Method for determining the state of health of an industrial process - Patent Application 20070122997

Abstract

A method (100) for determining a health state (1*) of an industrial process (1), the process (1) being carried out by at least one industrial plant comprising an array of entities (2a-2f), the state of each such entity (2a-2f) being characterized by a set of entity state variables (3a-3f), the method (100) comprising: obtaining (110) values ​​and / or a time series of values ​​of the entity state variables (3a-3f); providing (120) for each entity (2a-2f) the values ​​(3a-3f) and / or the time series of values ​​(3a-3f) to a model (4a-4f) corresponding to the respective entity (2a-2f), thereby obtaining a prediction of a health state (5a-5f) of each entity (2a-2f); 1. A method (100), comprising the steps of: determining (130) propagation paths (6) of anomalies between the entities (2a-2f) based at least in part on a layout (1a) of an industrial plant performing a process (1); determining (140) significances (7a-7f) of health states (5a-5f) of the individual entities (2a-2f) to an overall health state (1*) of the process based at least in part on the propagation paths (6); and aggregating (150) the individual health states (5a-5f) of the entities (2a-2f) based at least in part on the significances (7a-7f) to obtain an overall health state (1*) of the process (1).

Description

[Technical Field]

[0001] The present invention relates to monitoring industrial processes for current or impending anomalies. [Background technology]

[0002] Dynamic industrial processes are becoming increasingly complex as they increasingly involve more and more diverse process units, and the process health of each of these units contributes to the overall process health. Different statistical analysis methods have been widely used in industrial plants to investigate different data signals that are independent of each other. However, to make a step forward in detecting overall health problems, it is important to consider the hierarchical relationships and the direction of causality.

[0003] EP3379357B1 discloses grouping technical status data from a process into signal groups according to, inter alia, the physical location of each signal within an industrial process system or correlation analysis, and then analyzing the signals from each group with a machine learning model dedicated to that group. Summary of the Invention

[0004] It is an object of the present invention to further improve the accuracy with which the state of health of an industrial process as a whole can be determined.

[0005] This object is achieved by the methods set out in the independent claims. Further advantageous embodiments are detailed in the respective dependent claims.

[0006] The present invention provides a method for determining the state of health of an industrial process. The process is performed by at least one industrial plant comprising an array of entities. These entities may have any level of granularity. For example, an entity may correspond to equipment such as a pump, a tank, a reactor vessel, a pipe, or a valve. However, an entity may also correspond to, for example, a subdivision of a process (such as "production of substance A," "production of substance B," and "production of product C from substances A and B") or a process module of a modular plant. The state of each entity is characterized by a set of entity state variables.

[0007] During the method, values ​​and / or time series of values ​​of entity state variables are obtained. For each entity, these values ​​and / or time series of values ​​are provided to a model corresponding to the respective entity, thereby obtaining a prediction of the health state of the respective entity. The model can, for example, predict at least one further variable of the entity, from which the health state of the entity can then be inferred. The model can also, for example, directly predict the health state of the entity. The health state can be in the form of a numerical score (e.g., on a scale of 0 to 1, with 1 being best), but can also be in the form of one of several available discrete classes. The classes can, for example, represent discrete grades (e.g., A to F) or binary classifications such as "healthy" or "unhealthy."

[0008] The models corresponding to the entities may be, for example, machine learning models. From a set of training examples, these models learn unknown propagations from inputs (i.e., state variables) to outputs (i.e., further variables or health states). Alternatively or in combination, simulation models may be used for entities where there is at least some a priori knowledge of the propagation from inputs to outputs. Depending on the accuracy and level of detail with which such simulation models mimic the behavior of the entities, the models may be computationally expensive. Therefore, a surrogate approximation of a simulation model may be used instead of the actual simulation model. The surrogate approximation may be, for example, a machine learning model that learns the behavior of the simulation model. Different types of models may be used for different entities.

[0009] A propagation path of the anomaly between entities involved in the process is determined based at least in part on the layout of the industrial plant.

[0010] Based at least in part on these propagation paths, the importance of the health states of the individual entities relative to the overall health state of the process is determined, and based at least in part on these importances, the individual health states are aggregated to obtain the overall health state of the process.

