A computer implemented invention for processing logging data from one or more industrial packaging or labelling machine
Patent Information
- Application Number
- NL2038934
- Authority / Receiving Office
- NL · NL
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2026-06-04
- Estimated Expiration
- 2044-10-27
Smart Images
Figure 00000025_0000 
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Abstract
Description
Title: A computer implemented invention for processing logging data from one or more industrial packaging or labelling machine Description: TECHNICAL FIELD The present invention generally relates to the field of industrial automation and control systems, and more particularly to the field of data processing and anomaly detection in industrial packaging or labelling machines. BACKGROUND In the current state of the art, complex industrial packaging and labelling machines comprise a plurality of electronic components, such as PLCs, CPUs, microprocessors, smart sensors, encoders etc. which generate a vast amount of logging data. This data is crucial for monitoring, control, and maintenance purposes, containing detailed information about the performance and status of various machine components. Typically, the generated data is stored locally, creating a need for significant storage capacity due to the sheer volume of data. This approach necessitates heavy storage specifications, making it cumbersome and expensive to maintain. Furthermore, the process of debugging, maintenance, and inspection requires local presence, adding to operational inefficiencies. To address the storage issue, another approach has been to transfer the logging data to remote locations, such as cloud servers. However, this method demands substantial bandwidth to handle the large data transfers, which can be both cumbersome and costly. Additionally, the transfer process might introduce delays, impacting the timely availability of crucial data for remote diagnostics and maintenance. A third approach involves pre-selecting data that is likely to be relevant for remote storage and inspection. This method attempts to mitigate storage and bandwidth issues by reducing the data volume before transmission. However, it is challenging to determine in advance which data will be relevant, leading to potential loss of valuable information and missing correlations between different data elements. Each of the existing solutions has significant disadvantages. Local data storage requires extensive and expensive hardware, and it requires a lot of resources and time to process vast amounts or raw data. Remote data transfer demands high bandwidth and incurs ongoing costs. Pre-selecting data for transmission risks omitting critical information, compromising the effectiveness of predictive maintenance and error handling. These challenges highlight the limitations of current methodologies in managing and processing the vast amounts of data generated by industrial machines. It is therefore a goal of the present invention to provide an improved system for processing logging data from industrial packaging machines such as shrink sleeve labelling machines, spouted pouches machines, cartooning machines, pressure-sensitive labelling machines, steam tunnels for shrink sleeve labelling machines and similar equipment. This system aims to efficiently reduce the data volume while preserving relevant information, thereby overcoming the above- mentioned disadvantages of the prior art at least in part. SUMMARY In a first aspect, there is provided, a computer implemented method for processing logging data from one or more industrial packaging or labelling machine for identifying anomalies, the machine comprising a plurality of electronic components for measuring and control of components within the machine, each component generating logging data, the method being performed by a processing unit, such as a local computer like an industrial personal computer, IPC, arranged to be located in the vicinity of the one or more industrial packaging or labelling machine, and comprising an interface module, a communication module, a storage module, and a processing module, the interface module being arranged to interface with the plurality of electronic components of the one or more industrial packaging or labelling machine, the communication module being arranged to communicate with a remote server, the storage module being arranged to store data, the processing module being arranged to store and execute computer program code to perform the method comprising the steps of: - receiving, from the interface module, logging data from the plurality of electronic components of the one or more industrial packaging or labelling machine connected to the interface module; - storing, by the storage module, the logging data received by the interface module as raw data locally in a local data storage module comprised in the storage module; - processing, by the processing module, at least some of the raw data into a refined dataset, wherein the amount of refined dataset is at least one order of a magnitude smaller than the raw data; characterized by the processing module executing an algorithm comprising the steps of: - performing a data reduction process by modelling the raw data to filter and select relevant logging data relating to anomalies, reducing the data volume into the refined dataset; - employing lossless data compression on the refined dataset; - determine correlations between different data elements comprised in the raw data, to identify patterns and relationships between the data elements, relating to anomalies, and storing these relational data sets alongside the refined dataset, and - transmitting, through the communication module, the refined dataset to the remote server for remote storage in a remote data storage module comprised in the remote server. In a second aspect, there is provided, a system for processing logging data from one or more industrial packaging or labelling machines for identifying anomalies, the machine comprising a plurality of electronic components for measuring and control of components within the machine, each component generating logging data, the system comprising: - a processing unit, such as a local industrial personal computer (IPC) arranged to be located in the vicinity of the one or more industrial packaging or labelling machines, the processing unit comprising: - an interface module arranged to interface with the plurality of electronic components of the one or more industrial packaging or labelling machines, - a communication module arranged to communicate with a remote server, - a storage module arranged to store data, - a processing module arranged to store and execute computer program code to perform the method, wherein the processing module is configured to: - receive logging data from the plurality of electronic components of the one or more industrial packaging or labelling machines via the interface module, - store the received logging data as raw data locally in a local data storage module comprised in the storage module, - process at least some of the raw data into a refined dataset, wherein the amount of refined dataset is at least one order of magnitude smaller than the raw data, by executing an algorithm that: - performs a data reduction process by modeling the raw data to filter and select relevant logging data relating to anomalies, reducing the data volume into the refined dataset, - employs lossless data compression on the refined dataset, - determines correlations between different data elements comprised in the raw data, to identify patterns and relationships between the data elements relating to anomalies, and stores these relational data sets alongside the refined dataset, - transmit the refined dataset to the remote server for remote storage in a remote data storage module comprised in the remote server via the communication module. In a third aspect, there is provided a computer program product for processing logging data from one or more industrial packaging or labelling machines for identifying anomalies, the computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method of, or an example of, the first aspect of the present disclosure. One aspect of the present invention relates to a computer-implemented method for processing logging data from one or more industrial packaging or labelling machines for identifying anomalies. A computer-implemented method may be understood as a method performed by a computer system or network, involving software and hardware components to execute specific tasks. Processing logging data refers to the operations carried out to handle, analyze, and manipulate the data generated by the electronic components of the machines for various purposes, including anomaly detection. An industrial packaging or labelling machine is a type of machinery used in industrial processes to package or label products, often involving multiple electronic components for control and measurement. The method comprises receiving logging data from the electronic components of the industrial packaging or labelling machine. In the context of the present disclosure, the term "packaging or labeling machine" refers to any equipment or machinery designed forthe application, processing, or handling of labels, packaging materials, or containers in a production line. This includes, but is not limited to, machines for applying shrink sleeve labels, pressure-sensitive labels, spouted pouch systems, cartoning, sealing, and filling, as well as associated systems for product handling, configuration, and integration within packaging processes. The term is intended to encompass all types of machines that perform functions related to packaging or labeling, regardless of specific design, method of operation, or the type of packaging material used. The method comprises receiving logging data from the electronic components of the industrial packaging or labelling machine through an interface module. An interface module is a component that enables communication between different systems or components within a system. The effect of this arrangement is that it allows seamless integration and data flow from the machines electronic components to the processing unit ensuring that all relevant data is captured for further processing. In the context of the present disclosure, the term "processing unit" refers to any form of hardware, and optionally