139results about "Registering/indicating machine production" patented technology

The invention relates to a method for determining the cause of a fault in a machine line for filling and packaging food and / or beverages. The machine line comprises a plurality of machines. Using this method, operating states for each machine are recorded and stored as a data set. After a machine shutdown is detected, a period from the data set is retrieved to determine the cause of the fault and used as the basis for input into a spatio-temporal relational graph convolutional network (stRGCN). The stRGCN provides a probability for each machine in the machine line that the corresponding machine is the cause of the shutdown.

The invention relates to a method for detecting anomalies in a set of signals, wherein the signal represents a quantity of a technical system to be checked, and wherein the method comprises: - Providing a model (10) for mapping a fixed number of signals in a past time window to an output target, wherein the output target maps the signals in a future time step as future signals (ŷ), and providing a static limit (α) with respect to existing residuals (R) of the predicted future signals (ŷ) by the model (10), - Capturing a set of current signals as well as the parameter values ​​currently influencing the signal to be checked, from at least one measurement source of a technical system, - Input of the current signals into the model (10) over the past time window to output a predicted future signal (ŷ), - Generating a dynamic dynamic value (β), wherein the dynamic dynamic value (β) depends on a variation range of the current signals and / or a variation range of the currently influencing parameter sizes over the past time window, wherein the dynamic dynamic value (β) is set higher the more the signals and / or the influencing parameter sizes vary over the time window, - Generating a dynamic limit (α n ) based on the dynamic value (β) and the static limit value (α), - Determining a current residual (R) based on the predicted future signal (ŷ), - Generating an anomaly alarm when the current residual (R) of the future signal (ŷ) predicted by the model (10) exceeds the dynamic threshold (α) n ) exceeds.

The invention relates to the technical field of textile production, and discloses a loom data intelligent acquisition system, which collects performance, environment and configuration parameters of a loom in real time through a data acquisition module, and ensures effective communication with various loom controllers in a micro-service mode. The data preprocessing module performs cleaning, format unification and standardization processing on the collected data to form a feature data set. Based on this, a performance prediction model building module builds a prediction model to predict the variation trend of future performance parameters of the loom. And the real-time data acquisition module and the prediction data generation module work cooperatively to provide data support for performance monitoring. The system also has the functions of performance monitoring, data comparison and analysis, deviation detection and alarm, and can monitor the performance of the loom in real time, compare prediction and actual data, timely find and mark deviation, trigger an alarm mechanism, and send detailed alarm information to operation and maintenance personnel, thereby realizing intelligent management of the loom.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

The present invention relates to a method for fault and / or anomaly detection. To solve the problem of providing a method for fault and / or anomaly detection which eliminates the disadvantages of the prior art and in particular allows high precision in a handling or manipulation task of an industrial robot, the method comprises obtaining first torque-determining data (D1) for a first joint (G1) and second torque-determining data (D2) for a second joint (G2) of an industrial robot (1), and determining whether a fault or anomaly exists in the handling or manipulation task of the industrial robot (1) based on the first torque-determining data (D1) and the second torque-determining data (D2).Furthermore, the invention relates to a device comprising means for carrying out the method according to the invention, a system comprising an industrial robot (1) with at least one first joint (G1) and a second joint (G2), and means for carrying out the method according to the invention for error and / or anomaly detection in a handling or manipulation task of the industrial robot (1), a computer program product comprising program instructions to execute and / or control the method according to the invention when the program is executed on a processor (201), and a computer-readable storage medium (209, 210, 211, 212, 213, 214, 215) comprising a computer program product according to the invention.

The invention relates to a computer-aided maintenance method (10) for correcting a fault condition (12) on a processing machine (14, 14a-c), comprising the method steps: a) Detecting (16) the fault condition (12) by identifying an error message (18) on the processing machine (14, 14a-c) and determining the time of the fault; b) Creating (22) a fault description (24) by determining fault data (26) at the time of the fault; c) Identifying (38) similar and / or identical fault conditions (42) by comparing (40) the fault description (24) with stored fault descriptions (42) by comparing the fault data (26) determined for the fault descriptions (24, 42); e) Issuing (50) a recommendation for action (52), including at least one suggestion for remedying the fault condition (12).