[0011] The inventors have found that decomposing the determination of the health state into these method steps provides an opportunity at each of these steps to consider a priori knowledge about the entities or the interactions between these entities in the overall process. Such a priori knowledge may appear in models of the entities, the propagation paths of anomalies, the importance of the health states of individual entities to the overall health state of the process, and the manner in which the individual health states are aggregated. Having multiple opportunities to consider such knowledge at different steps makes it possible to take advantage of a wider range of available knowledge. Thus, given more specific available a priori knowledge, the accuracy with which the overall health state can be determined is improved.

[0012] In particular, the method allows distinguishing qualitative causal relationships between anomalies within entities from quantitative causal relationships. Qualitative causal relationships exist in the topology of propagation paths between entities and contain information about which entities could, in principle, be involved in a chain of events that begins with one entity and escalates to a degradation, shutdown, or failure of the entire process. Quantitative causal relationships are a measure of the probability that such escalation will occur. This quantitative causal relationship may be encoded, for example, in the strength of the propagation path or in the importance of the health status of individual entities when aggregating to the overall health status of the process. Quantitative causal relationships do not necessarily depend only on the two specific entities connected by the propagation path. Rather, they may also depend on the layout of the plant as a whole. For example, the functions of some entities may be fulfilled in a plant with a certain level of redundancy. One example of this is an arrangement of parallel pumps that share the task of transporting a certain amount of fluid. If one pump fails, the remaining pumps may at least partially pick up the shortfall.

[0013] In particularly advantageous embodiments, the propagation path of the anomaly is determined based at least in part on the topology of material and / or energy flows between entities. Such flows are a primary reason why entities in industrial processes depend on each other. In particular, many chemical processes are multi-step processes where the product of one step is a necessary extract for the next step.

[0014] In a further advantageous embodiment, at least one known directional interaction path from a first entity to a second entity during normal operation of the plant can be extended to a bidirectional propagation path of anomalies between the first and second entities. This is based on experience that anomalies in a process can reverse the causal relationships between different entities. For example, the flow rate of a pump normally causes the fill level in a downstream tank, but an anomaly causes the tank to overfill and, as a result, cannot accept any more liquid from the pump. This in turn stops the pump's flow rate, as liquid is not compressible.

[0015] Therefore, in a further particularly advantageous embodiment, at least one propagation path is in particular a causal relationship between a first entity state variable of a first entity and a second entity state variable of a second entity; and / or A causal relationship between the occurrence of a first event at a first entity and the occurrence of a second event at a second entity.

[0016] One possible source of these propagation paths is a pre-existing set of interlocking rules between triggering events and the events that occur as a result of these triggering events. For example, if the pressure in a vacuum chamber is too high, certain sensitive instruments such as mass spectrometers or getter pumps may be prevented from starting to prevent damage to the instrument.

[0017] However, the propagation paths of anomalies between entities may also be determined in a data-driven manner without a priori knowledge. In a further particularly advantageous embodiment, determining at least one propagation path from a first entity to a second entity specifically comprises: calculating a statistical influence of a first time series of a first entity state variable of a first entity on a second time series of a second entity state variable of a second entity; Quantifying a strength of propagation of the anomaly between the first entity and the second entity based at least in part on the statistical effect.

[0018] For example, calculating the statistical influence may comprise calculating Granger causality and / or transfer entropy between the first and second time series. Granger causality is a statistical hypothesis test to test how useful a first time series is for predicting a second time series. Transfer entropy further focuses on the information gain between probability distributions.

[0019] In a further advantageous embodiment, determining the propagation path comprises: Verifying the feasibility of each propagation path from a set of candidate propagation paths; In response to a propagation path not being feasible, removing the propagation path from the set of candidate propagation paths.

[0020] In particular, extending directional interaction paths from a first entity to a second entity to bidirectional interaction paths between the first and second entities may reveal that the reverse direction is not feasible. For example, if material is transported from the first entity to the second entity by falling downward, it cannot fall upward again. The impossibility of such reverse interactions may even be intentionally devised as a safety feature when industrial plants are designed. Therefore, eliminating infeasible propagation paths provides a more accurate picture of the overall health state.

