Automatic detection of anomalies in a machine operation

Abstract

The invention relates to a method, a computer device, and a system for automatically detecting anomalies in machine operation. The machine operation in question is, in particular, that of machines for filling and packaging food and / or beverages. Anomaly detection comprises the acquisition of sensor data, the automatic categorization of this data according to operating states, and the extraction of relevant features for each category of operating states. Thresholds are determined using statistical methods to define precise operating limits. This model is monitored and adapted to react to anomalies at an early stage and to ensure operational safety. The invention provides a robust monitoring system that enables real-time monitoring of the machine system's condition and allows for early responses to deviations in operating conditions.This contributes to increased operational reliability, avoidance of downtime and optimization of maintenance processes.

Description

[0001] The invention relates to a method, a computer device and a system for automatically detecting an anomaly in machine operation, in particular for machines for filling and packaging food and / or beverages.

[0002] Monitoring machine systems and ongoing machine operation is a crucial aspect of industrial production and particularly important for maintenance. Continuously monitoring the operating status of machines is essential to detect anomalies early on that might indicate potential malfunctions or impending failures. Traditionally, machine system monitoring is achieved through periodic manual inspections and simple alarm systems that react to predefined thresholds. However, these methods are increasingly reaching their limits, especially in complex and dynamic production environments. A typical example of such an environment is a machine line used for packaging and filling food products, such as beverages.

[0003] Conventional approaches to monitoring machine systems are mostly based on timed inspections and maintenance. Technicians perform regular checks to identify, for example, visible signs of wear and tear and other anomalies. However, this approach is time-consuming and not always reliable, as many potential problems are only detected late, when they have already led to damage, inefficient operation, or even breakdowns.

[0004] Another well-known approach is the use of simple alarm systems that react to predefined thresholds. For example, temperature or vibration sensors can trigger an alarm when certain limits are exceeded. However, these systems are often unable to detect subtle anomalies that indicate gradual deterioration. Furthermore, they require manual threshold setting, which is not always optimally suited to the specific conditions and requirements of the application.

[0005] Therefore, there is a need for improved methods, computer devices and systems for the automatic and generic detection of anomalies in machine operation.

[0006] The problem is solved according to the invention by a method according to claim 1, a computer device according to claim 9, and a system according to claim 10. Embodiments and further developments are covered in the dependent claims.

[0007] One embodiment of the invention relates to a method for automatically detecting anomalies in machine operation. The machine operation can be, in particular, machines for filling and packaging food and / or beverages. The method comprises a first step in which signals from the machine are acquired over a predetermined reference period. The acquired signals are thus time-series data from the machine. This is followed by the automatic categorization of the acquired signals into a multitude of process type groups. The categorization is based on process variables. For example, the acquired variables can be automatically categorized based on the machine's operating states. This can be achieved by analyzing the operating conditions and assigning the data to corresponding categories.

[0008] Following this, characteristics can be determined for each of the process type groups. This corresponds to a so-called "feature extraction." Relevant characteristics (features) are extracted from the categorized data. These characteristics can include statistical measures such as means, variances, peaks, frequency components, etc. Subsequently, a correlation analysis of the extracted characteristics can be performed to identify relationships and dependencies between the variables / signals.

[0009] In the next step of the process, threshold values ​​are determined for each of the defined characteristics for each of the process type groups. Since the characteristics are statistically normalized, these threshold values ​​for all signals can be set automatically. This can simplify the process compared to manually setting threshold values ​​on the original signals. These threshold values ​​define the normal operating limits of the system. The resulting model can be implemented in the machine and optionally continuously monitored and retrained as needed to account for changes in system behavior.

[0010] The machine's signals are continuously monitored during operation based on the determined thresholds of specific characteristics. If a signal (or multiple signals) from the machine reaches one of these thresholds during operation, an anomaly in the machine's operation can be detected. For example, at least one anomaly can be detected if a single characteristic reaches its threshold, or if at least half of the characteristics reach their respective thresholds, or if all characteristics reach their respective thresholds, etc. This allows the sensitivity of the system to be adjusted. Anomaly detection can trigger a corresponding notification from an operator.

[0011] Further embodiments relate to a computer device and a system in which the above task is solved.

