Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B19/00—Programme-control systems
  • G05B19/02—Programme-control systems electric
  • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
  • G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B19/00—Programme-control systems
  • G05B19/02—Programme-control systems electric
  • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
  • G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F11/00—Error detection; Error correction; Monitoring
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/50—Context or environment of the image
  • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/31—From computer integrated manufacturing till monitoring
  • G05B2219/31282—Data acquisition, BDE MDE
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/31—From computer integrated manufacturing till monitoring
  • G05B2219/31318—Data analysis, using different formats like table, chart
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/32—Operator till task planning
  • G05B2219/32234—Maintenance planning
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components

Definitions

• the invention relates to a low-cost, user-friendly data collection and processing system for electronic controlled machines, robotic mechanisms, and devices used in industrial production, without the need for separate software and hardware.
• PLCs can already collect data such as part count, machine status, error codes, temperature, and pressure. However, when a user wants to utilize this product, they are required to use specific software and hardware for the PLC. The cost burden of PLC systems increases when the user only wants to obtain basic data such as part count, work order, operator name, start and end times, and downtime reasons. On the other hand, traditional methods of data collection encounter various problems.
• the invention involves a data collection system that operates in a plug-and- play manner, requiring no separate software or hardware. It incorporates a QR code embedded in a card for capturing worker information using a camera, reading the job quantity information from the machine's display using a camera and a pre-trained image processing algorithm, and capturing downtime reasons through a Human- Machine Interface (HMI). After collecting all these inputs, the system displays and reports this information in an online database.
• HMI Human- Machine Interface
• the system determines the maintenance frequency of machines and performs predictive analysis based on the captured downtime information displayed on the unit's screen, which is selected by the workers using buttons and recorded in the online database by the information processing unit. Additionally, the invention enables the protected data in the database to be accessible for monitoring through the control panel and provides authorized personnel with access to the obtained data. This allows for immediate notification of any disruptions or faults occurring in the machines to the relevant authorities within the company, minimizing operator dependence.
• the invention is a low-cost and user-friendly data collection system that can be used for purposes such as gathering data from industrial machines without the need for different software and hardware configurations.
• FIG. 1 Installation Flow Diagram View
• the invention involves a data collection unit equipped with a camera positioned to monitor the screens of industrial machines. It collects production data by capturing the machine screen images using image processing methods. Additionally, it utilizes an RFID kit to determine the start and end times of workers' shifts. The system also incorporates an interface (HMI - Human-Machine Interface) to capture machine downtime reasons from the machine operator. All these inputs are processed by an artificial intelligence-based information processing unit, which enables quality management and machine maintenance analysis. The collected data is immediately stored in a database through the use of loT (Internet of Things) technology and seamlessly integrated with ERP (Enterprise Resource Planning) programs.
• loT Internet of Things
• ERP Enterprise Resource Planning
• the dimensions for image processing are determined.
• Options such as dilation and erosion are provided, allowing the user to observe the real-time black-and-white representation of the data on the screen.
• the dilation feature expands the edges of the image, while the erosion feature thins them.
• an optimal value is found for clear data understanding, which is then saved for the program's regular operation.
• the Otsu method is employed to convert the image to a black-and- white format and remove the background. The white areas are then enclosed in frames, isolating the data individually and making them ready for the application of artificial intelligence models.
• the artificial intelligence model consists of input, feature detector (filter) 3x3, pooling layer, feature detector (filter) 5x5, pooling layer, and output.
• the feature detector layer extracts key features, and the pooling layer reduces the number of parameters, preventing overfitting and reducing processing power requirements.
• the coefficients in these layers are saved as matrices for future use. In the normal operation of the program, these coefficients are applied to processed data obtained from image processing. The values are classified into the closest category, and data from buttons on the interface, RFID, or other tools are collected and transferred to the server. After obtaining fault data, outlier metrics such as mean, mode, standard deviation, and skewness are calculated.
• the time elapsed from the reference point of the faults is determined, and a linear regression machine learning algorithm is used to identify the mathematical relationship between the increase in equipment wear and the frequency of faults. Based on this mathematical formula, the prediction of the next fault occurrence is estimated in terms of days from the reference point, and appropriate actions are recommended.
• the model is designed to incorporate reinforcement learning, where the dataset grows and the model approaches accurate results with each iteration.
• Shewhart Control Charts are computed using Python libraries to analyze data obtained from the human-machine interface. The quality metrics are established through collaboration with the customer company. The generated reports are then created using PDF libraries within the decision support system software platform based on node.js.
• the invention presents a low-cost and user-friendly data collection system that can seamlessly integrate with industrial machines, enabling quality management, maintenance analysis, and real-time data recording and reporting through loT and ERP integration.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• General Engineering & Computer Science (AREA)
• Quality & Reliability (AREA)
• Software Systems (AREA)
• Manufacturing & Machinery (AREA)
• Automation & Control Theory (AREA)
• Artificial Intelligence (AREA)
• Data Mining & Analysis (AREA)
• Evolutionary Computation (AREA)
• Medical Informatics (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Computing Systems (AREA)
• Mathematical Physics (AREA)
• Multimedia (AREA)
• General Factory Administration (AREA)
• Control By Computers (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=84084160

Family Applications (1)

Country Status (2)

Citations (4)

• 2022
  • 2022-06-02 TR TR2022/009059A patent/TR2022009059A2/tr unknown
• 2023
  • 2023-05-31 WO PCT/TR2023/050494 patent/WO2023234904A1/en unknown

Patent Citations (4)

Also Published As

Similar Documents

Legal Events