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

Method for detecting and evaluating an event during the operation of a sorting plant, wherein the method steps are executed on a monitoring system for machine parameters and / or material data streams of the sorting plant such that the monitoring system continuously records sensor data from aggregates and / or sensor data from machines and / or sensor data from machine components and / or sensor data from material streams 2; such that, after the sensor data of at least one sensor fulfills a detection criterion 5 defined for the respective sensor, in particular a defined threshold value, a timestamp is set in the memory, an event criterion is created, the existing sensor data remain stored assigned to the event criterion, and a pattern recognition routine is started,wherein the sensor data acquired from the timestamp onwards in a time interval of a predetermined duration are evaluated by means of the pattern recognition routine in such a way that a confidence value of 5 is repeatedly determined, and / or in such a way that, in the time interval, a decision is repeatedly made on the basis of the sensor data and / or on the basis of the currently determined confidence value of 5, such that a fault message is assigned to the event criterion and this is sent to at least one fault reporting point 13, 15, 16 or such that an event message is assigned to the event criterion and this is sent to a reporting point and the pattern recognition routine is executed again or such that the pattern recognition routine is aborted 14.,

The invention discloses an MES-based production process management method, and belongs to the technical field of production management. The method comprises the following steps: S1, production planning: creating a production order through an SAP system; then generating a material picking list and making a detection plan in the process management system according to the work order; and finally, the SAP system carries out account passing and material sending, and the SAP system generates a material voucher and a material receiving list. S2, warehouse material picking; s3, producing a whole board; s4, inputting production line information; s5, production: the process management system generates a material summary page and feeds the material into the SAP system; s6, detecting; s7, outputting and inputting; s8, production information input: a process report is sent to an SAP system; and S9, warehousing. The process management system sends a warehousing record to the SAP system and receives a warehousing order number returned by the SAP system; in the SAP system, goods receiving operation is carried out according to the warehousing order number; account passing operation is carried out in the process management system; and S11, closing the order.

The invention relates to a database establishment method and device based on ship VDR data. The method comprises the steps of obtaining original VDR data in VDR equipment and an associated storage medium; analyzing the original VDR data to obtain field features of the original VDR data; classifying the original VDR data based on the field features to respectively obtain time series data, structured data and vector data; a collaborative three-level association database is constructed, and the three-level association database comprises a time sequence database, a structure database and a vector database; and respectively storing the time sequence data, the structured data and the vector data in corresponding databases. And efficient application of the VDR data is realized.

The invention provides an agricultural work device, an agricultural work management system and a program, which can properly manage agricultural work information and can smoothly execute future agricultural work and business based on the utilization of the agricultural work information. A farming device (1) is provided with: a first imaging device; an agricultural work determination unit (34) that determines whether or not to perform agricultural work on an object on the basis of first image information obtained by capturing an image of the object by means of the first image capturing device; a farming execution unit (36) for executing farming on the basis of the determination result of the farming determination unit (34); a farming information generation unit (38) that generates farming information including farming results; a measuring unit that measures the position of the object; and a farming information management unit (40) that manages farming information and position information indicating the measured position.

To provide a diagnostic device, diagnostic method, program and diagnostic system that can improve the accuracy of diagnosing an operation state.SOLUTION: A diagnostic device comprises: a reception unit that receives, from an object device, context information corresponding to a prescribed operation of multiple pieces of context information defining operation of operation objects forming the object device determined with respect to each type of operation performed by the object device, and detection information on a physical quantity changing in response to operation of the operation objects; and a determination unit that determines the state of an operation object by using the detection information when the operation object is during the prescribed operation and a model corresponding to the received context information of one or more models determined for each of the one or more pieces of context information.SELECTED DRAWING: Figure 4

The invention discloses a time sensor scheduling method based on a 5G deterministic network, and relates to the technical field of communication engineering, and the method comprises the steps: enabling an automatic equipment group to be wirelessly connected with a time sensor through a 5G network, and obtaining the real-time operation time, the remaining processing time and the maximum operation time of each equipment; calculating a load value according to the real-time operation time and the maximum operation time, judging the load condition of the equipment according to the load value, marking categories and sorting, and carrying out rest scheduling according to the category and sorting; task load division, preferential distribution of emergency tasks, reasonable distribution of conventional tasks and elastic tasks are carried out by combining the theoretical residual capacity of the equipment, the tasks are stored in a buffer pool for specific equipment application, the task completion condition is monitored in real time, and task load stripping and equipment category adjustment are carried out when necessary; according to the method, automatic equipment scheduling and task allocation can be optimized, the production efficiency is improved, and the equipment fault risk is reduced.

The invention discloses a dynamic equipment fault early warning method based on a GRU algorithm and an SPC. The dynamic equipment fault early warning method specifically comprises the following steps of S1, collecting original data of dynamic equipment; s2, performing data processing on the training set, and storing a standardized model of the training set; s3, constructing a GRU model; s4, before the model is applied, processing measuring point parameter values; s5, estimating by applying a GRU model; s6, calculating deviation of the parameters of the main power equipment; s7, judging the health condition of the mobile equipment according to the deviation degree em; and S8, defining an SPC rule capable of changing the health degree of the mobile equipment in real time by referring to eight abnormal judgment criteria of the SPC standard. Considering deep learning model structure analysis, the invention provides a model based on deep learning, the model can quantify the operation state of the dynamic equipment, namely, health degree estimation, and the change trend of the health degree is judged in combination with an SPC rule, so as to carry out early warning on the health condition of the dynamic equipment.