[0021] In a further advantageous embodiment, the importance of an entity's health state increases with the number and / or strength of propagation paths that include this entity. For example, a process module may produce a product that is used as an extract by multiple downstream process modules. In another example, if a process includes entities that reside on different physical plants connected by a pipeline, this pipeline may be a "single point of failure" for the overall process and therefore has very high importance in determining the overall health state.

[0022] For example, in a system with a pump and pipes supplying fluid to a tank, an excessively high fill level in the tank may prevent further fluid supply and cause back pressure on the pump. Thus, the pump is causally related to the high fill level of the tank. However, the pressure in the pipe is not causally related to another entity. Therefore, the health status of the pump receives a higher importance in the overall health status of the process than the health status of the pipe.

[0023] In a further advantageous embodiment, aggregating the individual health states comprises calculating a weighted sum of these individual health states, with the weights determined based on the importance of each individual health state. This results in an easily interpretable indicator of the overall health state, such that thresholds for a binary classification of the overall state, such as "healthy" or "unhealthy," can be easily derived. However, as an alternative to or in combination with a weighted sum, the aggregation of the individual health states may comprise, for example, multiplicative contributions. For example, if health is measured on a scale of 0 to 1 and an entity is a "single point of failure" to the functioning of the process as a whole, the contribution of this entity may be multiplicative. The overall health state of the process may in this case be less favorable than the health state of the "single point of failure" entity.

[0024] As previously mentioned, in particularly advantageous embodiments, the industrial process may be a chemical production process that converts one or more extractives into one or more products. At least one entity in the array of entities may be a pipe, a pump, or a tank. These are the entities that have the most numerous and strongest propagation paths for anomalies between them.

[0025] The present invention also provides a second method for determining the state of health of an industrial process. In a similar manner to the first method, the process is performed by at least one industrial plant comprising an array of entities, the state of each such entity being characterized by a set of entity state variables, and values ​​and / or time series of values ​​of the entity state variables are obtained.

[0026] In contrast to the first method, values ​​and / or time series of values ​​are now provided to a model of the industrial process as a whole. This model is implemented as a machine learning model. From the results of the model, a prediction of the health state of the industrial process as a whole is obtained. For this purpose, similar to the models corresponding to the individual entities used in the first method, a single model for the entire process can output process variables from which the health state can then be inferred or the health state can be predicted directly.

[0027] The basic idea of ​​using entity state variables to determine the overall health state is also the same as that used in the first method. However, this second method does not assume that a priori knowledge about the interactions between entities is available. For example, a single model can be trained directly in a "black box" manner on training data with values ​​and / or time series of values ​​of entity state variables as inputs and the overall health state of the process as ground truth labels. Therefore, which method is more advantageous to use depends on the amount of a priori information available in a specific situation.

[0028] In a further advantageous embodiment, the machine learning model comprises a recurrent neural network (RNN) and / or a neural transformer network, which are particularly well suited to processing time series of state variables as sequences.

[0029] The method may be computer-implemented in whole or in part. Accordingly, the present invention also relates to one or more computer programs having machine-readable instructions that, when executed on one or more computers and / or computational instances, cause the one or more computers to perform one of the above-described methods. In this context, virtualization platforms, hardware controllers, network infrastructure devices (such as switches, bridges, routers, or wireless access points), and end devices in a network (such as sensors, actuators, or other industrial field devices) capable of executing machine-readable instructions shall also be considered as computers.

[0030] Therefore, the present invention also relates to non-transitory storage media and / or downloadable products having one or more computer programs. Downloadable products are products that can be sold in online shops for immediate fulfillment by download. The present invention also provides one or more computers and / or computing instances having one or more computer programs and / or one or more non-transitory machine-readable storage media and / or downloadable products.

