A method for generating output signals from virtual sensors of industrial machinery with reliability / trust checks.

Abstract

According to an innovative method (3000), an output signal is generated from a virtual sensor of an industrial machine, the output signal corresponding to a sequence of values of output variables of a software model, the output variables being related to the measurement or evaluation of parameters of the industrial machine, the method being executed during machine operation time, a) receiving (3400) at least one input sequence of values of at least one input variable of a software model related to at least one physical quantity of the industrial machine, b) predicting (3500) a sequence of output values of the output variables via the software model based on the at least one input sequence, c) calculating (3600) for one or more or all of the values of the predicted output sequence, corresponding flags and / or values regarding the reliability and / or statistical evaluation of the predicted values, d) based on the corresponding flags and / or values, deciding (3700) whether to discard none or one or more of the values of the predicted output sequence, and c) using (3800) only the non-discarded values of the predicted output sequence as the output signal.

Description

Technical Field

[0001] The subject matter disclosed herein relates to a method for generating an output signal from a virtual sensor of an industrial machine that accounts for inaccuracies of the virtual sensor. In particular, one or more predicted values from a predictor within the virtual sensor may be discarded when they are considered unreliable. Further, the subject matter disclosed herein relates to a system for evaluating a virtual sensor and an industrial machine.

Background Art

[0002] Virtual sensors are becoming increasingly popular in the field of industrial control, particularly in the oil and gas industry.

[0003] As is known, a virtual sensor is typically one piece of software that can provide a signal related to a physical quantity within an industrial system, such as temperature or pressure, when it is not possible or desirable to directly measure this physical quantity via a physical sensor. A virtual sensor predicts the value of a variable of interest based on the values of one or more other variables that are measured directly within or on the industrial system.

[0004] Virtual sensors are often based on artificial intelligence and may require, for example, preliminary training by machine learning.

[0005] The accuracy of a virtual sensor typically depends on its preliminary training and the data used for that training.

[0006] Ideally, the virtual sensor should be trained in conjunction with the exact same industrial machine intended to receive virtual measurements during operation. In other words, the “domain” used to train the virtual sensor should also be used for the virtual sensor’s operation or normal operation. Using different domains is already known. For example, the paper by L. Strazzera, V. Gori, and G. Veneri, titled “DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift,” published in 2021 in connection with the Conference on Neural Information Processing Systems, describes a turbomachinery application consisting of building a virtual sensor using data collected from the machine during winter time, called the so-called “source domain,” and then applying it to data collected from the same machine during daylight saving time, called the so-called “target domain.”

[0007] A paper by Hossein Shahabadi Farahani et al., titled "A Novel Method for Designing Transferable Soft Sensors and its Application," published on Cornell University's arxiv.org, discloses a domain-adversarial training method for "transferring" a "soft sensor" from one plant (corresponding to the "source domain") to another plant (corresponding to the "target domain"). According to this paper, if the "soft sensor" is properly and sufficiently "transferred" through domain-adversarial training, its output is used in the new plant, and the generated output is always considered correct.

[0008] A patent application published as U.S. Patent Application Publication No. 2021 / 110147(A1) discloses a system and method specific to image processing, particularly human detection in different scenes, i.e., different “domains.” This document teaches the use of domain adversarial learning. According to this document, if the confidence score for an image is low, i.e., if the predictions provided by the system regarding the scene are not sufficiently reliable, the system requests that a human be annotated rather than automatically annotating the image. Furthermore, the system requires annotated data that belongs to the target domain from the outset. In fact, this document discloses semi-automatic domain adaptation.