Method (1) for determining (8) at least one operating parameter of a hand-held household appliance (2), characterized in that at least the following method steps are included: - Acquiring (3) measurement data from at least one sensor of at least one data processing device (5) worn close to the body by a user (4) during the use of the handheld household appliance (2), wherein the sensor measures vibrations generated by the handheld household appliance (2) which are at least partially transmitted to the body of the user (4), - Determine (8) at least one operating parameter of the hand-held household appliance (2) using at least some of the measurement data.

A method for evaluating a technical system comprising at least one electric motor is proposed. During an operational phase, an operational data set is recorded, comprising a plurality of measured variables, each of which comprises a plurality of measured values. The measured values ​​of each variable are recorded sequentially. Several temporally successive segments are extracted from the operational data set. Each segment of the operational data set is assigned to a class by a classifier. Each segment of the operational data set is then examined using at least one previously trained anomaly model. A warning message is issued if an anomaly is detected in any of the segments of the operational data set during the examination. A system according to the invention for evaluating a technical system comprises at least one anomaly model and one classifier.The system is set up to carry out the method according to the invention.

The present invention belongs to the field of automobile production line production, hot melt adhesive pressing and pasting fully automated operation technology, and discloses an automatic timing device, method, equipment and application for pressing hot melt adhesive into the sunroof air outlet. The automatic timing method for pressing hot melt adhesive into the sunroof air outlet includes: installing the air outlet to a designated position, instructing different buttons to start different groups of cylinders, and pressing the sandbags into the air outlet from top to bottom after reaching the designated position; when all the sandbags are pressed to the designated position, the switch is triggered, and all the pressed sandbags return to their positions at the same time after the device starts timing. The present invention saves costs: using its own resources, independent research and development, and independent transformation (reducing construction costs and maintenance costs). The present invention saves time: it saves the operator's quality requirement of 20 seconds of waiting time for pressing the sandbags; the present invention improves safety: it completely eliminates the occupational disease of wrist pain caused by pressing the air outlet; it eliminates the harm to the operator caused by the slipping of sandbags.

A recording device (100) for recording device information for evaluating a cause of a failure of a monitored device (50), which has a function of outputting the device information and an error signal indicating that an error has occurred when an error has occurred, comprises a device information acquisition section (110) for acquiring a plurality of pieces of device information from the monitored device (50), an error signal acquisition section (120) for acquiring the error signal from the monitored device (50), a hold information display section (130) for displaying the device information to be held based on the error signal, and a device information recording section (140) for recording the device information in a predetermined time including areas before and after a time at which the error signal was output,based on a display by the stop information display section.,

Methods and systems are provided for implementing a diagnostic technique for a closed crankcase ventilation (PCV) assembly. In one example, the method includes generating an estimated crankcase pressure at a crankcase pressure sensor location based on a nominal model and a fault model of a PCV system generated by a controller. The method further includes diagnosing a PCV system component based on a difference between the estimated crankcase pressure and an input from the crankcase pressure sensor; and triggering a sensor degradation indicator in response to the crankcase pressure sensor having a fault diagnosis.

The application discloses a method for evaluating the loading operation performance of a excavator, an electronic device and a storage medium, and the technical scheme is that a gyroscope is installed on the left and right handles of the excavator, the fixed working condition and the fixed material throwing are excavated, the operation actions of the operating hand are recorded by the gyroscope, and the excavator operation performance is evaluated by the composite degree and the control time of each action of the handle. The application has the characteristics of simple operation and intuitive data.

The invention relates to a computer-implemented method for planning a tool change at at least one production plant (10), with at least one tool (14) which has a limited service life. It is proposed that in a process step (22) at least one monitoring parameter of the tool (14) is retrieved from a planning program, and in a further process step (24) at least on the basis of the retrieved monitoring parameter an end of the tool's service life (14) is estimated and a reorder of the tool (14) is automatically planned.

Technical system for the distributed acquisition and state-dependent evaluation of usage-related interaction signals in digital transaction environments, comprehensive an interaction signal acquisition unit, designed to capture usage-related signals generated during a transaction process, a state mapping unit, designed to convert the acquired signals into a structured usage state representation, and an evaluation coordination unit, designed for the coordinated processing of multiple usage state representations from different transaction environments, whereby the evaluation is based on the state representations without central storage of the underlying raw signals.

This disclosure provides an optimization method, system, electronic device, medium, and program product for an excavator, relating to the field of excavator technology. The optimization method includes: acquiring excavator operation data; dividing each work cycle of the excavator into multiple work stages based on the operation data; evaluating the operation performance of the excavator in each work stage based on the operation data of each work stage; and optimizing at least one of the excavator's components and operating level based on the evaluation results of each work stage.