software, capable of processing data in various forms. This includes general-purpose computers, industrial (personal) computers (IPC), embedded systems, microcontrollers, or any combination of hardware and software components designed to execute, control, or facilitate data processing tasks. The processing unit may be implemented in different configurations and architectures, ranging from standalone devices to integrated systems, capable of performing the steps of the method of the present disclosure. It is not limited to specific hardware types, allowing flexibility to encompass industrial computers and other specialized devices that may be required for processing data in the described system and method. Through the present disclosure, processing unit may also be referred to as industrial personal computer, IPC, but the skilled person may appreciate that it may be interpreted as any type of processing unit. The method includes storing the received logging data as raw data locally in a local data storage module comprised in the storage module. A storage module is a hardware component or assembly that stores data, while raw data refers to unprocessed data in its original form. An effect of this arrangement is that it provides immediate availability of the raw data for local processing, reducing the need for immediate data transfer and thus conserving bandwidth. The method involves processing at least some of the raw data into a refined dataset, where the refined dataset is at least one order of magnitude smaller than the raw data. A processing module is a hardware and software component that executes computer programs to process data and to this end comprises one or more hardware processors such as microcontrollers, PLCs, ASICS, CPUs, GPUs, etc. An effect of this arrangement is the significant reduction in data volume, which optimizes storage capacity and processing power by focusing only on the relevant information needed for anomaly detection. The processing module executes an algorithm comprising performing a data reduction process by modeling the raw data to filter and select relevant logging data relating to anomalies, thereby reducing the data volume into the refined dataset. An algorithm is a set of rules or steps performed by a computer. An effect of this arrangement is that it ensures only pertinent data is retained, enhancing the efficiency and effectiveness of subsequent analysis and anomaly detection processes. The method further employs lossless data compression on the refined dataset. Lossless data compression is a method of reducing data size without losing any information. An effect of this arrangement is that it further decreases the storage requirements and transmission bandwidth while ensuring that all original information can be perfectly reconstructed, maintaining data integrity. The method determines correlations between different data elements comprised in the raw data to identify patterns and relationships between the data elements relating to anomalies and stores these relational data sets alongside the refined dataset. It is expressed that the proposed method not only provides for determining correlations between data elements in respect of anomalies, but may in stead or in addition also determine improvements of the system. For example, it may be determined that a certain data element is related to the efficiency of a the machine or a component thereof, which thus provides insight that certain modifications resulting in the change of that particular data element, e.g. a sensor output, will improve the performance of the machine, might reduce wear or provide other useful insights. Determining correlations may involve statistical analysis to find relationships between data points, but may alternatively or additionally also involve employing machine learning algorithms. An effect of this arrangement is that it enhances the understanding of underlying patterns and relationships, which is crucial for accurate anomaly detection and predictive maintenance. The method includes transmitting the refined dataset to the remote server for remote storage in a remote data storage module comprised in the remote server through the communication module. A communication module is a component that enables data exchange between devices or systems over a network. An effect of this arrangement is that it allows for centralized data storage and analysis, facilitating remote monitoring and diagnostics while minimizing bandwidth usage due to the reduced data volume. The inventors had foreseen that the traditional methods of handling large volumes of logging data from industrial packaging or labelling machines were not sufficient to address the challenges of storage capacity, bandwidth usage, and effective anomaly detection. By integrating a local industrial personal computer with specific modules, or referred to as algorithmic modules, which may be referred to as bricks, for interfacing, communication, storage, and processing, a method was developed that not only reduces the data volume significantly but also preserves the most relevant information. The surprising effect of this approach is that it enables efficient local data processing and storage, drastically minimizing the amount of data that needs to be transmitted to remote servers. This arrangement effectively balances the need for comprehensive data analysis and the practical constraints of storage and bandwidth, leading to a more robust and scalable solution for predictive maintenance and error handling in industrial environments. In an example, the method comprises a processing module that is arranged to comprise a plurality of algorithmic modules that are configurable, allowing the modification to one or more of the algorithmic module to change variables of the data reduction process, data compression mechanism, or correlation detection mechanism in place. It may be provided that this feature allows for configurability of the data processing, enabling the system to optimize the data handling processes according to specific requirements or changes in operational conditions. An effect of this feature is the enhanced efficiency in data processing, storage, and transmission, as the system can tailor its operations to the most relevant and current data needs. In a further example, the modification of variables of one or more of the algorithmic modules can be changed dynamically, either as a self-learning mechanism or based on user input. . It may be provided that this feature allows the system to dynamically adapt and improve its performance through self-learning algorithms or user-directed adjustments. An effect of this feature is the ability to maintain optimal data processing and anomaly detection performance over time, even as the operating environment or data patterns evolve. In an example, the method includes a data reduction process performed by the algorithmic modules based on regression analysis to filter and select relevant logging data relating to anomalies. It may be provided that regression analysis models are used to identify and prioritize the most significant data points. An effect of this feature is the reduction of data volume while preserving the integrity and usefulness of the information, which enhances the efficiency of data storage and subsequent analysis. In an example, the method includes data compression performed by the algorithmic modules using a lossless compression technique to ensure no loss of relevant logging data. It may be provided that lossless compression techniques are applied to maintain the full fidelity of the original data while reducing storage space requirements. An effect of this feature is the efficient utilization of storage capacity without compromising data quality, which is particularly beneficial for detailed anomaly detection and troubleshooting. In an example, the method includes a correlation detection mechanism that identifies relationships between data elements such as motor information and sensor data, and stores these relational data sets alongside the refined dataset. It may be provided that statistical and analytical methods are used to uncover and record significant data relationships. An effect of this feature is the enhanced ability to detect and understand complex anomalies and interactions within the machinery, leading to more effective predictive maintenance and error resolution. In an example, the method comprises a timing analyzer module that measures the time taken for a product to move from one location to another within the machine, based on sensor data. It may be provided that timing analysis is used to track product movement and detect any deviations or delays. An effect of this feature is the precise monitoring of machine performance, allowing for timely interventions and adjustments to maintain optimal operation. In an example, the method comprises employing algorithmic modules that are configured using a JSON configuration file that allows easy addition and modification of algorithmic modules without manual intervention. It may be provided that JSON files are used to standardize and simplify the configuration process. An effect of this feature is the streamlined setup and maintenance of the system, reducing the need for specialized technical expertise and minimizing the risk of configuration errors. In an example, the method includes the step of the refined dataset being transmitted to the remote server, which includes metadata such as the history and routing information of the processed messages. It may be provided that detailed metadata is appended to the refined dataset to facilitate tracking and analysis. An effect of this feature is the improved traceability and context for the transmitted data, enhancing the ability to perform remote diagnostics and historical analysis. In an example, the method includes a local data storage module that retains raw data for a predefined period to allow for retrospective analysis in case of anomaly detection. It may be provided that raw data is temporarily stored to enable thorough investigation of detected issues. An effect of this feature is the provision of a robust fallback mechanism for detailed troubleshooting, ensuring that no critical data is overlooked during the anomaly resolution process. In an example, the method includes a communication module that employs secure communication protocols to ensure the integrity and confidentiality of the data