A packaging machine of products in respective packages or envelopes includes at least one central control unit and a plurality of functional groups suitable to operate synergetically and in synchronism in order to obtain the desired product packaging result. The functional groups include at least one feeding group of the products, at least one feeding and/or forming group of the packages in which the products must be inserted, at least one insertion group of the products in the respective packages, at least one outlet group of the packages containing the respective products. Each of the groups include respective operating stations suitable for carrying out a specific operation on the products, on the packages or on the combination of same, in order to obtain a specific result in terms of packaging of the products. The central control unit includes a device for identifying at least one malfunctioning or faulty operating station, and a first device for deactivating said malfunctioning or faulty operating station.

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.

The present disclosure provides methods, systems, and computer-readable media for the fault detection and identification in an aircraft that may occur in real time during a flight, or any time the aircraft is operating. For example, a controller may receive and calculate various parameter values at various times during an aircraft flight, and compare those values to baseline values in order to determine if a fault has occurred. Additionally, the controller may identify a fault that has occurred by comparing a calculated fault signature value with a fault signature database comprising fault signatures and their associated faults.

To provide a time-series data analysis device which allows to compare time-series data easily. A time-series data analysis device analyzes the time-series data output by a machine tool, the time-series data analysis device including a time-series data acquisition unit configured to acquire the plurality of time-series data including operation conditions and operation results of the machine tool, a classification unit configured to classify the plurality of time-series data according to their respective operation conditions, a display control unit configured to perform control for displaying the plurality of time-series data according to the respective operation condition into which data was classified in the form of a list, and a calculation unit configured to calculate differences in the operation results for a plurality of time-series data selected from the plurality of time-series data included in one operation condition into which data was classified, wherein the display control unit performs control for displaying the calculated differences.

A behavior recognition device for recognizing behaviors of a semiconductor manufacturing apparatus includes a storage device and a control unit. The storage device is configured to store log data of the semiconductor manufacturing apparatus. The control unit is cooperatively connected to the storage device, and configured to build a transition state model based on the log data to analyze behaviors related to wafer transfer sequences and manufacturing operations of the semiconductor manufacturing apparatus.

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.

A device for estimating a cause of a mounting error includes a first determination section configured to perform a process of determining whether the error occurrence status is biased under a condition that an individual as the first factor is specified according to a difference in the second factor, on each of multiple individuals as the first factor, a second determination section configured to perform a process of determining whether the error occurrence status is biased under a condition that an individual as the second factor is specified according to a difference in the first factor, on each of multiple individuals as the second factor, and a cause estimation section configured to estimate a causative individual causing the mounting error based on determination results in the first determination section and the second determination section.

A method comprising a plurality of image acquisition units and a plurality of error detection units at a production plant. The production plant comprises a plurality of work zones, each of which are assigned at least one of the image acquisition units and at least one of the error detection units. A control unit is configured to detect a signal of at least one of the error detection units and based on this detection detect whether an error in the production plant has occurred in the work zone to which the error detection unit is assigned. When the control unit detects an error in a work zone, it provides image data from at least one image acquisition unit of that work zone to a user via an output unit. The invention also relates to a production plant using this method.

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.

A real-time control system includes a faulty variable identification technique to implement a data-driven fault detection function that provides an operator with information that enables a higher level of situational awareness of the current and likely future operating conditions of the process plant. The faulty variable identification technique enables an operator to recognize when a process plant component is behaving abnormally to potentially take action, in a current time step, to alleviate the underlying cause of the problem, thus reducing the likelihood of or preventing a stall of the process control system or a failure of the process plant component.