Automated method for data-based error cause analysis for at least one deviation in a container treatment process

Abstract

The invention relates to an automated method for data-based error cause analysis for at least one deviation in a container treatment process in a container treatment machine of the beverage / filling industry from a target container treatment process. The method comprises: determining at least one deviation in a container treatment process in a container treatment machine of the beverage / filling industry from a target container treatment process by analysing sensor data and / or measurement data from sensors and / or measuring devices provided in the container treatment machine; bringing the determined at least one deviation into context with a most probable error cause; outputting the most probable error cause for an error which causes the at least one deviation and an action instruction for rectifying the error; and applying the action instruction to rectify the error.

Description

[0001] Automated procedure for data-based root cause analysis for at least one deviation in a container handling process

[0002] The invention relates to an automated method for a data-based root cause analysis for at least one deviation in a container treatment process according to the independent claim.

[0003] State of the art

[0004] It is known that when a fault occurs in a container handling machine, the operator is shown individual symptoms (and their consequences) of the fault. Based on the combination of individual error messages and their temporal relationship, the operator must deduce the cause of the fault or, through a process of elimination, find and rectify it. In many cases, this requires extensive experience and a good technical understanding on the part of the operator.

[0005] Task

[0006] The object of the invention is to provide an automated method for a data-based root cause analysis for at least one deviation in a container treatment process in a container treatment machine of the beverage / bottling industry from a target container treatment process, which can enable improved operation of the container treatment machine.

[0007] Solution

[0008] The problem is solved by the automated method according to the independent claim. Further embodiments are disclosed in the dependent claims.

[0009] The automated method according to the invention for a data-based root cause analysis for at least one deviation in a container treatment process in a container treatment machine of the beverage / bottling industry from a target container treatment process comprises identifying at least one deviation in a container treatment process in a container treatment machine of the beverage / bottling industry from a target container treatment process by analyzing sensor data and / or measurement data from sensors and / or measuring devices provided in the container treatment machine, contextualizing the identified at least one deviation with a most probable root cause, outputting the most probable root cause for a fault that causes the at least one deviation, and providing instructions for correcting the fault, and applying the instructions for correcting the fault.A container handling process can include preparatory steps for filling a container, such as a bottle, filling the container with the product, and sealing the filled container. A container handling machine can then include a filling and sealing device.

[0010] A container treatment process can include a blow molding process. A container treatment machine can then include a blow molding device, for example for PET containers.

[0011] A container handling process can include a labeling process. A container handling machine can then include a labeling device, for example, a rotary labeling machine.

[0012] For example, sensor data and / or measurement data from sensors and / or measuring devices integrated into the container treatment machine can be recorded for each container undergoing the treatment process. During operation of the container treatment machine, this can mean that this occurs sequentially for many containers. The container treatment machine may have several identical treatment stations (e.g., a rotary machine) and / or frequently repeating container treatment processes may occur at a single treatment station (e.g., a packer).

[0013] The phrase "determining at least one deviation" is intended to mean that at least one deviation actually exists.

[0014] The at least one deviation in a container handling process can include a deviation from the target pressure during evacuation, from the target pressure during gas injection into the container, from the target pressure during product filling into the bottle, from the target pressure during CO2 injection into the container, from the target pressure during the sealing of the container (placing the closure or firmly connecting the closure to the container, for example by crimping), and / or from the target pressure during the removal of the filled and sealed container into a channel.

[0015] The at least one deviation in a container handling process can include a deviation from the target pressure during the filler process when the capper seals an external space with a cap, a deviation from the target pressure during the filler process when product is being filled into the container, a deviation from the target pressure during the injection of CO2 into the container, a deviation from the target pressure during the sealing of the container (placing the cap or firmly connecting the cap to the container, for example by crimping), and / or a deviation from the target pressure during the ejection of the filled and sealed container into a channel.

[0016] By displaying the most likely cause of the fault that results in at least one deviation, an operator of the container handling machine is not only confronted with symptoms resulting from the fault.

[0017] By issuing the action instruction to correct the fault, an operator of the container handling machine can receive instructions for performing one or more actions to correct the fault.

[0018] A deviation in the treatment process can be caused by a technical problem or a fault in the container treatment machine, for example, in one of its components. This could be, for example, a mechanical defect, a software error, or incorrect calibration.

[0019] The instructions may include a Standard Operating Procedure (SOP).

[0020] A standard procedure may include a documented execution plan, which may contain detailed, written instructions for performing one or more actions. It may also include a list of the materials and equipment required to perform the one or more actions, as well as detailed step-by-step instructions on how to perform each action.