transmitted to the remote server. It may be provided that encryption and secure transmission methods are used to protect data during transfer. An effect of this feature is the safeguarding of sensitive information against unauthorized access and data breaches, maintaining the confidentiality and reliability of the transmitted data. In an example, the method includes a processing module that uses machine learning algorithms to continuously improve the data reduction and anomaly detection processes based on historical data and user feedback. It may be provided that machine learning models are employed to refine and adapt the processing algorithms over time. An effect of this feature is the continuous enhancement of the system's accuracy and effectiveness, leveraging accumulated knowledge and user inputs to optimize performance. In an example, the method comprises an interface module that supports multiple industrial communication protocols to interface with various types of electronic components within the packaging or labeling machine. It may be provided that the interface module is compatible with a wide range of industry-standard protocols. An effect of this feature is the increased versatility and interoperability of the system, enabling seamless integration with diverse machinery and electronic components. In an example, the method comprises a user interface that allows users to configure and monitor the performance of the processing module and the algorithmic modules. It may be provided that a user-friendly interface is provided for system management. An effect of this feature is the enhanced accessibility and control for users, facilitating easier configuration, monitoring, and adjustment of the system's operations. In an example, the method comprises the refined dataset with timestamps to allow for chronological analysis of the logging data. It may be provided that timestamps are added to the processed data to maintain temporal context. An effect of this feature is the ability to conduct time-based analysis and trend monitoring, providing valuable insights into the operational dynamics and historical performance of the machinery. In an example, the method comprises a processing module that provides alerts to the remote server or local users when anomalies are detected based on the refined dataset. It may be provided that automated alert mechanisms are in place to notify relevant parties of detected issues. An effect of this feature is the prompt communication of critical information, enabling timely responses and mitigating the impact of potential problems. In an example, the method comprises a domain-based routing mechanism to manage communication between algorithmic modules in different domains. It may be provided that domain-based routing is used to control and direct data flows between distinct system segments. An effect of this feature is the organized and efficient management of data traffic, ensuring appropriate and secure communication across different parts of the system. In an example, the method comprises algorithmic modules that can process messages from multiple sources and aggregate the data before performing the data reduction process. It may be provided that multi-source data aggregation is implemented to consolidate input data. An effect of this feature is the comprehensive and unified processing of data from various origins, enhancing the robustness and reliability of the refined dataset. In an example, the method comprises a feedback mechanism that allows users to provide input on the relevance of the detected anomalies, which is then used to refine the processing algorithms. It may be provided that user feedback is incorporated into the system to adjust and improve the algorithms. An effect of this feature is the continuous refinement of the anomaly detection processes, leveraging user insights to enhance system accuracy and effectiveness. In an example of the computer-implemented method, the method may further comprise an operator interface module configured to allow operators to input information related to machine events, including maintenance activities, such as cleaning, incidents, or product breakage, wherein the inputted information is processed and stored alongside the refined dataset to enhance anomaly detection and system analysis. It is to be understood that each feature described in the context of an example relating to one aspect of the invention, is similarly applicable to other aspects of the invention, including but not limited to a system, a computer-implemented method, a computer program product, and a data carrier signal. Each feature thus contributes to the overall functionality and technical advantages of the invention in its various embodiments. Similarly, any advantages discussed in relation to specific features of an aspect or example of the present invention are equally pertinent to other aspects of the invention. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure will be explained in more detail below by means of examples of a device according to the present disclosure shown in the drawings, in which: Fig. 1 shows the steps of a method according to an aspect of the present disclosure; Fig. 2 shows a system according to another aspect of the present disclosure. DETAILED DESCRIPTION Figure 1 illustrates a flowchart 100 that details the steps of the computer- implemented method for processing logging data from one or more industrial packaging or labeling machines. The method is designed for identifying anomalies and optimizing data processing. The flowchart is structured in a sequential manner, each step numbered, but it is expressed that the sequence may be changed and that some or all steps may be performed in a different order, or partly or fully in parallel. The first step, labeled 101, involves receiving logging data. This step signifies the action of collecting logging data from the various electronic components within the packaging or labeling machine. The interface module of the local processing unit, such as an industrial personal computer (IPC), facilitates this data reception, In the context of the present disclosure, the term "packaging or labeling machine" refers to any equipment or machinery designed for the application, processing, or handling of labels, packaging materials, or containers in a production line. This includes, but is not limited to, machines for applying shrink sleeve labels, pressure-sensitive labels, spouted pouch systems, cartoning, sealing, and filling, as well as associated systems for product handling, configuration, and integration within packaging processes. The term is intended to encompass all types of machines that perform functions related to packaging or labeling, regardless of specific design, method of operation, or the type of packaging material used. In the context of the present disclosure, the term "processing unit" refers to any form of hardware, and optionally software, capable of processing data in various forms. This includes general-purpose computers, industrial computers, embedded systems, microcontrollers, or any combination of hardware and software components designed to execute, control, or facilitate data processing tasks. The processing unit may be implemented in different configurations and architectures, ranging from standalone devices to integrated systems, capable of performing the steps of the method of the present disclosure. It is not limited to specific hardware types, allowing flexibility to encompass industrial computers and other specialized devices that may be required for processing data in the described system and method. Through the present disclosure, processing unit may also be referred to as industrial personal computer, IPC, but the skilled person may appreciate that it may be interpreted as any type of processing unit. In step 102, the received logging data is stored locally as raw data. This storage occurs within the local data storage module that is part of the storage module of the processing unit, alternatively, the actual storage itself may also be performed at a different, e.g. local or remote, location in a dedicated storage. The next step, 103, involves processing the logging data. During this step, at least some of the raw data is processed into a refined dataset, significantly smaller in volume compared to the original raw data. The reduction in data volume may be achieved through one or a series of data filtering and compression techniques designed to retain only the most relevant information needed for anomaly detection and other relevant information. The refined dataset may be created by first modeling the raw data to filter out unnecessary or redundant information. For instance, logging data that consistently falls within normal operational ranges over time can be binned or aggregated into statistical summaries, such as mean, median, or standard deviation. This binning process compresses large quantities of routine data into smaller, representative values, thus reducing the overall dataset size. Only critical data points, such as deviations, sudden spikes, or patterns that indicate potential anomalies or other differences, may be preserved in their full detail. Data reduction may also involve prioritizing the storage of key operational parameters that are indicative of machine health. For example, sensor data that directly correlates with known failure modes (e.g., motor temperature spikes, vibration frequencies, or pressure variations) may be kept in its original form or with higher precision. In contrast, less significant or auxiliary data (e.g., constant environmental conditions) might be abstracted or removed entirely. After filtering, the processing module may employ lossless data compression techniques, described in more detail below, to further reduce the dataset size. This ensures that while the dataset volume is minimized, the integrity of the critical information is maintained. The refined dataset may also include calculated correlations between various data elements, providing insight into patterns and relationships thatmay relate to anomalies. These correlations may be stored alongside the refined dataset, enhancing the system's ability to perform targeted, predictive analysis and enabling the identification of complex fault scenarios. Thus, the refined dataset becomes a focused, highly informative subset of the raw data, optimized for remote transmission and