includes at least one of a rules set, a complex domain expression with respect to a process industry, an analytical model, and a protocol decoder.

Methods and systems for detecting and correcting anomalies includes generating historical binary codes from historical time series segments. The historical time series segments are each made up of measurements from respective sensors. A latest binary code is generated from a latest time series segment. It is determined that the latest time series segment represents anomalous behavior, based on a comparison of the latest binary code to the historical binary codes. The sensors are ranked, based on a comparison of time series data of the sensors in the latest time series segment to respective time series data of the historical time series, to generate a sensor ranking. A corrective action is performed responsive to the detected anomaly, prioritized according to the sensor ranking.

In a preferred example embodiment of the present disclosure, a multi-agent control system includes: a malfunctioning-agent detector configured to detect a malfunctioning agent among a plurality of agents based on a malfunction signal received from each of the plurality of agents; and a multi-agent controller configured to control a neighboring agent around the malfunctioning agent to transmit a correction control signal to the malfunctioning agent such that the plurality of agents operate in a platoon.