Embodiments of the present disclosure provide a two-phase process for searching root causes of a yield loss in a production line. In a first phase, an interaction between two process tools, that between two parameters, or that between one process tool and one parameter that is likely to cause the yield loss is identified. In a second phase, a threshold of the parameter that is likely to cause the yield loss and is obtained from the first phase is identified. In each phase, two different algorithms can be used to generate a reliance index (RI.sub.I) for gauging the reliance levels of their search results.

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.

A method and system for automatically generating interactive wiring diagram in an industrial automation environment are disclosed. The method includes acquiring real-time data associated with devices commissioned in a plant from one or more sensing units disposed at the respective devices. The method also includes determining connections between the devices in the plant based on the acquired real-time data using a lookup table. The method includes generating a wiring diagram of the plant based on the determined connections between the devices. The wiring diagram represents the devices located in the plant and physical connections between the devices. The method includes dynamically generating interactive wiring diagrams by superimposing the wiring diagram with the device connectivity status information associated with respective connections between the devices.

A petroleum pipeline safety system for preventing contamination of an environmentally sensitive area close to a pipeline includes an upstream portion of the pipeline supplying a flow of fluid material, a crossing portion of the pipeline receiving the flow of fluid material from the upstream portion and conveying the flow of fluid material through the environmentally sensitive area to a downstream portion of the pipeline, the downstream portion, a pipeline pressure activated valve selectively capable of blocking the flow of fluid material from entering the crossing portion based upon a change in pressure within the crossing portion, and a fluid capacitor connected to the upstream portion configured to filter out a pressure spike in the upstream portion associated with the valve blocking the flow of fluid material.

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.

According to one embodiment, an estimation device acquires a data set from history data. The history data includes a plurality of data IDs, path information, first and second qualitative variables. The data IDs respectively indicate a plurality of data flowing through a plurality of nodes. The path information indicates a path of the nodes for each of the data. The first and second qualitative variables are mutually-independent and indicate classifications of each of the data IDs. The data set includes a part of the data IDs having a first variable value assigned as the first qualitative variable. The estimation device estimates an overall relevance indicating a relevancy to the data set for each of the nodes. The estimation device generates a plurality of partial data sets. The estimation device estimates a partial relevance indicating a relevancy to each of the partial data sets for each of the nodes.

A technique is provided for providing early fault detection using process control data generated by control devices in a process plant. The technique determines a leading indicator of a condition within the process plant, such as a fault, abnormality, or decrease in performance. The leading indicator may be determined using principal component analysis. A process signal indicating a process variable corresponding to the leading indicator is then obtained and analyzed. A rolling fast Fourier transform (FFT) may be performed on the process signal to generate time-series data with which to monitor the process plant. When the presence of the leading indicator is detected in the time-series data, an alert or other prediction of the condition may be generated. Thus, process faults may be identified using fluctuations and abnormalities as leading predictors.

A state determination device acquires data related to an injection molding machine, stores a learning model obtained by learning an operation state of the injection molding machine with respect to the data, and performs estimation using the learning model based on the data. Further, the state determination device acquires a correction coefficient, which is associated with a type of the injection molding machine and equipment attached to the injection molding machine and numerically converts and corrects the estimation result with a predetermined correction function to which the acquired correction coefficient is applied.

A power distribution unit and a fault detecting method applied in the power distribution unit are disclosed herein. The power distribution unit includes an input terminal, an insulation fault detection circuit and a processing circuit. The input terminal is electrically coupled to a positive power line and a negative power line, and configured to receive a high voltage direct current (HVDC) voltage. The insulation fault detection circuit is configured to detect an insulation resistance value between a ground terminal and the positive power line or the negative power line. The processing circuit is configured to output a warning signal according to the insulation resistance value.

A controller for controlling a plurality of robots includes a failure prediction section configured to predict a failure time for each of the robots; and a load adjustment section configured to perform adjustment of a work load of each of the robots according to each of the predicted failure times, so that each of the robots operates until a maintenance time determined in common to each of the robots.