analysis. Step 104 is the data reduction process. During this step, the processing module may execute an algorithm to model the raw data, filtering and selecting relevant information associated with anomalies. This step may involve several layers of data analysis, where the algorithm distinguishes between routine operational data and potential indicators of anomalies. For instance, standard operational parameters, such as steady temperature readings or consistent motor speeds, may be aggregated into statistical summaries like mean values, ranges, or variance metrics. The data reduction process may also involve binning time-series data, where sensor readings are grouped into intervals that represent normal operational behavior. For example, if a sensor measuring motor vibration shows consistent values within a specific range, these readings could be summarized rather than stored individually, thereby reducing the dataset's volume. The algorithm could also employ techniques such as regression analysis or machine learning models to identify patterns within the data that correlate with known fault conditions. In practical embodiments, data reduction may include filtering out noise or insignificant data points. For instance, a packaging machine might have sensors monitoring environmental conditions like room temperature or humidity, which might fluctuate naturally without affecting the machine's operation. Such fluctuations could be binned into broader categories (e.g., "normal," "slightly elevated," "high") or omitted if they do not impact performance. The refined dataset resulting from this process focuses on the most critical data, preserving only the information necessary for accurate anomaly detection while minimizing storage requirements. Step 105 involves employing lossless data compression on the refined dataset to further minimize its size without losing any information. Several lossless compression techniques, such as the DEFLATE algorithm or Huffman coding, may be used here. These techniques work by identifying and encoding repetitive patterns or sequences within the dataset more efficiently. For example, if the machine produces periodic logging data with recurring values (e.g., "temperature stable at 70°C" every minute), the compression algorithm can replace these repetitions with a shorter representation. This not only conserves storage space but also streamlines the transmission of data. The choice of compression algorithm can vary depending on the nature of the data; in some cases, the system may use domain-specific compression methods optimized for industrial logging data formats. For example, run-length encoding may be suitable if the dataset contains long sequences of identical sensor readings. The goal of this step is to ensure that while the data volume is reduced, the information integrity is maintained, allowing the data to be perfectly reconstructed when decompressed for analysis. In step 106, the processing module determines correlations between different data elements within the raw data to identify patterns and relationships related to anomalies. This step may involve statistical analysis, such as calculating correlation coefficients between sensor readings, or machine learning algorithms that can detect more complex interactions between multiple variables. For example, a rise in motor temperature might correlate with an increase in vibration frequency. By identifying and storing such relationships, the system may provide valuable insights into potential causes of machine malfunctions. These correlations are stored alongside the refined dataset to enhance future analyses. In practical embodiments, this could mean that when a certain pattern in sensor data, such as increased pressure in conjunction with an abnormal sound frequency, is detected, the system logs these findings for further evaluation. This relational data becomes a crucial part of predictive maintenance, allowing operators to identify recurring patterns that precede faults. The system might also adjust the correlation detection process dynamically, refining its analysis based on historical data and user feedback to improve its accuracy over time. The final step, 107, involves transmitting the refined dataset to a remote server for storage through the communication module. To optimize the transmission, the communication module may use secure protocols like HTTPS or MQTT, ensuring data integrity and confidentiality during transfer. The reduced size of the refined dataset significantly lowers the required bandwidth, enabling quicker and more efficient data transmission, even in environments with limited network capabilities. For example, in a factory setting with multiple machines generating large volumes of data, the reduced dataset ensures that essential information is transmitted in near-real-time to a central server. Once received at the remote server, the dataset may be used for various purposes, such as long-term storage, further analysis, or integration into a broader monitoring system. In some implementations, the remote server may also provide feedback or alerts to local operators based on the transmitted data, facilitating timely interventions and maintenance actions. Figure 2 illustrates a schematic representation of the system, designated as 200, for processing logging data. At the core of the system is the local processing unit, 210, which may be an industrial personal computer (IPC), server, or the like, located near the packaging or labeling machines 220. This processing unit could contain multiple modules 211- 214, and at least an interface module 211, communication module 212, storage module 213 and processing module 214, that work together to implement the data processing steps. There may be other modules present, which are not described in more detail and illustrated in Fig. 2 by outlining the blocks with a dashed line instead of a solid one. The IPC can for example include hardware and software components configured to interact with various machine elements, making it versatile for different operational environments. The interface module, 211, serves as the bridge between the IPC and external devices or systems, such as the electronic components 221-223, 225 of the packaging or labeling machine 220 or machines (in the figure there is shown only one such machine, but there may be more and each may have a dedicated separate or integrated local processing unit 210, but one local processing unit may also be used for two or more packaging or labeling machines. The interface module is responsible for connecting with the external devices for collecting logging data from sensors, actuators, and other control elements within the machinery. It can interface with the components using various industrial communication protocols, such as Modbus, OPC UA, or Ethernet / IP. In certain implementations, the interface module may also support wireless communication to facilitate data collection in environments where cabling is impractical. The communication module, 212, facilitates data transfer between the local processing unit and a remote server. It may employ a variety of communication methods, such as Ethernet, Wi-Fi, or cellular networks, to ensure reliable transmission of the refined dataset to remote storage or cloud-based analysis systems. In some embodiments, the communication module may incorporate secure data transmission protocols like HTTPS or MQTT, enhancing data integrity and confidentiality during transmission. The storage module, 213, contains at least one, but may also contain two or more local data storage module(s), 213-1, 213-2, where logging data received from the interface module may be stored. This storage module could use various forms of storage media, such as solid-state drives (SSD), hard disk drives (HDD), or even volatile memory, depending on the specific requirements of the application. The storage module allows for immediate access to raw logging data, which is beneficial for local processing and analysis. The processing module, 214, is a dedicated or general processor(s) configured to execute computer program code that performs the data processing method. This module may include various functionalities, such as data reduction algorithms, lossless data compression techniques, and correlation determination processes. The processing module may utilize hardware accelerators, like GPUs or FPGAs, to enhance processing speeds, particularly when handling large volumes of data. Additionally, the processing module could support dynamic adjustments, where algorithmic modules can be modified or replaced based on evolving operational requirements or user input. The remote server, 220, serves as the endpoint for the refined dataset transmitted by the communication module. It may include a remote data storage module, 221, where the refined dataset is stored for further analysis, long-term archiving, or integration into larger data systems. In some practical embodiments, the remote server may also host machine learning models or analytical tools to interpret the refined dataset, providing feedback that could be used to optimize machine performance or predict maintenance needs. Arrows depicted in Figure 2 illustrate the flow of data between the system's components, including the interface module, storage module, processing module, communication module, and the remote server. These arrows represent the pathways for data collection, processing, and transmission, showing the interaction between different parts of the system. The arrangement and interaction of these components may vary based on specific implementations, allowing for flexibility in the system design. Based on this description, a skilled person may envision modifications and additions to the method and system disclosed, such as incorporating additional modules or alternative data transmission methods. These potential modifications fall within the scope of the appended claims, reflecting the system's adaptability to various industrial settings and data processing needs. It will be clear that the intention of the above description is to shed light on the working of possible embodiments of the present invention, and not to limit the scope of protection of the invention. Starting from the description, a person skilled in the art is able to conceive of and use various embodiments that fall within the inventive concept and scope of protection of the present invention.
