A defect profiling and tracking system for a process-manufacturing enterprise is provided. The system includes a memory and a processor. The processor is configured to access entity data for a plurality of entities of the process-manufacturing enterprise and process parameter data for one or more deviating entities. The processor is configured to analyze the entity data and the process parameter data for each of the deviating entities to determine a plurality of relationships between quality defects and the process parameters to generate a unique entity specific process signature (EPS) for each entity. The processor is configured to receive real-time process parameter data for one or more entities to generate a real-time process signature for the one or more entities and compare the real-time process signature of each entity with EPS corresponding to the entity to detect one or more EPS matches that are indicative of a quality defect.

A method and system for assigning slot addresses to modules in a fault tolerant industrial control system includes a pair of backplane switches on each base. Each backplane switch is configured to communicate between one backplane and the modules located on the base and to communicate between backplane switches located at adjacent bases. A backplane switch on a bank master base first assigns a base address and slot addresses to itself. The backplane switches on each additional base initiate transmission of a base address request. A base address response, including a base address and slot numbers for the adjacent base, is transmitted after a base has its own address assigned. Each base repeats the process in sequence along the bank, incrementing the base address by one and the slot address by the number of slots on the base and passing the new base and slot addresses along the bank.

Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

An electronic control device comprises an electronically rewritable nonvolatile memory which has first storage area and second storage area. The first storage area stores a control program in which control data is embedded, and the second storage area stores a plurality of control data usable for the control program and a rewrite program for rewriting the control data of the control program to one piece of control data selected from the plurality of control data.

A method and system for assigning slot addresses to modules in a fault tolerant industrial control system includes a pair of backplane switches on each base. Each backplane switch is configured to communicate between one backplane and the modules located on the base and to communicate between backplane switches located at adjacent bases. A backplane switch on a bank master base first assigns a base address and slot addresses to itself. The backplane switches on each additional base initiate transmission of a base address request. A base address response, including a base address and slot numbers for the adjacent base, is transmitted after a base has its own address assigned. Each base repeats the process in sequence along the bank, incrementing the base address by one and the slot address by the number of slots on the base and passing the new base and slot addresses along the bank.

A restoration work information storage unit stores a work item for restoration that is set for each location at which an abnormality is expected to occur in equipment to be managed, and priority information indicating a priority of execution for each of the work items. Upon detecting that an abnormality has occurred in the equipment and an operation of the equipment has been stopped, a restoration assist information generation unit generates restoration assist information, in which the work items for restoring the equipment are arranged in the order of execution according to the priority information, based on information of a location at which the abnormality has occurred. Upon detecting that a state of any location in the equipment has changed before a restoration work based on the restoration assist information is completed, a restoration assist information update unit updates the restoration assist information.

A device management system includes a device information transmitting apparatus that collects information about a device, and a management apparatus that communicates with the device information transmitting apparatus via a communication network. At least one of the management apparatus and the device information transmitting apparatus includes an anomaly state input unit that receives an anomaly state of the device, and a designating unit that designates, in accordance with the received anomaly state, first information to be used to identify a cause of the anomaly state of the device. The device information transmitting apparatus includes a first information transmitting unit that transmits the first information to the management apparatus.

Methods and systems for performing a validation process on a part to be process are provided. An identifier associated with the part is received. A listing of at least one automated process based on the identifier is displayed. An indication of a selected automated process from the listing is received. First instructions are transmitted to an automated process device for performing an automated processing program in accordance with the selected automated process. Processing results associated with the part are received from the automated process device. Second instructions are transmitted to a validation process device for performing a validation program in accordance with a validation process associated with the selected automated process. Validation results associated with the part are received from the validation process device. The processing results and the validation results are stored in association with the identifier.

A system for monitoring industrial processes using an evolving set of iso-functional smart nodes, for a distributed mesh network, each node comprising a Linux or Linux-compatible computer hardware architecture and a software stack. Each node receives an execute statement from a program hosted by another node in the mesh and, by execution on the computer hardware architecture of each node, the program is responsible for: two-way communication with other nodes or a central platform (Big Data Management), control of sensors or programmable automatons for monitoring a process or actuators, acquisition and logging of data from the latter, formatting of data and decentralized calculations, the central platform allowing the acquisition, management and storing of a data lake and comprising nodes for synchronous or asynchronous communication with the distributed mesh network.

The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.