[0021] Furthermore, the automated procedure may include providing one or more measurement curves when one or more faults occur on a machine of the container treatment machine, wherein the one or more measurement curves include the sensor data and / or measurement data from sensors and / or measuring devices provided in the container treatment machine.

[0022] The measurement curves can be continuously recorded during operation of the tank treatment machine. One or more measurement curves are provided when at least one deviation from the target tank treatment process occurs in the tank treatment machine, as determined by analyzing sensor data and / or measurement data from sensors and / or measuring devices integrated into the machine – for example, when a fault has occurred in the tank treatment machine. The provided measurement curves can be further analyzed and / or forwarded.

[0023] The provision of the data can involve one or more measurement curves being provided as data packets by a programmable logic controller (PLC).

[0024] An edge device can be connected to data from the container treatment machine.

[0025] The automated process can include caching and preprocessing the data in the edge device.

[0026] The automated process can involve forwarding pre-processed data from the edge device to a cloud. Protection against network outages can be achieved by having the control system run on the edge device, rather than in the cloud. This allows the connection between the control system and the tank treatment machine to be established via the machine network, rather than over the internet.

[0027] The automated process can involve calculating one or more measurement curves using a computational formula on the edge device or in the cloud. For example, the computational formula can include an algorithm.

[0028] The calculation can be performed using a rule-based system and / or a data-driven approach. For example, the rule-based system might incorporate empirical values ​​and / or historical data into an algorithm. The data-driven approach might, for instance, employ machine learning.

[0029] Based on experience and / or historical data, patterns and trends that lead to deviations can be identified. These insights can be used to optimize the handling process by considering them when issuing the most likely root cause for a defect that causes at least one deviation, and by providing instructions for correcting the defect.

[0030] The automated process can include contextualizing the identified deviation and outputting the most likely cause of the error.

[0031] The sensor data can come from two pressure sensors of a filling valve, which may be part of the container handling machine. For example, a first pressure sensor could be located inside a bottle for pressure measurement, and a second pressure sensor could be located in an external space for pressure measurement. The container handling machine may include a filling and capping device. The container handling process may include preparatory steps for filling a container, such as a bottle, filling the container with the product, and capping the filled container.

[0032] The measurement data can come from a current measuring device of a motor of a capping machine, for example, a servo capping machine, which is part of the container handling machine. For example, the container handling machine can include a filling and capping device.

[0033] The measurement data can further include data on the position, speed, and / or motor temperature of a capper in the filling and capping device. During a container handling process, the capper can change its position to pick up a cap, move it to the container, position the cap on the container, and seal it securely.

[0034] Brief character description

[0035] The accompanying figures illustrate aspects and / or embodiments of the invention for better understanding and demonstration purposes. They show:

[0036] Figure 1 shows a diagram of the pressure profile over time in a bottle and the pressure profile over time in the filler and

[0037] Figure 2 shows a diagram with the time course of the position, speed, motor current and motor temperature of the clamping device.

[0038] Detailed character description

[0039] The time series shown in Figures 1 and 2 depict a time interval for data recorded for a bottle being processed in a filling and capping machine in the beverage / bottling industry. These time series closely approximate, or can be considered as, a target container handling process.

[0040] Areas to be used for data-driven root cause analysis of at least one deviation in a container handling process within a container handling machine in the beverage / bottling industry from the target container handling process are schematically indicated by elongated outlines. Other and / or additional areas may also be provided. Depending on the type of container handling process, areas to be used for data-driven root cause analysis of at least one deviation in a container handling process within a container handling machine in the beverage / bottling industry from the target container handling process can be selected. The filling and capping device constitutes the container handling machine, and the container handling process comprises the preparatory steps for filling the bottle with product, the filling of the product, and the capping of the filled bottle.

[0041] Figure 1 shows a diagram 1 with the pressure profile 2 over time in a bottle and the pressure profile 3 over time in the filler. Two pressure sensors can be provided at each filling valve. A first pressure sensor can be located inside the bottle for pressure measurement, and a second pressure sensor can be located in an external space for pressure measurement.

[0042] First, an initial evacuation occurs, resulting in a pressure drop in the bottle in area 4. This is followed by three gas injections, during which the pressure in the bottle increases with each injection. A second evacuation then occurs, resulting in a pressure drop in the bottle in area 6. Finally, product is filled into the bottle using the filler, with the pressure curve 7 in the bottle showing a stepwise increase until the bottle is filled to the desired level.

[0043] After filling, CO2 is pushed into the bottle in area 8, resulting in a pressure increase of, for example, 0.5 bar.

[0044] To seal the bottle, a cap is first placed on the bottle using the capper in section 9, thus sealing the bottle. Then, in section 10, the cap is firmly attached to the bottle, for example by crimping. In section 11, the filled and sealed bottle is moved into a channel by releasing the pressure (relieving the pressure).