Claims
1. A computer-implemented method for processing log data of one or more industrial packaging or labeling machines for the identifying anomalies, where the machine a multitude of electronic includes components for measuring and controlling components within the machine, where each component generates log data, where the method is executed by a local processing unit, which is set up to be located near the one or more industrial packaging or labeling machines to be located, and which a interface module, a communication module, a storage module and a includes a processing module, where the interface module is configured to interface with the multitude of electronic components of one or more industrial packaging or labeling machine, where the communication module is configured to communicate with an external server, where the storage module is configured to to store data, where the processing module is configured to to store and execute computer program code to perform the method, where the following steps are performed: - the receiving, from the interface module, of log data of the plural to electronic components of one or more industrial packaging or labeling machines connected to the interface module; - the storage, by the storage module, of the log data that by the interface module are received as raw data, locally in a local data storage module included in the storage module; - the processing, by the processing module, of at least a part of the raw data to a refined dataset, where the amount of refined dataset at is at least one order of magnitude smaller than the raw data; characterized by the fact that the processing module executes an algorithm that includes the following steps: - performing a data reduction process by the raw data model to filter relevant log data regarding anomalies and to select, whereby the data volume in the refined dataset is reduced; - applying lossless data compression to the refined dataset; - determining correlations between different data elements that are included in the raw data, to identify patterns and relationships between the data elements with with regard to identify anomalies and these relational datasets alongside the to store refined dataset, and - sending, via the communication module, the refined dataset to the external server for external storage in an external data storage module that is recorded on the external server.
2. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the processing module comprises a multitude of algorithmic modules that be configurable, whereby the modification of one or more of the algorithmic modules it is possible to variables of the data reduction process, data compression mechanism or correlation detection mechanism at site to change 3. The computer-implemented method for processing log data according to claim 3, where the change in variables of one or more of the algorithmic modules can be dynamically modified, either as a self-learning mechanism or based on user input.
4. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the data reduction process performed by the algorithmic modules is based on regression analysis to relevant log data regarding to filter and select anomalies.
5. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the data compression performed by the algorithmic modules a lossless compression technique is (to guarantee that there are no relevant log data is lost).
6. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the correlation detection mechanism identifies relationships between data elements such as engine information and sensor data, and stores these relational datasets alongside the refined dataset.
7. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the processing module further includes a timing analyzer module that measures the time that a product needs to move from one location to another within the machine move, based on sensor data.
8. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the algorithmic modules are configured according to a data exchange file format, and in particular one or more of a markup language or data serialization format, and more specifically one or more of JSON, XML, YAML, HTML and SGML, 9. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the refined dataset which is sent to the external server includes metadata, such as the history and routing information of the processed messages.
10. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the local data storage module raw data for a predefined period retains (to enable retrospective analysis in the event of anomaly detection).
11. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the communication module uses secure communication protocols (to ensure integrity and confidentiality of the data sent to the external server guarantees).
12. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the processing module uses machine learning algorithms (to the data reduction and continuously improve anomaly detection processes based on historical data and user feedback).
13. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the interface module supports multiple industrial communication protocols to interface with various types of electronic components within the packaging or labeling machine.
14. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the system further includes a user interface (with which users the performance of the can configure and monitor the processing module and the algorithmic modules).
15. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the refined dataset contains timestamps (to enable chronological analysis of the log data to make).
16. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the processing module warnings to the external server or local users provided when anomalies are detected based on the refined dataset.
17. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the system further includes a domain-based routing mechanism to communication between to manage algorithmic modules in various domains.
18. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the algorithmic modules can process messages from multiple sources and the data merge before the data reduction process is performed.
19. The computer-implemented method for processing log data according to one of the preceding conclusions, whereby the system a includes a feedback mechanism that enables users to provide input on the relevance of the detected anomalies, which is subsequently used to the to refine processing algorithms.
20. A system (200) for processing log data of one or more industrial packaging or labeling machines (220) for identifying anomalies, where the machine has a multitude of electronic components (221, 222, 223) includes for measuring and controlling components within the machine, where each component generates log data (231, 232, 233), where the system (200) includes the following: - a local processing unit (210) set up to be near the to be located one or more industrial packaging or labeling machines (220), where the local processing unit (210) includes the following: - an interface module (211) designed to interface with the plural to electronic components of one or more industrial packaging or labeling machines, - a communication module (212) designed to communicate with a external server, - a storage module (213) configured to store data, - a processing module (214) designed to put computer program code on to save and execute to carry out the method, whereby the processing module is configured to: - log data (231, 232, 233) received from the plurality at electronic components (221, 222, 223) of one or more industrial packaging or labeling machines (220) via the interface module (211), - save the received log data (221, 222, 223) as raw data local in a local data storage module (213-1) that is included in the storage module (213), - process at least part of the raw data into a refined dataset, where the amount of refined dataset is at least one order of magnitude is smaller than the raw data, by executing an algorithm that: - performs a data reduction process by the raw data model to filter relevant log data regarding anomalies and to select, whereby the data volume in the refined dataset is reduced, - applies lossless data compression to the refined dataset, - correlations between different data elements in the raw determines data, to establish patterns and relationships between the data elements with with regard to identify anomalies, and store these in relational datasets alongside the refined dataset, - sending the refined dataset to the remote server (240) for external storage in an external data storage module (241) included in the external server (240) via the communication module (212).