[0012] Exemplary aspects of the invention are illustrated in the drawings. They show: Fig. 1: a flowchart showing an exemplary procedure for the automatic detection of anomalies; Fig. 2: An example histogram shows threshold values ​​for anomaly monitoring; Fig. 3: an exemplary plant configuration for PET containers and adhesive packaging; Fig. 4: An exemplary plant configuration for PET containers and shrink packers; Fig. 5: an exemplary plant configuration for cans or glass bottles; and Fig. 6: An example plant configuration for cans.

[0013] In Fig. Figure 1 shows an exemplary method with which anomalies in a machine operation can be automatically and intelligently recorded, detected and reported.

[0014] In the first step, S102, signals are acquired over a reference period. For example, data from various sensors and measuring devices within the machine system can be continuously recorded during a defined reference period. These variables can include various physical quantities such as temperature, current consumption, pressure, vibrations, etc. Acquiring these sensor signals over the reference period thus yields various time series data from available sensor data. The data can, for example, be read from the sensors and stored in a database for later analysis.

[0015] The reference period can be set manually, for example. Preferably, the reference period is chosen so that the sensor data recording covers machine operation without any anomalies. This could be, for example, a normal production phase over an extended period, such as several weeks, to generate a specific amount of time-series data. A sufficiently long reference period ensures that various process dependencies are included in the sensor signals, such as different operating states, idle states, maintenance states, cleaning phases, different operators, and / or different product processing methods. In alternative embodiments, the selection of reference periods can also be dynamic. This allows the reference period to be continuously adjusted as a sliding window.

[0016] By recording / capturing sensor signals during the reference period, a large amount of time-series data is created. For example, a machine in a bottling plant can generate up to 4000 signals, which are recorded as time-series data over the reference period, such as temperature values ​​from individual stations, status data from individual modules in the machine, and so on.

[0017] These signals, or time series data, are then categorized into different process type groups in step S104. This categorization is based on process variables, which divides the recorded time series data into different operating modes and (production) process steps, for example. This can be done automatically and corresponds to classifying the signals according to different machine behaviors. Thus, the signals can be divided into different time periods, such as an ongoing operating state of the machine, idle data, etc. The categorization can be performed, for example, by analyzing the operating conditions and assigning the data to corresponding categories.

[0018] In principle, only data exhibiting stationary behavior, i.e., naturally oscillating signals, are used for anomaly detection. For example, an augmented Dickey-Fuller test (ADF) can be used to determine whether the time series data is a stationary signal or a non-stationary signal, such as counter data. Non-stationary signals / data can be discarded, as they are irrelevant for further processing.

[0019] All other, i.e., stationary signals / time series data, are categorized into groups 1 to 3 (groups 110 to 114). Groups 1 to 3 are only examples, and there may be more or fewer groups. For instance, there may be only a single group.

[0020] Optionally, preprocessing of the time series data for each group G1 to G3 can be performed in step S106. This preprocessing can include, for example, data cleaning (filling in or removing missing data) or data resampling. Furthermore, the individual groups can be filtered or categorized according to predefined criteria such as operating states / programs, speed ranges, etc. The preprocessing in S106 is, for instance, a purely data-driven procedure in which the data is processed so that each signal has a similar data structure.

[0021] In step S108, features are extracted from the various groups G1 to G3. This begins with feature extraction from the raw sensor data. This raw data, continuously acquired by the various sensors, can be divided into fixed time windows, for example. This can be part of the preprocessing in step S106. Specific statistical features are then extracted for each of these time windows. These features form the basis for the subsequent correlation analysis and model building.

[0022] This feature determination corresponds to a statistical processing of the original signal, such as the course of a standard deviation of the signal. For example, the determined features (i.e., the extracted features) can include statistical measures such as means, variances, peaks, frequency components, etc. A correlation analysis of the extracted features can then be performed to identify relationships and dependencies between the variables or the individual signals. The correlation analysis can lead to the exclusion of individual features based on statistical significance. After setting a significance level, non-significant features can, for example, be discarded.

[0023] The feature time series extracted in this way form the basis for a more in-depth analysis and monitoring of the machine system. This deeper analysis of the features also makes it possible to compare machines of similar types, as the features are more robust than the original signals. This, in turn, provides insights into how the process should be specifically configured. They offer insights into normal operating conditions and help to detect deviations early on. By identifying correlations between the features, complex relationships and potential causes of anomalies can be uncovered.

[0024] Once the feature time series for each group G1 to G3 have been created, corresponding threshold values ​​for each group can be determined in step S110. Setting or determining threshold values ​​for the monitored signals serves to reliably detect anomalies and potential malfunctions. These threshold values ​​define the operating limits within which the machine system operates under normal conditions. Various statistical methods based on statistical analyses of the measured data can be used to set the threshold values, and therefore they do not necessarily have to be set manually.

[0025] One approach according to embodiments of the invention consists of creating histograms of the acquired sensor data and analyzing the distribution of the data.

[0026] For symmetrical distributions of sensor values, such as the Gaussian distribution, threshold values ​​can be set by adding or subtracting one or more standard deviations in both directions from the mean. However, this method does not work optimally for asymmetrical distributions, such as a right-skewed signal distribution, as in the example of the one in Fig. The temperature distribution shown in Figure 2 illustrates this. Such an approach would be quite useful on the right side, while it would be less suitable on the left side, as the distribution there is flatter.

[0027] The exemplary histogram in Fig. Figure 2 shows the distribution of possible temperature measurements. The temperature values ​​could, for example, have been measured by a sensor at a specific critical point in a bottling plant. As shown in Fig. As can be seen in Figure 2, the distribution is left-skewed, meaning that the majority of measurements tend to show higher temperatures. In such a case, the mode (the most frequent value) is shifted further to the right.

[0028] To set precise thresholds even with asymmetric distributions, a kernel density estimation (KDE) can be performed. The KDE method estimates the density distribution of the data, thus providing a more accurate representation of the distribution's shape. This method enables the reliable determination of thresholds even for non-normally distributed data. Examples are provided below. Fig. Figure 2 shows two threshold values ​​that were set by the system in this way, i.e., a lower threshold 210 at 58.0°C and an upper threshold 220 at 68.2°C. These threshold values ​​are only examples.

[0029] By applying kernel density estimation, the boundaries defining the normal operating conditions of the system can be precisely determined. This technique takes into account the actual distribution of the data and enables flexible and accurate thresholding adapted to the specific properties of the measured variables.

[0030] The total number of threshold values ​​determined in this way for the various features in the individual groups thus forms a model of the machine system that can be implemented to monitor the machine and detect anomalies in a generic manner. This occurs in step S112, in which the model is applied to the corresponding machine. The implementation of the model can, for example, include a configuration file in which all threshold values ​​and grouping options are defined.

[0031] The configuration file or model can be implemented, for example, in a controller or computer device that is functionally connected to the machine. The computer device includes at least standard components such as memory, a processor, input and output units, and at least one network interface. The computer device can thus monitor the relevant signals from the machine during operation, based on the determined threshold values ​​of specific characteristics. If a signal from the machine reaches one of the threshold values ​​during operation (e.g., exceeds or falls below a certain threshold), an anomaly in the machine's operation is detected and can be reported to an operator.

[0032] The operator is notified or informed by means of a logically interpretable statement, such as "The temperature in module X is higher than normal." The operator can then take targeted action to rectify the situation before damage or malfunctions occur.

[0033] In step S114, feedback can optionally be obtained from the machine, which can be used for training the model in connection with step S112.

[0034] In some embodiments, instead of sending an anomaly message to an operator (or in addition to the message), one or more machine control parameters can be automatically adjusted to react to an anomaly. For example, the model may already contain additional measures / control structures that specify what to do when certain thresholds are reached.

[0035] Some examples in a beverage bottling plant in which the embodiments of the invention can be implemented are, for example, monitoring of a flow rate, idle monitoring for a servo drive, a chain drive for a cleaning machine, temperature monitoring for a cleaning machine, and / or vibration analysis of a blow molding machine.

[0036] In the following Fig. 3, Fig. 4, Fig. 5 to Fig. Section 6 describes various exemplary plant configurations for different bottle filling plants in which the invention, or at least parts and aspects of the invention, can be implemented. The description of the Fig. 3, Fig. 4, Fig. 5 to Fig. Section 6 is intended to provide only a general overview of machines in which generic anomaly detection can be implemented.

[0037] Basically, modern systems, such as those used in Fig. 3, Fig. 4, Fig. 5 to Fig. Figure 6 shows a variety of sensors used to continuously collect data and analyze it in real time. This data includes a wide range of measurements such as temperature, pressure, power consumption, vibrations, and more.

[0038] Fig. Figure 3 shows an example of a plant configuration 1000 for PET bottles or PET containers and adhesive packaging. As in Fig. As shown in Figure 3, the system configuration comprises 1000 different modules forming a line that dispenses finished PET containers onto pallets. Some of the modules and machines may be optional, and the invention is not limited to the exact shape and arrangement of the system configurations.

[0039] The system configuration 1000 comprises an oven 1002 for preforms, a preform sorter with a feeding machine 1004, and a blow molding machine 1008. Modules 1002, 1004, and 1008 generally form a stretch blow molding machine in which PET containers are produced and formed from a raw material. The manufactured PET containers are then transferred to a filler 1010, where the bottles are filled. The filler can optionally include a rinser. Various particles, such as dust, cardboard, or remnants of wooden pallets, can accumulate in the preforms during storage or transport. These are removed by the rinser. A capper can be installed at the end of the filler to seal the PET containers after filling.

[0040] Optionally, the system configuration 1000 can include a rotary device downstream of the filler 1010, which is used for hot filling of the PET containers. The filled PET containers are conveyed via one or more conveyor belts 1016, which can also include a buffer 1018 for intermediate loading of filled containers, to a singulator 1020 and then to a drying unit 1024, where the PET containers are dried.

[0041] After drying, the PET containers are conveyed to a labeling machine 1026. The labeling machine 1026 can be configured for various labeling techniques, such as hot melt adhesive, cold glue, self-adhesive labels, or sleeves. After printing or labeling, the PET containers are guided through a second drying unit 1028, a line distributor 1030, conveyor belts 1032, a packaging unit 1034, and a curing section to a handle applicator. In the packaging unit 1034, the PET containers are grouped into specific sizes and packaged into a container, such as a six-pack. A carrying handle is attached to the container in the handle applicator, allowing for comfortable carrying.The finished packages are then arranged accordingly by a robot 1042 for layer production and packed onto pallets by a palletizer 1044.

[0042] In the system configuration 1000, so-called format trolleys or format racks can be arranged on various modules and machines to provide quickly interchangeable format sets for short changeover times and automatic tool changes. Examples of format trolleys are the format trolley 1006 for the blow molding machine 1008, the format trolley 1012 for the filler 1010, the format trolley 1022 for the labeling machine 1026, the format trolley 1038 for the adhesive packaging production 1034, and the format trolley 1046 for the palletizer 1044.

[0043] Fig. Figure 4 shows another exemplary system configuration 1100 for PET containers and shrink wrappers. The system 1100 consists of Fig. 4 includes many of the modules and machines from plant configuration 1000. Fig. 3, however, there are some differences. The description of the modules, which are already related to Fig. 3 are described, therefore it will be used for Fig. 4 waived.

[0044] A key difference between the two example system configurations 1000 and 1100 is that the labeling machine 1126 with the labeling modules 1127 can be installed after the blow molding machine 1008 and before the filler 1008. In contrast, system configuration 1100 can include six transport lanes 1150 into which the PET containers can be inserted. Once the PET containers have inserted themselves into one of the six lanes 1150, they are conveyed into the film wrapping module 1152 and then into the shrink tunnel 1154.

[0045] Fig. Figure 5 shows an example plant configuration 1200 for cans or glass bottles. The example plant configuration 1200 from Fig. 5 again has some similarities to the plant configurations 1000 and 1100 from Fig. 3 and Fig. 4 and the description of the plant configuration is therefore limited to the differences in the plant configurations.

[0046] As in Fig. As shown in Figure 5, the exemplary system configuration can include two separate feeds. A first feed, on the left in Figure 5, is shown in Figure 5. Fig. Figure 5 shows a branch for cans or, optionally, a partial branch for reusable new bottles. The containers, i.e., cans or new bottles, are fed into the machine from a depalletizer 1302, where they are conveyed via conveyor belts to the filler 1010. A second feed, on the right in Fig. Figure 5 shows a branch of the system for reusable bottles that are fed into the system from a reusable sorting plant (not shown).

[0047] In the case that the already used reusable bottles are fed into system 1200 via the reusable bottle branch, the reusable bottles first pass through the cleaning machine or washing machine 1304. Another possible difference of the exemplary system configuration 1200 is the transfer packer 1306 after the labeling machine 1026. The transfer packer can sort the bottles or cans into a carton clip application, into crates, or both.

[0048] Fig.Figure 6 shows an example of a system configuration 1300 for cans, in which the elements already described in the other system configurations are not described again. In system configuration 1300, the cans are fed from a magazine 1402 into the depalletizer 1302. After passing through the filler and being filled, the cans are sealed by a sealing magazine 1404 and conveyed further along the system 1400 via the conveyor belts, as described above.

[0049] The optional Pasteur 1408 can be bypassed via the Bypass 1412 if it is not needed. Freshly filled products can be pasteurized in the Pasteur 1408 for preservation.

[0050] In contrast to plant configurations 1000, 1100, and 1200, exemplary plant configuration 1300 shows various tanks for corresponding consumables, such as tanks 1410 containing rinsing fluid and / or the filling product, and tanks 1406 containing belt lubricant. These tanks can also be included in the exemplary plant configurations already described above. For example, chemical products 106, which are fed from mixer 110 to the machines, can be stored in tanks 1406 and 1410.

Claims

[1] Method for automatically detecting an anomaly in a machine operation, in particular for machines for filling and packaging food and / or beverages, the method comprising: Acquisition (S102) of machine signals over a predetermined reference period, wherein the acquired signals are time series data of the machine; automatic categorization (S104) of the acquired signals into a variety of process type groups, with the categorization being based on process variables; Determine (S108) characteristics for each of the process type groups; Determine (S110) at least one threshold value for each of the specified characteristics for each of the process type groups; and Monitoring machine signals during operation based on the determined thresholds of specific characteristics; and If a signal from the machine reaches one of the threshold values ​​during operation, an anomaly in the machine operation is detected. [2] The method of claim 1, further comprising: Adjusting at least one control parameter of the machine based on the detected anomaly. [3] Method according to claim 1 or 2, further comprising: Preprocessing of the categorized signals, including: Dividing the time series data into equally sized windows, and Data cleansing. [4] Method according to any one of claims 1 to 3, wherein the automatic categorization of the detected signals into the plurality of process type groups is data-driven and further comprises: Testing the recorded time series data to determine whether the signals are stationary or not; Discarding non-stationary time series data; and Dividing the remaining time series data into process type groups based on one or more process variables, where the one or more process variables describe a current operating mode of the machine. [5] Method according to any one of claims 1 to 4, wherein determining features for each of the process type groups further comprises: Setting a statistical significance measure; Performing an automatic correlation analysis for each of the categorized time series data; Discarding categorized time series data that do not reach the specified statistical significance level. [6] Method according to any one of claims 1 to 5, wherein the determination of at least one threshold value for a feature is based on a probability distribution of the feature and includes a kernel density estimation. [7] Method according to any one of claims 1 to 6, further comprising: Generating a model based on the entirety of all determined threshold values ​​of the time series; Applying the model to the machine; and Training the generated model based on feedback from the machine. [8] Method according to any one of claims 1 to 7, wherein the machine and / or a process of the machine that is monitored for anomalies is: monitoring a flow rate; or an idle speed monitoring system for a servo drive; or a chain drive for a cleaning machine; or a temperature monitoring system for a cleaning machine; or A vibration analysis of a blow molding machine. [9] Computer device for automatically detecting an anomaly in a machine operation, in particular for machines for filling and packaging food and / or beverages, wherein the computer device comprises: a memory for storing computer code; and a processor for executing the computer code, wherein the computer device is designed to execute the method according to any one of claims 1 to 8. [10] System for automatically detecting an anomaly in a machine operation, in particular for machines for filling and packaging food and / or beverages, wherein the system comprises: at least one machine; and a computer device according to claim 9.

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