A diagnosis system diagnoses presence or absence of an abnormality from data pieces collected in a factory. The diagnosis system includes (i) a diagnoser that diagnoses presence or absence of an abnormality by classifying, in accordance with a diagnosis model defining a plurality of groups, the collected data pieces into at least one of the plurality of groups, (ii) an extractor that extracts, from the collected data pieces, a candidate for a data piece to belong to a new group different from the plurality of groups, (iii) a reception device that provides candidate information relating to the candidate extracted by the extractor, (iv) and a learner that learns a new model including the new group. The diagnoser diagnoses presence or absence of an abnormality with the new model after the new model is learned.

In a production system, a control unit controls a first apparatus configured to start to operate after a waiting time and a second apparatus configured to starting to operation after a waiting time. A first sensor outputs a value that changes in response to operation of the first apparatus. A second sensor outputs a value that changes in response to operation of the second apparatus. The control unit compares a first time period from a starting of the operation of the first apparatus to an occurrence of a change in the value of the first sensor with a predetermined first threshold value, and compares a second time period from a starting of the operation of the second apparatus to an occurrence of a change in the value of the second sensor with a predetermined second threshold value.

A petroleum pipeline safety system for preventing contamination of an environmentally sensitive area close to a pipeline is provided. The system includes a first portion of the pipeline including an upstream portion of the pipeline supplying a flow of fluid material and a flow restriction downstream of the first portion of the pipeline. The system further includes a second portion of the pipeline downstream of the flow restriction, receiving the flow of fluid material from the first portion and conveying the flow of fluid material through the environmentally sensitive area to a downstream portion of the pipeline. The flow restriction is configured to create a lower pipeline internal pressure within the second portion as compared to a pipeline internal pressure within the first portion. The system further includes a third portion of the pipeline downstream of the environmentally sensitive area and including the downstream portion of the pipeline.

A method for finding manufactured or to-be-manufactured products for defects includes obtaining basic information on processing history of products passed by each manufacturing machine at each processing workstation, and obtaining processing factor information of same, where each product is passed by a defect detection workstation after the product is passed by at least one processing workstation. The method includes obtaining quality information detected by each defect detection workstation. The method determines one or more problem manufacturing machines at one or more problem processing workstations according to the basic information on processing history and the quality information of the products. The method further determines one or more processing factors which influence the problem manufacturing machines according to the processing factor information of each manufacturing machine and the quality information of the products. A related electronic device and non-transitory storage medium are also provided.

Methods, apparatus, systems and articles of manufacture are disclosed to improve boundary excursion detection. An example apparatus to improve boundary excursion detection includes a metadata extractor to parse a first control stream to extract embedded metadata, a metadata label resolver to classify a boundary term of the extracted embedded metadata, a candidate stream selector to identify candidate second control streams that include a boundary term that matches the classified boundary term of the first control stream, and a boundary vector calculator to improve boundary excursion detection by calculating a boundary vector factor based on respective ones of the candidate second control streams that include the classified boundary term.

A service system and a server capable of increasing an operation rate of a production base are provided. The server acquires operation information of a component mounter in a mounting work from a host computer in the production base. The server generates analyzed result data obtained by analyzing the operation information based on the types of the manufacturers (component manufacturer and the package manufacturer) of electronic component and the package. The server transmits the generated analyzed result data to host computers in the other production bases.

Sensitivity calculations are provided of a process model through the rate of change of a model fingerprint with respect to process variables and defects. A fingerprint sensitivity table is generated, where process variables are associated with a set of fingerprint sensitivities. The fingerprint of incoming substrates is monitored through a production process by applying the same fingerprint method that is used in the process model. Calculations are made of the difference between the incoming substrate fingerprint and the process model predicted fingerprint. This difference fingerprint is compared against the table of fingerprint sensitivities to find the process variable most likely to be responsible for the difference. Spatial relationships between process variables and actual measurements on the substrate may be obtained. Correlation through fingerprint sensitivity improves the ability to pinpoint faulty process tools. The difference fingerprint may also identify the formation of defects on a substrate.

An industrial asset may have a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing a fault. An autonomous, resilient estimator may continuously execute an adaptive learning process to create or update virtual sensor models for that monitoring node. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, a level of neutralization may be automatically determined. The autonomous, resilient estimator may then be dynamically reconfigured to estimate a series of virtual node values based on information from normal monitoring nodes, appropriate virtual sensor models, and the determined level of neutralization. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.

Systems and methods for production line optimization include: receiving, from each of a plurality of equipment along the production line, equipment data at each interval of a plurality of intervals, the equipment data including equipment speed data, equipment state data, and equipment fault data; analyzing the equipment data with one or more optimization rules to identify an optimization setting; and, outputting the optimization setting to a production line device for modification of the production line.

Example implementations described herein estimate parameters for equipment that cannot be sensed directly, including determining if such equipment is running or stopping. Example implementations determine the standard throughput per equipment and product, based on history of equipment production data, extract previous and next equipment of robotic arms on the line by using physical topology information of robot arms and equipment, senses and determines throughput of associated robot arms and compares the robot arm throughput with the standard throughputs of the previous and next equipment, which help determine whether the previous/next equipment have stopped.