21. A computer program product for processing log data of a or more industrial packaging or labeling machines for identifying anomalies, where the computer program product includes instructions that, when the program is executed by a computer, inciting the computer to the to carry out the steps of the method of one of the preceding claims 1-20. 1 / 2 0 Receiving logging data 1 Storing the logging data 2 Processing the logging data 3 Performing data reduction 4 Employing lossless data compression 5 Determine correlations 6 Transmitting refined dataset 7 Fig. 1 2 / 2 2 00 23 1 22 0 23 2 22 2 22 3 0 21 3 - 2 21 3 - 1 22 1 21 3 22 5 21 2 23 3 21 4 23 1 - 23 3 21 1 24 0 24 3 - 1 24 3 24 4 24 1 Fig. 2 PORT CONCERNING RESEARCH INTO THE STATE OF THE ART Patent application 2038934 1 1 classification of the subject : Investigated areas of technology : B19 / 418; G05B23 / 02; G05B19 / 042; G05B; B65B B57 / 00 PC files: Scope of the investigation: -PATENTS; AbS-NPL Fully um of the investigated conclusions: Uninvestigated conclusions: January 2025 - Relevant literature 2 Citation of literature with indication, where necessary, of relevance to gory conclusion(s) of text passages or figures of particular importance X US 2022 / 0368761 A (STRONG FORCE IOT PORTFOLIO 2016 1 – 21 LLC) November 17, 2022 * figures 1 – 4, 6 and 14; paragraphs [0185 – [0191], [0197], [0206], [0207], [0210], [0221], [0224] – [0242], [0266] – [0270], [0288], [0293], [0314], [0317] * – – – X US 2021 / 0341905 A (ROCKWELL AUTOMATION TECH INC) 1, 6, 9, 14, 16, 18 – 21 November 4, 2021 * figures 4, 7, 11, 12 and 15; paragraphs [0033], [0039] – [0053], [0063], [0074], [0108], [0109] * – – – A US 2008 / 0065705 A (FISHER ROSEMOUNT SYSTEMS INC) 1, 20 March 13, 2008 * paragraph [0055] * – – – – – Date on which the investigation was completed: The competent official: June 2025 Dr. Ir. RJ Slooter Netherlands Patent Office part of the Netherlands Enterprise Agency See explanation on the next page. direction: welding areas of engineering: defined according to International Patent Classification (IPC). Generosity of the cited literature: p state of T being of particular importance in itself: literature on theory or not published in time the technical principle underlying the invention in conjunction with other cited literature E: patent literature published on or after the filing a state of the art of particular importance date of the present application and of which the submission date or the priority date is before the something of importance belonging to category X or Y submission date of the present application being state of the art D: mentioned in the application referring to an unrecorded state of affairs e technique L: literature mentioned for other reasons literature published between priority and &: member of the same patent family; corresponding literature submission date APPENDIX Accompanying the Report on the State of the Art Survey Patent application 2038934 The appendix contains a list of patent applications or patents published elsewhere (so-called and of the same patent family), which correspond to patent specifications mentioned in the report. The statement has been compiled based on data from the computer file of the European insurance agency as of June 19, 2025. The accuracy and completeness of this statement are neither guaranteed by the European Patent Office, nor guaranteed by the Netherlands Patent Office; the data are Provided for informational purposes. In the report mentioned Date of Corresponding Date of patent specification publication of patent specifications publication US 2022368761 A1 17-11-2022 AU 2019420582 A1 02-09-2021 AU 2019420582 B2 24-10-2024 AU 2024220127 A1 17-10-2024 CA 3126601 A1 16-07-2020 CN 111435923 A 21-07-2020 CN 212305354 U 05-01-2021 EP 3909223 A1 17-11-2021 EP 3909223 A4 28-12-2022 EP 3909223 B1 21-08-2024 EP 3909223 C0 21-08-2024 EP 4440165 A2 02-10-2024 EP 4440165 A3 11-12-2024 JP 2022523626 A 26-04-2022 JP 7549843 B2 12-09-2024 JP 2024161567 A 19-11-2024 WO 2020146036 A1 16-07-2020 US 2021360070 A1 18-11-2021 US 2021367840 A1 25-11-2021 US 2021365012 A1 25-11-2021 US 2021356945 A1 18-11-2021 US 2022191280 A1 16-06-2022 US 2022191281 A1 16-06-2022 US 2022191282 A1 16-06-2022 US 2022191283 A1 16-06-2022 US 2022191284 A1 16-06-2022 US 2022191285 A1 16-06-2022 US 2021341905 A1 04-11-2021 CN 111835628 A 27-10-2020 CN 111835628 B 12-07-2022 EP 3726320 A1 21-10-2020 EP 3726320 B1 20-09-2023 EP 3726320 C0 20-09-2023 US 2020326684 A1 15-10-2020 US 11086298 B2 10-08-2021 US 11774946 B2 03-10-2023 US 2008065705 A1 13-03-2008 WO 2008033231 A2 20-03-2008 WO 2008033231 A3 26-06-2008 WRITTEN OPINION Patent application 2038934 Opening date: Priority date: October 2024 - 1 Applicant: classification of the subject : 5B19 / 418; G05B23 / 02; G05B19 / 042; Fuji Seal Europe BV 5B57 / 00 The written opinion contains an explanation of the following sections: Part I Basis of the written opinion Part II Priority Determination of novelty, inventiveness, and industrial applicability not Part III possible Part IV The application relates to more than one invention Reasoned statement regarding novelty, inventiveness and Part V industrial applicability Part VI Other cited documents Other defects Part VII Part VIII Other remarks The competent official: Dr. Ir. RJ Slooter Netherlands Patent Office part of the Netherlands Enterprise Agency Defined according to International Patent Classification (IPC). Written Opinion Patent application 2038934 Part I Basis of the written opinion The written opinion was prepared on the basis of the conclusions submitted on 31 January 2025. Part V Reasoned statement regarding novelty and inventiveness and industrial applicability Declaration Yourself Yes: conclusion(s) 1 – 19, 21 No: conclusion(s) 20 ventivity Yes: conclusion(s) - No: conclusion(s) 1 – 19, 21 Industrial applicability Yes: conclusion(s) 1 – 21 No: conclusion(s) - Literature and commentary the report concerning the state of the art study become the following publications names: 1: US 2022 / 0368761 A1 (STRONG FORCE IOT PORTFOLIO 2016 LLC) June 16, 2022 2: US 2021341905 A1 (ROCKWELL AUTOMATION TECH INC) October 3, 2023 3: US 2008 / 0065705 A1 (FISHER ROSEMOUNT SYSTEMS INC) March 13, 2008 interpretation In conclusion 3, the phrase “The computer-implemented method for processing data according to conclusion 3”. Since a conclusion cannot depend on itself, read for: “The computer-implemented method for processing log data according to conclusion 2”. In conclusion 21, the phrase “…, induce the computer to perform the steps of the procedure of to implement n of the preceding conclusions 1 – 20”. Conclusion 20, however, describes an arrangement no method. The phrase is therefore read as follows: “…, prompting the computer to the to carry out the method of one of the preceding claims 1 – 19. sections that begin with the expression “preferably”, “for example”, “such as” or “in particular” have no limiting influence on the scope of protection of the claim in which they are taken into account. Such phrases are therefore used in the assessment of novelty and inventiveness of the leaving relevant conclusions out of consideration. Written Opinion Patent application 2038934 current D1 Current D1 reveals in Figure 6 a computer-implemented method (“method 600”) for processing log data from one or more industrial machines to identify anomalies (see paragraphs [0185] – [0187]). machine, see figures 1 and 4, comprises a multitude of electronic components (“IoT nsor 102”) for measuring and controlling components within the machine, where each component generates data (“sensor data”) (see paragraphs [0187] and [0266]). method is executed by a local processing unit (“edge device 104”, see paragraph 266]). The local processing unit (“104”) is set up to be located in the vicinity of one or more to be located industrial machines and includes a communication module (“communication system 404”), a storage module (“storage system 402”) and a processing module (“processing system 406”). That local processing unit also includes an interface module, follows for an expert on the offers industrial automation directly from the fact that the electronic components (“102”) send data to the