[0045] During operation of the filling and closing device for container treatment processes, the pressure profile over time (2) in the respective bottle and the pressure profile over time (3) in the filler are recorded for each treated bottle. The resulting pressure values ​​can then be compared with those from the target container treatment process in areas 4, 6, 8, 9, 10, and 11 to determine whether at least one deviation exists.

[0046] If at least one deviation is detected, the detected deviation is contextualized with its most probable cause. Furthermore, the most probable cause of the error resulting from the deviation is displayed. Additionally, corrective action instructions are provided. To realign the subsequent container handling process, in which the deviation was detected, with the target container handling process, the corrective action instructions are applied. Figure 2 shows a diagram 11 depicting the position profile 12, speed profile 13, motor current profile 14, and motor temperature profile 15 of the capper of the filling and capping device. The time period shown corresponds to that shown in Figure 1.

[0047] Next, in section 10, the closure is firmly attached to the bottle, for example by crimping. In section 11, the filled and sealed bottle is ejected into a channel by reducing the pressure (relieving the pressure).

[0048] For a data-driven root cause analysis of at least one deviation in the container handling process, sections 16 and 17 of the motor current curve 14 can be used. The motor current in section 16 indicates the change in values ​​that occurs when the closure is firmly connected to the bottle (see also section 10 in Figure 1). The motor current in section 17 indicates the change in values ​​that occurs when the filled and closed bottle is ejected into a channel due to a reduction in pressure (see also section 11 in Figure 1).

[0049] During operation of the filling and capping device for carrying out container treatment processes, the position profile 12, speed profile 13, motor current profile 14, and motor temperature profile 15 of the capper are recorded for each treated bottle. The resulting current values ​​of the motor current profile 14 can then be compared with those from the target container treatment process in areas 16 and 17 to determine whether at least one deviation exists.

[0050] If at least one deviation is detected, the system contextualizes the detected deviation with its most likely cause. Furthermore, the most likely cause of the error resulting from the deviation is displayed. Additionally, corrective action instructions are provided. To realign the subsequent container handling process with the target process, the corrective action instructions are applied.

Claims

8 Claims 1. Automated procedure for a data-based root cause analysis for at least one deviation in a container handling process in a container handling machine of the beverage / bottling industry from a target container handling process, comprising: - Determining at least one deviation in a container treatment process in a container treatment machine of the beverage / bottling industry from a target container treatment process by analyzing sensor data and / or measurement data from sensors and / or measuring devices provided in the container treatment machine, - contextualizing the identified at least one deviation with a most likely cause of the error, - Outputting the most likely cause of a fault that results in at least one deviation, and instructions for correcting the fault, and - Apply the instructions to correct the error.

2. The automated method according to claim 1, comprising providing one or more measurement curves (2, 3) when one or more faults occur on a machine of the container treatment machine, wherein the one or more measurement curves (2, 3) comprise the sensor data and / or measurement data from sensors and / or measuring devices provided in the container treatment machine.

3. The automated method according to claim 2, wherein the provision comprises that the one or more measurement curves (2, 3) are provided as data packets by a programmable logic controller.

4. The automated method according to claim 2 or 3, wherein an edge device is connected to data from the container treatment machine.

5. The automated method according to claim 4, comprising intermediate storage and preprocessing of the data in the edge device.

6. The automated method according to claim 5, comprising forwarding the pre-processed data from the edge device to a cloud.

7. The automated method according to one of claims 2 to 6, comprising calculating the one or more measurement curves (2, 3) by a calculation procedure, for example an algorithm, on the edge device or in the cloud.

8. The automated method according to claim 7, wherein the calculation is performed by a rule-based system and / or a data-based approach. 9 9. The automated method according to claim 7 or 8, comprising contextualizing the determined deviation and outputting the most probable cause of the error.

10. The automated method according to any one of claims 1 to 9, wherein the sensor data are from two pressure sensors of a filling valve encompassed by the container treatment machine, wherein, for example, a first pressure sensor is arranged in a bottle for pressure measurement and a second pressure sensor is arranged in an external space for pressure measurement, wherein, for example, the container treatment machine comprises a filling and closing device.

11. The automated method according to one of claims 1 to 10, wherein measurement data are from a current measuring device of a motor of a capper, for example a motor of a servo capper, which is encompassed by the container treatment machine, wherein, for example, the container treatment machine comprises a filling and capping device.

12. The automated method according to any one of claims 1 to 11, wherein the measurement data further comprise data on a position, a speed and / or a motor temperature of a closing device.

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