[0031] Regardless of which method is used to determine the health state, the determined health state can be used to provide an early warning if the process health is deteriorating. This can be utilized to take action to keep the plant safe. For example, in response to the determined health state of a process meeting a predetermined criterion (e.g., falling below a predetermined threshold), parameters characterizing the behavior of the process can be modified and / or optimized to improve the health state of the process. Such parameters can comprise, for example, setpoint values ​​for low-level controllers. In particular, in one example, one or more models used to predict the overall health state of a process can be invoked multiple times for different candidate actions to be taken on the process to determine which of these actions best improves the overall health state of the process. In this case, it would be particularly advantageous to be able to predict the overall health state much faster than real time. For example, it can be even more advantageous to use a surrogate model as an approximation of a computationally expensive simulation model.

[0032] In the following, the invention will be illustrated using figures without intending to limit the scope of the invention. [Brief explanation of the drawings]

[0033] [Figure 1] 1 is an exemplary embodiment of a method 100 for determining a state of health of an industrial process. [Figure 2] 2 is an exemplary embodiment of a method 200 for determining a state of health of an industrial process. [Figure 3] 1 is an exemplary system in which the methods 100, 200 may be applied. DETAILED DESCRIPTION OF THE INVENTION

[0034] 1 is a schematic flow chart of one embodiment of a method 100 for determining a health state 1* of an industrial process 1. The process 1 is performed by at least one industrial plant comprising an array of entities 2a-2f, the state of each such entity 2a-2f being characterized by a set of entity state variables 3a-3f.

[0035] In step 110, the values ​​and / or time series of values ​​of the entity state variables 3a-3f are obtained.

[0036] In step 120, for each entity 2a-2f, the values ​​3a-3f and / or the time series of values ​​3a-3f are provided to a model 4a-4f corresponding to the respective entity 2a-2f, such that a prediction of the health state 5a-5f of the respective entity 2a-2f is obtained.

[0037] According to block 121, a model 4a to 4f corresponding to at least one entity 2a to 2f is Machine learning models, and / or Simulation models, and / or A surrogate approximation of this simulation model may be provided.

[0038] In step 130, anomaly propagation paths 6 between the entities 2a-2f are determined based at least in part on a layout 1a of the industrial plant in which the process 1 is performed.

[0039] According to block 131, such propagation paths 6 may be determined based at least in part on the topology of material and / or energy flows between the entities 2a-2f.

[0040] According to block 132, at least one known directional interaction path from a first entity 2a-2f to a second entity 2a-2f during normal operation of the plant can be extended to a bidirectional propagation path of anomalies between the first entity and the second entity.

[0041] According to block 133, at least one propagation path 6 is specifically causal relationships between the first entity state variables 3a-3f of the first entities 2a-2f and the second entity state variables 3a-3f of the second entities 2a-2f, and / or A causal relationship between the occurrence of a first event in a first entity 2a-2f and the occurrence of a second event in a second entity 2a-2f.

[0042] According to block 134, a statistical influence of a first time series of first entity state variables 3a-3f of a first entity 2a-2f on a second time series of second entity state variables 3a-3f of a second entity 2a-2f may be calculated. In particular, according to block 134a, this may comprise calculating Granger causality and / or transfer entropy between the first and second time series. According to block 135, a strength of propagation of an anomaly between the first entity 2a-2f and the second entity 2a-2f may be quantified based at least in part on the calculated statistical influence.

[0043] According to block 136, the feasibility of each propagation path 6 from the set of candidate propagation paths 6 may be verified. According to block 137, in response to a propagation path 6 being not feasible, this propagation path 6 may be removed from the set of candidate propagation paths 6.

[0044] In step 140, based at least in part on the propagation path 6, the importance 7a-7f of the health states 5a-5f of the individual entities (2a-2f) to the overall health state (1*) of the process may be determined.

[0045] According to block 141, the importance 7a-7f of the health state 5a-5f of an entity 2a-2f may increase with the number and / or strength of the propagation paths 6 comprising this entity 2a-2f.

[0046] In step 150, the individual health states 5a-5f of the entities 2a-2f are aggregated to obtain an overall health state 1* of the process 1 based at least in part on the importance levels 7a-7f.