local processing unit (“104”) (see paragraphs [0188], [0224] – [0226]). The interface module is configured to interface with the multitude of electronic components. of one or more industrial machines (see paragraphs [0188], [0224] – [0226]). The communication module (“404”) is configured to communicate with an external server (“backend”) vote 150”). storage module (“402”) is configured to store data. processing module (“406”) is configured to store and execute computer program code to carry out the procedure (see paragraph [0231]), performing the following steps: the receiving, of the interface module, of log data from the multitude of electronic components of one or more industrial machines connected to the interface module ap “610”); the storage, by the storage module, of the log data received by the interface module raw data, locally in a local data storage module (“sensor data store 412”, see paragraph 226]) which is included in the storage module (“402”); the processing, by the processing module, of at least a part of the raw data into a finely tuned dataset (steps “612” and “614”). That through this processing the amount of data of the expert understands that the fine dataset is at least one order of magnitude smaller than the raw data. etc. from the fact that only anomalies are reported. Under normal circumstances, this concerns but a tiny fraction of the measurements that are performed every second, for example (see nea [0233]). To this end, the processing module (“406”) executes an algorithm comprising the following steps: performing a data reduction process by modeling the raw data to relevant to filter and select data regarding anomalies, thereby reducing the data volume in refined dataset is reduced (The expert immediately understands that the algorithm this step vat, because the possibility exists to send data only when there is an “issue”, and ...from filtering out duplicates and “sensor data that is clearly erroneous”, see paragraphs [0232] [0314]); applying lossless data compression to the refined dataset (see paragraphs [0266] – 270] and for example paragraph [0288]); Written Opinion Patent application 2038934 determining correlations between different data elements included in the raw a, to identify patterns and relationships between the data elements regarding anomalies identify and store these relational datasets alongside the refined dataset (The expert immediately realizes that such correlations are determined, because the “quick-decision AI module 424” on based on patterns and relationships the components monitors, see paragraph [0236]), where the found relationships are stored in “model data store 414”, see paragraph [0227]); and sending the refined dataset to the external server via the communication module (“404”) 50”) for external storage in an external data storage module (“storage system 502”) which is taken on the external server (step “616”). Conclusion 1 differs from what is disclosed in D1 in that the method was applied to one or industrial packaging or labeling machines. The expert in the field of industrial However, on the basis of the list of industrial machines from paragraph [0185], automation will be ensure that the method is usable without inventive effort in an industrial packaging labeling machine. Conclusion 1 is therefore new, yet not inventive compared to D1 and the general professional knowledge of expert. Conclusions 2–19, dependent on claim 1, are new on the basis of the foregoing. view of D1. further reveals that the processing module (“406”) a multitude of algorithmic modules 20, 422, 424”) includes which are configurable, whereby the modification of one or more of the It is possible for orhythmic modules to variables of the data reduction process, data- to change compression mechanism or correlation detection mechanism on site (see paragraphs [0233] – 236]). The characteristic measure of claim 2 is therefore known from D1. Conclusion 2 is therefore, depending on conclusion 1, also not inventive with respect to D1 and the General professional knowledge of the expert. D1 also reveals that the variables of one or more of the algorithmic modules can be modified as a self-learning mechanism (see paragraphs [0233] – [0236]). Conclusion 3 is therefore dependent on conclusion 1, and likewise not inventive with respect to D1. the general professional knowledge of the expert. The data reduction process disclosed in D1 is based on regression analysis to identify relevant to filter and select data regarding anomalies (see paragraph [0227]). For this reason, depending on conclusion 1, conclusion 4 is also not inventive with respect to D1 and the General professional knowledge of the expert. data compression revealed in D1 that is performed by the algorithmic modules is lossless compression technique (see, for example, paragraphs [0288] and [0293]). For this reason, depending on conclusion 1, conclusion 5 is also not inventive with respect to D1 and the General professional knowledge of the expert. Written Opinion Patent application 2038934 Regarding conclusion 6, it is known from D1 that machine learning models are trained with training datasets containing raw data belonging to various 'conditions' of the 'industrial 'mponent' is included to determine the status of an 'industrial component' (see paragraph [0229]). Trained machine learning models are stored in “model data store 414” (see paragraph 227]). The expert immediately understands that relationships between data elements must be identified. to be able to train the machine learning models, and that these relational datasets are struck alongside the refined dataset. The measure of conclusion 6 is therefore implicitly known. For this reason, conclusion 6 is not inventive with respect to D1 and the, depending on conclusion 1. General professional knowledge of the expert. processing module known from D1 does not include timing analyzer module that measures the time that a needs the product to move from one location to another within the machine, based on an sensor data. various sensors and components are mentioned in D1 (see paragraphs [0185] – [0187]), so Including a corresponding timing analyzer module is a matter for the expert ische design choice that cannot lend inventiveness to conclusion 7. Conclusion 7 is therefore not inventive with respect to D1 and the general professional knowledge of the expert. Algorithmic modules known from D1 are configured according to a data exchange- stand format (see, for example, paragraph [0235]). For this reason, conclusion 8 is not inventive with respect to D1 and the, depending on conclusion 1. General professional knowledge of the expert. further discloses that the refined dataset sent to the external server, includes data (see for example paragraphs [0241], [0242], [0267] and [0270]). For this reason, depending on conclusion 1, conclusion 9 is also not inventive with respect to D1 and the General professional knowledge of the expert. local data storage module (“412”) known from D1 stores raw data for a predetermined period defined period (for example, one year, see paragraph [0288]). For this reason, depending on conclusion 1, conclusion 10 is likewise not inventive with respect to D1. the general professional knowledge of the expert. communication module (“404”) known from D1 uses secure communication protocols (see paragraphs 186], [0197] and [0224]). For this reason, depending on conclusion 1, conclusion 11 is not considered inventive with respect to and the general professional knowledge of the expert. processing module (“406”) known from D1 uses machine learning algorithms (see paragraphs Written Opinion Patent application 2038934 233], [0236] and [0268]). For this reason, conclusion 12 is also found not to be inventive, depending on conclusion 1. insight into D1 and the general professional knowledge of the expert. Document D1 lists various industrial components that can submit log data. the paragraph [0187]). It is not stated here which industrial communication protocols are supported by the interface module used in the computer-implemented method for the processing of log data. However, an expert will immediately understand that the interface module has multiple industrial