A condition detection system (100) for detecting a condition of a system in which a workpiece is moved in a pass, the condition detection system (100) comprising: a sampling unit (111) for sampling, in chronological order, sampling data indicative of a plurality of signal values ​​of a plurality of signals responding sequentially according to the passage of the workpiece; a dividing unit (112) for dividing a set of the plurality of signal values ​​included in the sample data in chronological order into a plurality of signal groups depending on a position of the workpiece to generate sampled divided data for each group indicating one or more signal values ​​in each group in chronological order; a learning unit (113) for performing machine learning for each group, using the collected divided data as learning data to generate a normal model, which is a learned model, for each group; and a state detection unit (120) for detecting a state of the plant using the normal model of each group; wherein the dividing unit (112) divides the set of the plurality of signal values ​​into the plurality of signal groups based on group information data indicating, on a group basis, the plurality of signals divided into the plurality of signal groups according to a reaction sequence; and wherein the condition detection system (100) further comprises: a data analysis unit (130) for analyzing the survey data in chronological order to determine one or more signals belonging to each of the plurality of groups and generating data indicative of the one or more signals belonging to each of the plurality of groups as the group information data, wherein the data analysis unit (130) determines a signal that reacts first in the system as a first signal in a first group, and zero or one or more signals that react successively during a period from a reaction of the first signal in the first group to a next reaction of the first signal in the first group as a signal or signals belonging to the first group, a signal responding after a last signal in an immediately preceding group is determined as a first signal in each of the second and subsequent groups, and one or more signals are determined which react sequentially as a signal or signals belonging to each of the second and subsequent groups during a period from a reaction of the first signal in each of the second and subsequent groups to a next reaction of the first signal in each of the second and subsequent groups.

The invention discloses an energy-saving data collaborative management system and method based on big data analysis, and relates to the technical field of energy-saving data collaborative management, and the method comprises the steps: evaluating the temperature change abnormal condition of an area in the refrigeration process of a central air conditioner, and obtaining a target area; analyzing the correlation degree between the monitoring index in the historical environment monitoring record and the people flow in the target area, and predicting the people flow change condition in the target area in the current period; analyzing the cold load demand condition of the target area in the current period to obtain cold load demand data; cold load demand data of different areas in a building in the current period and equipment energy-saving data of the central air conditioner are obtained, a control strategy of the central air conditioner is generated in combination with the cold load demand data, and refrigeration in the different areas is cooperatively managed based on the control strategy, so that it is guaranteed that cold load demands of all the areas are met, and the energy-saving performance of the central air conditioner is improved. And the energy consumption of the central air conditioner during working is greatly reduced, and the energy consumption is obviously reduced.

The invention relates to a method for operating a conveyor device (2) for conveying objects (70), the conveyor device (2) having: a circulating conveyor line (10) with one or more movable conveyor devices (20) for supporting the objects (70); and a maintenance device (30) for detecting the transmission line (10), the method comprising: moving the maintenance device (30) along the transmission line (10); detecting the transmission line (10) by means of the maintenance device (30) in order to acquire information about the top side (12) and / or the bottom side (14) of the transmission line (10); and comparing the acquired information with previously known information about the transmission line (10) to find geometric deviations in the transmission line (10). The invention also relates to a conveyor device (2) and a use.

A method for evaluating a technical installation is proposed. An operating data set is recorded during an operating phase. The operating data set is assigned to a class by a classifier. At least one performance indicator is generated from the operating data set. The generated performance indicator is assigned to the operating data set. The performance indicator assigned to the operating data set is evaluated. A system according to the invention for evaluating a technical installation comprises a data memory and a classifier. The system according to the invention is configured to carry out the method according to the invention.

Methods, apparatus, systems, and articles of manufacture are disclosed. An apparatus for executing a rule includes: at least one memory; a machine readable instruction; and a processor circuit to at least one of instantiate or execute the machine-readable instructions to: access an attribute value from the data collector, the attribute value including an operational value of a workstation within the process control system; creating a data model instance representing the workstation; applying the attribute value to the data model instance; identifying a rule associated with the data model instance; causing an executable package associated with the rule to be executed using the data model instance; and recording a result of execution of the executable package.

The present invention relates to the technical field of thermal systems and provides a remote monitoring and management system for thermal systems, comprising: a data acquisition module for acquiring real-time thermal system data from data acquisition points of a thermal system; a first storage module in which historical thermal system data from the data acquisition points of the thermal system is stored; a second storage module in which standard thermal system data from the data acquisition points of the thermal system is stored; and a local monitoring terminal that is electrically connected to the data acquisition module and the thermal system and communicatively connected to a remote monitoring and management terminal, wherein the remote monitoring and management terminal is electrically connected to the first storage module and the second storage module.The present invention ensures a reliable assessment of the operating condition of the thermal system.