[0047] According to block 151, weights for each individual health state 5a-5f can be determined based on the importance 7a-7f of each individual health state 5a-5f. According to block 152, these weights can be used to calculate a weighted sum of these individual states 5a-5f.

[0048] 2 is an exemplary embodiment of a method 200 for determining a health state 1* of an industrial process 1. Similar to method 100, method 200 begins with a situation in which process 1 is performed by at least one industrial plant comprising an array of entities 2a-2f, the state of each such entity 2a-2f being characterized by a set of entity state variables 3a-3f.

[0049] In step 210, the values ​​and / or time series of values ​​of the entity state variables 3a-3f are obtained.

[0050] In step 220, the values ​​and / or time series of values ​​are provided to a model 1b of the industrial process 1. This model 1b is implemented as a machine learning model. From the results of the model, a prediction of the health state 1* of the industrial process is obtained. The model 1b may output the determined health state 1* directly or may output a variable of the process 1 that is indicative of the health state 1*.

[0051] According to block 221, the machine learning model 1b comprises a recurrent neural network (RNN) and / or a neural transformer network that is particularly well suited to processing time series and other sequences of values.

[0052] 3 shows an exemplary system in which methods 100 and / or 200 may be applied. The system comprises illustrative entities 2a-2f. Fluid is pumped by pump 2a through valve 2b into tank 2c. Tank 2c may also be filled from another source through valve 2d. Fluid from tank 2c passes through pressurized pipe 2e to another valve 2f. The pressure in pipe 2e may be measured by a pressure gauge that converts pressure p into an electric current I. The following is a summary of the claims as originally filed: [1] A method (100) for determining a health state (1*) of an industrial process (1), the industrial process (1) being carried out by at least one industrial plant comprising an array of entities (2a-2f), the state of each of the entities (2a-2f) being characterized by a set of entity state variables (3a-3f), the method (100) comprising: Obtaining (110) values ​​of said entity state variables (3a-3f) and / or time series of said values; providing (120) for each entity (2a-2f) said values ​​(3a-3f) and / or time series of said values ​​(3a-3f) to a model (4a-4f) corresponding to each said entity (2a-2f), thereby obtaining a prediction of the health state (5a-5f) of each said entity (2a-2f); determining (130) fault propagation paths (6) between the entities (2a-2f) based at least in part on a layout (1a) of the industrial plant implementing the industrial process (1); determining (140) the importance (7a-7f) of the health states (5a-5f) of the individual entities (2a-2f) to the overall health state (1*) of the industrial process based at least in part on the propagation path (6); aggregating (150) the individual health states (5a-5f) of the entities (2a-2f) based at least in part on the importance levels (7a-7f) to obtain the overall health state (1*) of the industrial process (1); A method (100) comprising the steps of: [2] The method (100) according to [1], wherein the propagation path (6) of the anomaly is determined (131) based at least in part on the topology of material and / or energy flows between the entities (2a-2f). [3] The method (100) according to [1] or [2], wherein at least one known directional interaction path from a first entity (2a-2f) to a second entity (2a-2f) during normal operation of the industrial plant is extended (132) to a bidirectional propagation path of anomalies between the first entity and the second entity. [4] At least one propagation path (6) is specifically: a causal relationship between a first entity state variable (3a-3f) of a first entity (2a-2f) and a second entity state variable (3a-3f) of a second entity (2a-2f), and / or a causal relationship between the occurrence of a first event in a first entity (2a-2f) and the occurrence of a second event in a second entity (2a-2f); (133) The method (100) according to any one of [1] to [3]. [5] Determining (130) at least one propagation path (6) from a first entity (2a-2f) to a second entity (2a-2f) specifically includes: calculating (134) the statistical influence of a first time series of first entity state variables (3a-3f) of said first entities (2a-2f) on a second time series of second entity state variables (3a-3f) of said second entities (2a-2f); Quantifying (135) a strength of propagation of an anomaly between the first entity (2a-2f) and the second entity (2a-2f) based at least in part on the statistical effect; and The method (100) according to any one of [1] to [4], comprising: [6] The method (100) according to [5], wherein calculating (134) the statistical influence specifically comprises calculating (134a) Granger causality and / or transfer entropy between the first time series and the second time series. [7] The determining (130) of the propagation path (6) specifically includes: Verifying (136) the feasibility of each propagation path (6) from a set of candidate propagation paths (6); removing (137) a propagation path (6) from the set of candidate propagation paths (6) in response to the propagation path (6) being not feasible; The method (100) according to any one of [1] to [6], comprising: [8] The method (100) according to any one of [1] to [7], wherein the importance (7a to 7f) of the health state (5a to 5f) of an entity (2a to 2f) increases (141) with the number and / or strength of propagation paths (6) comprising said entity (2a to 2f). [9] The models (4a to 4f) corresponding to at least one entity (2a to 2f) are Machine learning models, and / or Simulation models, and / or a surrogate approximation of said simulation model; (121) The method (100) according to any one of [1] to [8].

[10] The method (100) according to any one of [1] to [9], wherein aggregating (150) the individual health states (5a to 5f) comprises calculating (152) a weighted sum of the individual health states (5a to 5f), the weights of the weighted sum being determined (151) based on the importance (7a to 7f) of each individual health state (5a to 5f).

[11] The method (100) according to any one of [1] to

[10] , wherein the industrial process (1) is a chemical production process that converts one or more extractives into one or more products, and at least one entity in the array of entities is a pipe, a pump, or a tank.

[12] A method (200) for determining a state of health (1*) of an industrial process, the industrial process being carried out by at least one industrial plant comprising an array of entities (2a-2f), the state of each of the entities (2a-2f) being characterized by a set of entity state variables (3a-3f), the method (200) comprising: Obtaining (210) values ​​of said entity state variables (3a-3f) and / or time series of said values; providing (220) the value and / or the time series of values ​​to a model (1b) of the industrial process (1) to thereby obtain a prediction of the health state (1*) of the industrial process (1), wherein the model (1b) is implemented as a machine learning model; A method (200) comprising the steps of:

[13] The method (200) according to

[12] , wherein the machine learning model (1b) comprises a recurrent neural network (RNN) and / or a neural transformer network (221).

[14] A computer program comprising machine-readable instructions that, when executed by one or more computers, cause the one or more computers to perform the method (100, 200) described in any one of [1] to

[13] .

[15] A non-transitory machine-readable storage medium and / or download product having the computer program described in

[14] .

[16] One or more computers having the computer program described in

[14] and / or the non-transitory machine-readable storage medium and / or download product described in

[15] . [Explanation of symbols]

[0053] 1. Industrial processes 1*...Health status of process 1 1a…Topology of the plant running process 1 1b…Model of Process 1 2a~2f...Plant entities that execute Process 1 3a~3f...Entity state variables of entities 2a~2f 4a~4f...Models corresponding to entities 2a~2f 5a~5f...Health status of entities 2a~2f 6... Propagation path of anomalies between entities 2a to 2f 7a-7f: Importance of health status 5a-5f 100...First method for determining health status 1* 110...Get entity state variables 3a to 3f 120...Provide values ​​3a-3f to models 4a-4f 121...Use a specific type of model 130...Determine the propagation path 6 between entities 2a to 2f 131...Evaluate the topology of material and / or energy flows 132...Extend the directional path to a bidirectional propagation path 6 133...Consider specific causal relationships 134...Calculate statistical influences between time series 134a...Calculate Granger causality or transfer entropy 135...Quantifying the strength of propagation 136...Verify the feasibility of propagation path 6 137...Removing infeasible propagation paths 6 140...Determine importance 7a-7f 141...Make importance 7a to 7f dependent on propagation path 6 150...Aggregate health status 5a-5f using severity 7a-7f 151...Calculate weights based on importance 7a-7f 152...Calculate the weighted sum 200...Second method for determining health status 1* 210...Get entity state variables 3a to 3f 220...Provides values ​​for Model 1b of Process 1 221...Use RNN or Transformer Network p...pressure I…Current