communication supports otocols to interface with various types of electronic components within industrial machine, because various industrial components are mentioned in D1 that can submit data (see paragraph [0187]). Conclusion 13 is therefore, depending on conclusion 1, not inventive with respect to D1 and the General professional knowledge of the expert. The system (“sensor kit 100”) disclosed in D1 includes a user interface. This can be taken in the local processing unit (“104”, see paragraph [0221]). For this reason, depending on conclusion 1, conclusion 14 is also not considered inventive with respect to and D1 and the general professional knowledge of the expert. refined dataset, obtained from the computer-implemented method known from D1, contains jdstamps (see paragraphs [0267] and [0270]). Conclusion 15 is therefore, depending on conclusion 1, not considered inventive with respect to and the general professional knowledge of the expert. processing module (“406”) known from D1 issues warnings to local users when anomalies are detected based on the refined dataset by means of notification module 426” acting on the basis of the refined dataset provided by module “420” paragraphs [0188] and [0237]). Therefore, depending on claim 1, conclusion 16 is also not found to be inventive with regard to insight into D1 and the general professional knowledge of the expert. The system known from D1 further includes a topological map of the system (“sensor kit work”) containing the coordinates of the electronic components (“102”) and the ale processing unit (“104”) (see paragraph [0317] and Figure 14, see also paragraphs [0225] and [0238]). is not disclosed that a domain-based routing mechanism is used to manage communication between the algorithmic modules in different domains. Applying such a domain-based routing mechanism involves only a small adjustment that the expert will make depending on the circumstances and therefore cannot Confer entity on claim 17. Conclusion 17 is therefore not considered inventive in relation to D1 and the general professional knowledge of expert. Written Opinion Patent application 2038934 Algorithmic modules known from D1 can process messages from multiple sources and the merge data before the data reduction process is performed (see paragraph [0233], see k figures 2b, 2c and 3a and paragraphs [0206], [0207] and [0210] – [0215]). The expert understands immediately noting that the various (sub)sets of sensors are also found in different industrial machines can be included. Conclusion 18 is therefore, depending on conclusion 1, not inventive with respect to D1 and the General professional knowledge of the expert. The system disclosed in D1 does not include a feedback mechanism that enables users to to provide information regarding the relevance of the detected anomalies, the feedback is therefore not uses to refine the processing algorithms. does reveal how the machine learning models are trained. For example, a machine learning del be trained on 'outcome-based data', i.e. based on data that after a completed prediction is collected (see paragraph [0227]). It is then a logical choice for the expert thereby enabling a user to provide input. Conclusion 19 is therefore not found to be inventive in relation to D1 and general professional knowledge. to the expert. Current D1 reveals in figures 1 and 4 a system (“sensor kit 100”) for processing data from one or more industrial machines for identifying anomalies. That this system will also be suitable for processing log data from packaging or heresy machines, the expert immediately understands from the fact that there are a multitude of possible is referred to as industrial machinery (see paragraphs [0185] – [0187]). The machine comprises a a variety of electronic components (“IoT sensors 102”) for measuring and controlling components within the machine (see paragraph [0187]), where each component generates log data the paragraph [0191]). The system includes the following: and local processing unit (“edge device 104”) configured to be near the one or industrial machines are located there, where the local processing unit comprises the following: and an interface module configured to interface with the multitude of electronic components of one or more industrial packaging or labeling machines (this follows for the expert directly from the fact that the electronic components (“102”) log data to the local send operating unit (“104”), see paragraphs [0188], [0224] – [0226]), and a communication module (“communication system 404”) configured to communicate with n external server (“backend system 150”), and a storage module (“storage system 402”) configured to store data, and processing module (“processing system 406”) which is configured to run computer program code strike and execute to carry out the method (see paragraph [0231]), whereby the operation module (“406”) is configured to all steps mentioned in conclusion 20 of the to execute the application (see the discussion of Conclusion 1, which includes the same steps). Conclusion 20 is therefore not new compared to what was disclosed in D1. Written Opinion Patent application 2038934 Current D1 furthermore discloses a computer program product for processing log data. on one or more industrial machines for identifying anomalies. If the expert the computer program product applies to a packaging or labeling machine, includes the mputerprogrammaproduct mutatis mutandis instructions which, when the program is conducted by a computer, prompting the computer to perform the steps of the procedure of to carry out conclusions 1–6, 8–16 and 18. Conclusion 21 is therefore not inventive with respect to D1 and the general professional knowledge of the expert. current D2 current D2 discloses a computer-implemented method for processing data from one or more industrial machines for identifying anomalies (see paragraphs 042] and [0043]) where the machine comprises a multitude of electronic components for the operating and regulating components within the machine (see paragraph [0033]), where each component generates data. The procedure is executed by a local processing unit (“smart gateway platform 402”). also figures 4, 7, 11, 12 and 15) which is arranged to be in the vicinity of one or more industrial to be located in machines. The local processing unit further includes an interface module (“interface component 404”), a communication module (see paragraphs [0049] and [0050], where it states that the processed data is sent to 'external systems'), a storage module (see paragraph [0049], where that collects “404” data) and a processing module (“data modeling component 408”). The interface module is configured to interface with the multitude of electronic components. on one or more industrial machines (see paragraph [0047]), the communication module is therewith aimed at communicating with an external server, which follows directly from the expert sending data to 'external systems' (see paragraphs [0049] and [0050]), the storage module is purpose to store data (which follows directly from paragraph [0049]), and the processing module is aimed at storing and executing computer program code to perform the method (see nea's [0049] and [0074] – [0076]). The following steps are performed for the procedure: the receiving, of the interface module, of log data from the multitude of electronic components of one or more industrial machines connected to the interface module (see apps “1504” and “1506”); the storage, by the storage module, of the log data received by the interface module raw data, locally in a local data storage module included in the storage module that directly follows from the collection of the data in step “1506”); the processing, by the processing module, of at least a part of the raw data into a refined dataset (see steps “1508” and “1512”), where the amount of refined dataset is at least is an order of magnitude smaller than the raw data. The expert understands this immediately from the given only relevant information is processed (see paragraph [0053]), which usually results in anomalies but concerns a small part of all data. To this end, the processing module executes an algorithm that comprises the following steps: