INDUSTRIAL PLANT AND METHOD FOR PLANT OPERATION AND MONITORING

Description

[0001] The invention relates to an industrial plant comprising several production machines and to a method for plant operation and monitoring.

[0002] US 2020 / 401109 A1 deals with the processing of information related to an industrial facility that comprises several individual systems. Numerous key figures are obtained, some of which also relate to non-production facilities, such as an air conditioning system. In particular, the process described in US 2020 / 401109 A1 evaluates the energy consumption of systems.

[0003] The following article deals with the modeling of energy consumption of industrial plants: Gallachóir, Brian Ö et al.: Modelling energy consumption in a manufacturing plant using productivity KPIs, ECEEE 2009 Summer Study Act! Innovate! Deliver! Reducing energy demand sustainably, December 31, 2009, pages 1155 - 1161 This article focuses in particular on so-called Key Performance Indicators (KPIs).

[0004] EP 3 055 976 B1 discloses methods and systems for a universal wireless platform for plant monitoring. The system is computer-implemented and comprises a plurality of metrological sensing devices and a plurality of metrological interface devices. Furthermore, mobile data processing devices are included in the system known from EP 3 055 976 B1.

[0005] EP 3 644 153 A1 discloses a system for active system monitoring, which is designed to perform voltage measurements on one or more systems. The system is designed as a learning system.

[0006] CN 111950767 A discloses an analysis system that focuses on energy efficiency and accesses a database. Among other things, the analysis system processes operating and maintenance data. Real-time data acquisition is provided. The results of the energy efficiency analysis can be displayed on a screen and are intended to serve as a basis for decision-making by operating and maintenance personnel.

[0007] DE 10 2013 111 497 A1 discloses a clothing treatment device that indicates energy efficiency and is intended to enable the user to check the energy efficiency in real time. The clothing treatment device described in DE 10 2013 111 497 A1 comprises, among other things, a heat pump including a compressor, a condenser, and an evaporator. A control unit is intended to be able to calculate the energy efficiency based on the value of a physical parameter of a circulating heating medium.

[0008] EP 1 886 199 B1 describes an operating method for an evaluation device for a production machine, in particular a machine tool. Within the scope of this operating method, sensors detect actual states of the production machine during the manufacture of a product. The detected states are then compared with predefined state combinations, with the aim of determining fulfilled state combinations. Further evaluations can follow. The recording times of the states play a particularly important role here. Ultimately, the aim is to obtain statistical information about the production machine.

[0009] DE 10 2012 206 082 A1 proposes a method for assessing the condition of a facility and initiating appropriate measures. Within this method, energy and / or media data are recorded over a reference period of predefined duration to determine reference values. In a later step of the method, data are recorded over a comparison period, the duration of which may correspond to the duration of the reference period. If values ​​recorded during the comparison period deviate too significantly from the corresponding values ​​of the reference period, predefined measures can be initiated.

[0010] DE 10 2014 006 321 A1 and DE 10 2014 006 322 A1 deal with methods for analyzing the energy efficiency of a motor vehicle. In both cases, sensors are used to record conditions during vehicle operation. The data collected by the sensors is incorporated into various data sets.

[0011] The invention is based on the object of providing more advanced options for plant operation and monitoring in the industrial sector compared to the state of the art, with a particular focus on energy and media consumption as well as on the emission of climate-relevant gases.

[0012] This object is achieved according to the invention by a method for plant operation and monitoring according to claim 1. The operating and monitoring method relates to a production machine. The object is also achieved by an industrial plant comprising several production machines with the features of claim 6. The embodiments and advantages of the invention explained below in connection with the devices, i.e., a single production machine or a complete industrial plant, also apply mutatis mutandis to the operating and monitoring method, and vice versa.

[0013] The operating and monitoring procedure assumes that a production machine is initially assigned two key performance indicators, which were already determined before the start of the intended operation, i.e., production operation. These key performance indicators are an energy utilization rate and an energy performance rate. Once these key performance indicators are available, a third key performance indicator, an energy quality rate, is repeatedly determined during intended operation. At least one of the three key performance indicators has a direct and / or indirect relationship to the emission of climate-relevant gases, in particular to the emission of carbon dioxide.

[0014] Recurring determination can mean that the corresponding key figure is obtained at discrete time intervals. Continuous recording of the key figure is also possible. In both cases, an energy index is generated from all three key figures. This energy index is repeatedly compared with a limit value; if the limit value is exceeded, a signal relating to the operation of the production machine is triggered. This signal is automatically selected from a group of signals that includes at least a warning signal and a shutdown signal. Depending on the system configuration, the shutdown signal shuts down either a single production machine, a group of production machines, or the entire system (i.e., all production machines).

[0015] The operating and monitoring procedure according to the application is particularly suitable for integration into an energy management system as defined by DIN EN ISO 50001 (December 2018) and is generally applicable to any manufacturing machine or system. The procedure is particularly applicable in cases where a distinction can be made between productive operation and non-productive operation of the machine or system, generally referred to as idle operation. It is assumed that, in principle, there are possibilities for influencing energy consumption during productive operation and / or idle operation. Productive operation provides a so-called reporting period as defined by DIN EN ISO 50001 (Section A.6.4).Compared to the principles of energy management set out in DIN EN ISO 50001, the registered operating and monitoring procedure represents a refined method for increasing energy efficiency in a manufacturing facility. In particular, the operating and monitoring procedure provides control variables for improving value-added energy consumption.

[0016] The energy utilization rate is the first key figure that depends on the ratio between the power consumed during production and the power consumed during idle mode. It is assumed that power, like media consumption, is not generally reduced to zero during idle mode. Typically, power is specified in kWh. However, this is not a prerequisite for implementing the operating and monitoring procedures. If, for example, different forms of materials and power, and thus also energy, such as electrical power and chemically bound energy, are required to operate the machine or system, the power or other quantities are not necessarily simply added together. Instead, the various energy and other flows can be assigned weighting factors that take into account the different ecological impacts of the various energy or media flows.In such a case, the result, which is generally referred to as performance, can be a dimensionless key figure.

[0017] The energy efficiency ratio, the second key figure, depends on the efficiency during production. The term "efficiency" is not strictly physical but should be understood in a broader sense. In any case, the energy efficiency ratio depends on the uniformity of energy consumption during production of the machine or system in question. In this case, the key figure, like the first key figure, can also be dimensionless. Comparisons with at least one other machine that is fundamentally comparable to the production machine under consideration—i.e., a reference machine—can also be used to determine the energy efficiency ratio.

[0018] The energy quality rate, which represents the third key figure, includes a comparison between the energy consumption during the production of a reference part and the current energy consumption during normal operation, i.e., during the reporting period. The reference part can have been produced using the same machine or system used for normal operation. The energy quality rate is therefore based on a test run or past regular operation in which reference parts were produced. Alternatively, the energy quality rate can be obtained through simulation.

[0019] A combined input of simulated and measured data into the energy quality rating is also possible. In all cases, the energy quality rating is particularly useful for detecting changes, especially deteriorations, during ongoing operation promptly – ideally in real time. In individual cases, such changes can be attributed to leaks or component deterioration, for example. Compared to the first two metrics, namely the energy utilization rate and the energy performance rate, the energy quality rating can fluctuate significantly more in typical applications.

[0020] Compared to the energy quality rate, the first two indicators can typically be considered approximately constant during a given phase of normal, productive operation. Thus, in simplified terms, the energy index at any point in time during normal operation is at least roughly approximated as the sum of two indicators that remain constant during the corresponding operating phase and a third indicator, the energy quality rate, which may be subject to significant changes. The energy quality rate can also be included in the energy index, which represents a resulting indicator, with a factor other than one, i.e., a weighting factor.

[0021] The energy index is used in particular to achieve continuous, plannable and measurable improvements in the energy efficiency of the machine or system from one operating phase to the next. This improvement can be reflected in the first two key figures, i.e. the energy utilization rate and the energy performance rate. Improvements can be achieved, for example, by adjusting the parameters of idle mode, which can be designed as standby mode. The changes can affect, among other things, electrical energy consumption, media consumption and / or compressed air consumption, although in individual cases the pressure level can also play a role. The same applies in cases where the machine or system must be connected to a vacuum, i.e. a defined negative pressure must be maintained.

[0022] In general, the further the first two key figures, assumed to be constant in the first approximation, are from the critical range, the less likely it is that the energy index limit will be reached during productive operation. Therefore, there is a strong incentive to optimize the first two key figures during the planning phase. In any case, the optimization is systematized and consolidated through the recurring determination of the third key figure, the energy quality rate, during the operation of the machine or system. The energy quality rate includes values ​​obtained using sensors on the machine or system. The sensors are designed specifically to record electrical quantities. Counting devices that, for example, output the number of completed workpieces, also fall under the term "sensor technology" in this sense.In addition, the input of values ​​by the operating personnel can be provided.

[0023] Regardless of the data collection method, comparisons between the energy index and the corresponding threshold value can be incorporated into an analysis program to optimize the productive and / or non-productive operation of the production machine or the entire industrial plant. In addition to energy optimization, the analysis program can also provide information for preventive maintenance, among other things.

[0024] Depending on various possible configurations, there are six or more possible energy index levels. A low index typically represents a poor rating, and a high index represents a good rating. The individual levels can be indicated by color. Particularly good ratings, which exceed the previous highest level, can be indicated, for example, by single or multiple plus signs. The threshold value against which the determined energy index is to be compared is, in the simplest case, a constant value. Temporal changes, particularly reductions, of the threshold value are also possible.

[0025] Various further developed variants of the method provide that in the course of generating the energy index, at least one derived energy index is determined, which is selected from a group of indices comprising an analyzed energy index averaged over a time unit, a classified energy index indicating a permanent classification, and a live energy index acting as a real-time classification, whereby in the latter case, hard real-time requirements do not necessarily have to be met.

[0026] The analyzed energy index is calculated, for example, over a week or a month as a time unit, and provides weekly or monthly averages accordingly. As part of the calculation of the analyzed energy index, an automatic adjustment of energy consumption to a production task is also possible. Among other things, reduced energy consumption during periods when a machine is not operating productively can be taken into account. In particular, the analyzed energy index can be used to automatically issue a maintenance task when actual energy consumption deviates from a specified normal range.

[0027] The classified energy index is particularly suitable for assigning a permanent classification and should be viewed as a statistical value. A machine can be permanently marked according to the assigned classified energy index. Possible markings include stickers, seals, plaques, or specially designed human-machine interfaces. The inclusion of the classified energy index in the operating instructions of a machine, for example, a machine for machining or forming metallic or non-metallic workpieces, is also possible.

[0028] The Live Energy Index is the counterpart to the classified energy index. As the name suggests, this index displays live values ​​related to the current state of a machine. Common display devices on the machine in question or at any other location are also suitable for displaying the Live Energy Index.

[0029] Regardless of the number and level of detail of indices that are generated relating to energy consumption and, at least in part, to the emission of climate-relevant gases, the determination of at least one index may also include processes that are upstream or downstream of the production process under consideration, i.e. the process in the narrower sense.

[0030] Upstream processes that are linked in some way to energy consumption and emissions may, in individual cases, include employee travel and upstream transport. Intermediate products and services purchased by the operator of the production machine are also generally associated with upstream consumption and emissions. If the operator of the production machine leases mobile or stationary goods, even if there may be only an indirect connection to production, these processes are also included in the calculation of consumption and climate-relevant emissions from upstream processes. The term "upstream value chain" is also used for the entirety of upstream processes.

[0031] As for downstream processes, which are also included in the calculation of energy indices, examples include emissions generated by customers or franchisees of the production facility. Downstream emissions and consumption also arise from the transport and disposal of produced goods. Overall, the downstream processes are classified as part of a downstream value chain.

[0032] In general, it is possible to use either dimensionless or dimensioned values ​​as limit values ​​and energy indices. In the latter case, kWh can be chosen as the unit, although different forms of energy or media flows must be converted for this purpose. This may mean that the kWh values ​​are not necessarily to be understood as values ​​in a precisely defined physical unit. In such cases, the energy index can be understood as the quotient of the total consumption (in kWh) and the number of units produced, i.e., the throughput of the machine or system. By specifying individual levels, for example, 4, 10, 13, 17, and 20 kWh, different energy efficiency classes can be defined. Falling below the first level represents the best energy efficiency class, and exceeding the highest level represents the worst of the six energy efficiency classes in this case.

[0033] An industrial plant according to the application comprises a plurality of production machines configured in conjunction with a data processing system to carry out the method according to claim 1. The industrial plant typically belongs to the metalworking industry. There are no fundamental restrictions regarding the type of products manufactured.

[0034] For example, there are at least four interconnected production machines, with the centrally or decentrally implemented data processing system configured to generate the overall energy index of the industrial plant comprising all production machines. The data processing system can be spatially integrated, either fully or partially, into one of the production machines. It is also possible to arrange the data processing system in the form of a single machine or a plurality of distributed components outside the production machine.

[0035] Reporting periods over which an energy index of an individual machine and / or industrial plant is continuously determined and included in the data analysis range from a single day to several months. The determined energy index can be displayed on the machine or plant in any way, such as on a nameplate or on a monitor. Requirements for the energy efficiency classes to be met by the individual machines in the plant can be defined during the design phase of a complex plant. Compliance with these minimum requirements can then be monitored during ongoing operation using the described operating and monitoring procedures.

[0036] An embodiment of the invention is explained in more detail below with reference to a drawing. In the drawings: Fig. 1 shows a process for plant operation and monitoring in a flowchart, Fig. 2 shows a symbolic representation of an industrial plant constructed from several interlinked production machines, which is used to carry out the process according to Figure 1 Fig. 3 shows further features of the method for plant operation and monitoring, including a comparison of different production processes.

[0037] A production plant, designated overall by reference number 1, in which the product according to the application, Figure 1 illustrated operating and monitoring procedures is implemented, comprises a plurality of interlinked production machines 2, 3, 4, 5, 6, 7. In the Figure 2In the constellation outlined, machines 2, 3, 4 represent machines for processing a preliminary product, machines 5, 6 represent machines for processing an intermediate product, and machine 7 represents a machine for manufacturing a final product. A material flow through production plant 1 is designated MF.

[0038] A data processing system 8 is linked to all production machines 2, 3, 4, 5, 6, and 7. The registered operating and monitoring procedure is part of an energy management system according to DIN EN ISO 50001, version December 2018.

[0039] Accordingly, the operating and monitoring procedure contributes to the continuous improvement of the energy-related performance of production facility 1. In the present case, production facility 1 primarily processes metallic workpieces. This includes the machining of workpieces. Assembly steps are also carried out in production facility 1. The degree of automation of the individual production machines 2, 3, 4, 5, 6, and 7 can vary considerably and range from manual workstations to fully automated machines. Overall, production facility 1 consumes various types of energy and media, including electricity, gas, water, compressed air, and cooling lubricants. The operation of production facility 1 is also associated with EM emissions. These include, among other things, direct and indirect emissions of climate-relevant gases, in particular CO2 emissions.

[0040] The substances emitted during the operation of production facility 1 include gaseous emissions EM, particularly in the form of carbon dioxide, as well as emissions in other aggregate states, particularly in the form of wastewater. In all cases, these can be direct or indirect emissions. Consumption and emissions EM are allocated both to the individual production machines 2, 3, 4, 5, 6, 7 and to production facility 1 as a whole. This applies during the planning of production facility 1 and even more so during the intended operation of production facility 1. DIN EN ISO 50001 uses the term reporting period for intended operation. During intended operation, the material flow MF is geared towards the production of high-quality, saleable end products. In particular, intended operation must be distinguished from trial operation of production facility 1.

[0041] Before the start of intended operation, i.e., before the beginning of the reporting period, an energy efficiency factor (ENG) is determined in a first process step S1. This energy efficiency factor is defined as the first key figure and describes the relationship between production operation and idle operation of production facility 1. Typically, the power consumption of production facility 1, as well as of each individual production machine 2, 3, 4, 5, 6, 7, is specified in kWh, both in production operation and idle operation. In this case, it is assumed that production facility 1 is capable of manufacturing different products.

[0042] During various test operation phases, different data are used to determine the energy efficiency ENG, as shown in Figure 1illustrated by several arrows. The corresponding data streams are linked to the data processing system 8 in a manner not shown. The spatial arrangement of the data processing system 8, as well as a centralized or decentralized structure of the data processing system 8, has no fundamental influence on the operating and monitoring procedure according to Figure 1 In particular, the data processing system 8 can be partially implemented as a cloud solution. In any case, the data processing system 8 actually receives information collected at the individual production machines 2, 3, 4, 5, 6, and 7. In simple cases, this is consumption information.

[0043] In a second process step S2, which does not necessarily have a temporal connection to the first process step S1, an energy performance level ELG is determined, which is generally referred to as the second key figure. In a broader sense, the energy performance level ELG takes into account the efficiency and the uniformity of the energy consumption of the production plant 1. In accordance with the energy utilization level ENG, the energy performance level ELG is also determined not only for the entire production plant 1, but also for each individual production machine 2, 3, 4, 5, 6, 7. Like key figure 1, key figure 2 is dimensionless. To determine the second key figure, the production plant 1 or the individual production machines 2, 3, 4, 5, 6, 7 are compared with the energy level of a reference plant or machine that is as identical as possible.For this purpose, access is made to data stored in the data processing system 8 and obtained in earlier periods.

[0044] After the preliminary determination of the key performance indicators ELG and ENG, an energy quality rate (EQR) is determined as a third key performance indicator in a third process step S3. The energy quality rate (EQR) relates energy consumption to the production volume and, thanks to an analysis and evaluation system implemented by the data processing system 8, quickly responds to changes that may be due, for example, to leaks within a production machine 2, 3, 4, 5, 6, or 7. Upon commencement of intended operation, the energy quality rate (EQR) is initially set to a starting value, i.e., a default value. This starting value can be determined, in particular, through simulation.

[0045] Process step S4 begins the intended operation of production plant 1. An energy index EI is assigned to the intended operation. This energy index is dimensionless according to the individual key figures ENG, ELG, EQR and, in the simplest case, results from the addition of the three key figures ENG, ELG, EQR. Likewise, individual key figures ENG, ELG, EQR can be assigned weighting factors. Furthermore, process step S4 also obtains the energy indices AIE (an analyzed energy index), CIE (a classified energy index), and LIE (a live energy index), i.e., an energy index that relates to the current state of production machines 2, 3, 4, 5, 6, 7 and thus of the entire production plant 1 without any technically relevant delay.

[0046] Within the reporting period, the first two indicators, ENG and ELG, can be viewed as constants. However, this does not apply to the third indicator, EQR. The third indicator, EQR, therefore plays a central role in determining the characteristics of the facility monitoring. Along with the third indicator, EQR, the energy index (EI) also represents a fundamentally fluctuating value. The same applies to the further developed indices AIE, CIE, and LIE, which were derived using the energy index EI. These indices, on the one hand, offer particularly rapid response options (LIE) and, on the other hand, can be used for medium- and long-term evaluations (AIE, CIE).

[0047] In process step S5, the energy index EI is repeatedly compared with a limit value. Optionally, this also applies to at least one of the indices AIE, CIE, and LIE, in particular to the latter index LIE, which essentially represents a real-time index. Each of the indices EI, AIE, CIE, and LIE is assigned a separate limit value, provided a comparison with a limit value is provided. If the energy index EI and, if applicable, each of the indices AIE, CIE, and LIE is below the associated limit value, the process continues. Otherwise, measures are automatically initiated by the data processing system 8 in step S6. In the simplest case, this can be a warning message to the operating personnel of production system 1 or an individual production machine 2, 3, 4, 5, 6, 7. Likewise, an automatic maintenance or repair order can be issued. In extreme cases, process step S6 is linked to shutdown functions.Reasons for a shutdown could include, for example, a danger to operating personnel or unacceptable environmental impacts. In any case, the acquired data is fed back into data processing system 8 to initiate optimizations of production system 1.

[0048] The optimization, which runs with the help of algorithms stored in the data processing system 8 and which can use artificial intelligence tools, relates in particular to the first two key figures ENG, ELG. Among other things, measures to reduce non-productive times of production system 1 or individual production machines 2, 3, 4, 5, 6, 7 can be the result of the optimization. Conceivable optimization measures range from minor changes to individual production machines 2, 3, 4, 5, 6, 7 to the replacement of entire production machines 2, 3, 4, 5, 6, 7 or the type of interlinking between the individual production machines 2, 3, 4, 5, 6, 7. A change in the number of production machines 2, 3, 4, 5, 6, 7 that can be assigned to production system 1 is also possible within the scope of system optimization.

[0049] In general, an optimization process is expressed by shifting the key figures ENG and ELG towards the optimal range, so that the distance of the energy index EI from the corresponding limit value during normal operation is also increased. The optimization of the key figures ENG and ELG thus contributes significantly to increasing the operational reliability of production plant 1. The energy index EI of production plant 1 and of each individual production machine 2, 3, 4, 5, 6, and 7 can be classified on a six-level scale, with the lowest energy index EI indicating the lowest energy efficiency and the highest energy index EI indicating the highest energy efficiency. The six-level scale should not be viewed as rigid. Rather, it is conceivable that additional, higher energy efficiency classes, expressed as energy index EI, could be added in the event of future optimizations of production plant 1.

[0050] The inclusion of upstream and downstream processes in the determination of the indices EI, AIE, CIE and LIE is in Figure 3 Furthermore, Figure 3 refers to a comparison of an older production process PP1 with a newer production process PP2, assuming that both production processes PP1 and PP2, which can be carried out in industrial plant 1, produce the same end products.

[0051] Differences between the production processes PP1 and PP2 already exist in the upstream processes generally designated VP1 to VP5. The upstream processes VP1, VP2, VP3, and VP4 required for the first production process PP1 are collectively referred to as cluster C1. In the case of the processes VP2, VP3, VP4, and VP5, which precede the second production process PP2, a cluster C2 is formed. Clusters C1 and C2 thus overlap but are not identical. Each of the upstream processes VP1, ... VP5 is associated with specific emissions EM, including emissions of climate-relevant gases. Examples include emissions from upstream transport and purchased goods, as well as emissions from the generation of purchased electricity and / or purchased district heating and cooling.

[0052] All these emissions EM attributable to the upstream processes VP1, ... VP5 are included in the calculation of the indices EI, AIE, CIE, and LIE, as are the emissions EM occurring directly in the production processes PP1 and PP2. The latter emissions EM include emissions from stationary equipment in industrial plant 1 and, where applicable, also emissions from mobile equipment.

[0053] In Figure 3In the case outlined, downstream processes NP1, NP2, ... NPn continue to play a role with regard to emissions EM, whereby it is assumed here that the various production processes PP1, PP2 do not result in different processes NP1, NP2, ... NPn. Examples of emissions that occur in downstream processes NP1, ... NPn include emissions from the use of sold products manufactured in industrial plant 1 and emissions from downstream transport. For both the first production process PP1 and the second, newer production process PP2, data on all process-specific emissions EM are compiled in data processing system 8 so that comparisons, particularly with regard to the ecological footprint, can be made between the various production processes PP1, PP2 at any time. List of reference symbols

[0054] 1Industrial plant 2Production machine for processing a preliminary product 3Production machine for processing a preliminary product 4Production machine for processing a preliminary product 5Production machine for processing an intermediate product 6Production machine for processing an intermediate product 7Production machine for manufacturing a final product 8Data processing system AIE Analyzed Energy Index C1, C2 Cluster of upstream processes CIE Classified Energy Index EI Energy Index EMEmission ENG Energy Utilization Rate, first key figure ELGEnergy Performance Rate, second key figure EQR Energy Quality Rate, third key figure LIE Live Energy Index MF Material Flow NP1 ... NPn Downstream Processes PP1, PP2 Production Processes S1...S6 Process Steps VP1 ... VP5 Upstream Processes

Claims

1. A method for plant operation and monitoring, integrated into an energy management system, wherein a production machine (2, 3, 4, 5, 6, 7) is assigned two key figures (ENG, ELG) obtained before commencing its intended operation, namely a degree of energy utilisation (ENG), which as a first key figure has a dependency on the ratio between the power consumed in production operation and the power consumed in idle operation, and a degree of energy performance (ELG), which as a second key figure depends on both the efficiency in production operation and the uniformity of the energy consumption in production operation, and, during intended operation, a third key figure, namely an energy quality rate (EQR), which includes a comparison between the energy consumption during the production of a reference part manufactured by the same production machine (2, 3, 4, 5, 6, 7) and the current energy consumption, is repeatedly determined, at least one of the said key figures (ENG, ELG, EQR) also has an at least indirect relationship to the emission of climate-relevant gases, and an energy index (EI, AIE, CIE, LIE) is generated from all three key figures (ENG, ELG, EQR) and is repeatedly compared with a limit value, and if the limit value is exceeded, a signal relating to the operation of the production machine (2, 3, 4, 5, 6, 7) is triggered, which signal is automatically selected from a group of signals comprising at least a warning signal and a shutdown signal.

2. The method according to claim 1, characterised in that the mentioned energy management system is an energy management system according to DIN EN ISO 50001, version December 2018.

3. The method according to claim 1 or 2, characterised in that the determination of the energy performance level (ELG) includes a comparison with at least one other machine, i.e. a reference machine, that is fundamentally comparable to the production machine under consideration (2, 3, 4, 5, 6, 7).

4. The method according to any one of claims 1 to 3, characterised in that, in the course of generating the energy index (EI, AIE, CIE, LIE), at least one derived energy index (AIE, CIE, LIE) is determined which is selected from a group of indices comprising an analysed energy index (AIE) averaged over a unit of time, a classified energy index (CIE) indicating a permanent classification and a live energy index (LIE) intended as a real-time rating, the determination of the energy index (EI, AIE, CIE, LIE) also including upstream and downstream processes (VP1, ... VP5, NP1, ... NPn) of a production process (PP1, PP2).

5. The method according to any one of claims 1 to 4, characterised in that comparisons made between the energy index (EI, AIE, CIE, LIE) and the limit value are included in an analysis program for optimising the productive and / or non-productive operation of the production machine (2, 3, 4, 5, 6, 7).

6. An industrial plant (1) comprising a plurality of production machines (2, 3, 4, 5, 6, 7) which, in cooperation with a data processing system (8), are configured to carry out the method according to claim 1.

7. The industrial plant (1) according to claim 6, characterised in that same comprises at least four interlinked production machines (2, 3, 4, 5, 6, 7), the data processing system (8) being configured to generate at least one total energy index (EI, AIE, CIE, LIE) of the industrial plant (1).