Claims

1. A method (100) for determining a health state (1*) of an industrial process (1), said industrial process (1) being carried out by at least one industrial plant comprising an array of entities (2a-2f), the state of each of said entities (2a-2f) being characterized by a set of entity state variables (3a-3f), said method (100) comprising: - obtaining (110) values ​​of said entity state variables (3a-3f) and / or time series of said values; providing (120) for each entity (2a-2f) said values ​​(3a-3f) and / or time series of said values ​​(3a-3f) to a model (4a-4f) corresponding to each said entity (2a-2f) thereby obtaining a prediction of the health state (5a-5f) of each said entity (2a-2f); determining (130) fault propagation paths (6) between the entities (2a-2f) based at least in part on a layout (1a) of the industrial plant implementing the industrial process (1); determining (140) the importance (7a-7f) of the health states (5a-5f) of the individual entities (2a-2f) to the overall health state (1*) of the industrial process based at least in part on the propagation paths (6); aggregating (150) the individual health states (5a-5f) of the entities (2a-2f) based at least in part on the importance (7a-7f) to obtain the overall health state (1*) of the industrial process (1); A method (100) comprising the steps of:

2. The method (100) of claim 1, wherein the propagation path (6) of the anomaly is determined (131) based at least in part on a topology of material and / or energy flows between entities (2a-2f).

3. 3. The method (100) according to claim 1 or 2, wherein at least one known directional interaction path from a first entity (2a-2f) to a second entity (2a-2f) during normal operation of the industrial plant is extended (132) to a bidirectional propagation path of anomalies between the first entity and the second entity.

4. The at least one propagation path (6) is specifically: a causal relationship between a first entity state variable (3a-3f) of a first entity (2a-2f) and a second entity state variable (3a-3f) of a second entity (2a-2f), and / or a causal relationship between the occurrence of a first event in a first entity (2a-2f) and the occurrence of a second event in a second entity (2a-2f); The method (100) according to claim 1 or 2, comprising (133)

5. Determining (130) at least one propagation path (6) from a first entity (2a-2f) to a second entity (2a-2f) specifically comprises: calculating (134) the statistical influence of a first time series of first entity state variables (3a-3f) of said first entity (2a-2f) on a second time series of second entity state variables (3a-3f) of said second entity (2a-2f); quantifying (135) a strength of propagation of an anomaly between said first entity (2a-2f) and said second entity (2a-2f) based at least in part on said statistical effect; The method (100) of claim 1 or 2, comprising:

6. 6. The method of claim 5, wherein calculating the statistical influence comprises calculating Granger causality and / or transfer entropy between the first and second time series.

7. The determining (130) of the propagation path (6) specifically comprises: Verifying (136) the feasibility of each propagation path (6) from the set of candidate propagation paths (6); - removing (137) a propagation path (6) from the set of candidate propagation paths (6) in response to said propagation path (6) being not feasible; The method (100) of claim 1 or 2, comprising:

8. The method (100) according to claim 1 or 2, wherein the importance (7a-7f) of the health state (5a-5f) of an entity (2a-2f) increases (141) with the number and / or strength of propagation paths (6) comprising said entity (2a-2f).

9. The model (4a to 4f) corresponding to at least one entity (2a to 2f) comprises: machine learning models, and / or Simulation models, and / or a surrogate approximation of the simulation model; The method (100) according to claim 1 or 2, comprising (121)

10. 3. The method (100) of claim 1 or 2, wherein aggregating (150) the individual health states (5a-5f) comprises calculating (152) a weighted sum of the individual health states (5a-5f), the weights of the weighted sum being determined (151) based on the importance (7a-7f) of each individual health state (5a-5f).

11. 3. The method (100) of claim 1 or 2, wherein the industrial process (1) is a chemical production process that converts one or more extractives into one or more products, and at least one entity in the array of entities is a pipe, a pump, or a tank.

12. 10. A computer program comprising machine-readable instructions that, when executed by one or more computers, cause the one or more computers to perform the method (100) of claim 1.

13. A non-transitory machine-readable storage medium and / or downloadable product comprising the computer program of claim 12.

14. One or more computers comprising a computer program according to claim 12 and / or a non-transitory machine-readable storage medium and / or downloadable product according to claim 13.

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