[0009] It is desirable that the "target domain" is more different from the "source domain." In particular, it is desirable to train virtual sensors on a "prototype machine" within the machine manufacturer's premises, for example, and then use the trained virtual sensors with "fleet machines," i.e., machines installed and operating within the premises of users, typically the machine manufacturer's clients. The more different the domains are, the greater the potential for inaccuracy. Therefore, the applicant has come to consider the risk of inaccuracy, particularly the risk of inaccuracy arising from the differences between the "source domain" and the "target domain." [Overview of the Initiative]

[0010] According to a first aspect, the subject matter disclosed herein relates to a computer implementation method for generating output signals from virtual sensors of an industrial machine, based on a software model having input variables relating to physical quantities of the industrial machine and output variables relating to the measurement or evaluation of parameters of the industrial machine. During machine operation time, the values ​​of the output variables are predicted, and one or more of them may be discarded based, for example, on calculated flags and / or values ​​relating to the reliability and / or confidence level of the prediction.

[0011] According to a second aspect, the subject matter disclosed herein relates to a machine learning artificial intelligence virtual sensor for industrial machinery, the output of which relates to the measurement or evaluation of parameters of the industrial machinery. The virtual sensor comprises a feature extractor, a domain classifier, a predictor, and a discarder, the output of which corresponds to the output of the virtual sensor. During training time, the extractor, classifier, and predictor are active, and the discarder is inactive. During machine operation time, the extractor, classifier, predictor, and discarder are all active.

[0012] According to a third aspect, the subject matter disclosed herein relates to a computer-implemented system for evaluating industrial machinery. This computer-implemented system comprises at least one virtual sensor of industrial machinery configured to output only predicted values ​​having a specific reliability and / or a specific confidence level. [Brief explanation of the drawing]

[0013] Many of the disclosed embodiments of the present invention and their associated advantages will be better understood by referring to the following embodiments for carrying out the invention, and a more complete understanding will be easily obtained, as they will be better understood when considered in relation to the accompanying drawings. [Figure 1] A schematic block diagram of one embodiment of an innovative virtual sensor is shown. [Figure 2] A schematic block diagram of one embodiment of an innovative evaluation system is shown. [Figure 3] A flowchart illustrating one embodiment of an innovative method for generating output signals from a virtual sensor is shown. [Modes for carrying out the invention]

[0014] A virtual sensor is typically a piece of software capable of providing values ​​for variables within an industrial system, such as temperature or pressure, that are impossible or undesirable to measure directly through physical sensors. Generally, the output of a virtual sensor may be relevant to the measurement or evaluation of parameters in industrial machinery. The values ​​initially calculated and then provided by a virtual sensor may be affected by (e.g., calculation) errors. Innovative virtual sensors can determine within themselves if the calculated value is incorrect or excessively incorrect and discard it. In the case of innovative virtual sensors, errors may stem from the fact that the virtual sensor is at least partially trained on prototype machinery and not fully trained on production machinery.

[0015] Generally, virtual sensors are first trained and then used; therefore, it should be noted that this specification uses the expression "machine operating time" to distinguish it from "training time." However, the expression "machine operating time" does not exclude the possibility of a delay (fixed or variable) (short, e.g., 1 second, or long, e.g., 1 year) between the time when some data is collected from one or more physical sensors and the time when such collected data is processed by the innovative virtual sensor to generate an output signal after training is complete. It should not be excluded that the innovative virtual sensor may have a first training period, followed by a first operating period, followed by a second training period, followed by a second operating period, and so on. Furthermore, it should not be excluded that the operating period may at least partially overlap with the training period, in which case the innovative virtual sensor learns during operation.

[0016] Background information to more easily understand this specification can be found in the paper "DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift" by L. Strazzera, V. Gori, and G. Veneri, published in 2021 in connection with the Conference on Neural Information Processing Systems, and in the paper "Domain-Adversarial Training of Neural Networks" by Y. Ganin et al., published in the Journal of Machine Learning Research in 2016.

[0017] Figure 1 shows a schematic block diagram of one embodiment of the innovative virtual sensor 100. The virtual sensor 100 includes a feature extractor 20, a predictor 40, a domain classifier 60, and a discarder 80. During the operation of the virtual sensor 100, the feature extractor 20 receives input variable values, extracts feature values ​​from the input variable values, and provides the feature values ​​to the predictor 40 and the domain classifier 60. During the operation of the virtual sensor 100, the predictor 40 makes predictions using the received feature values ​​and outputs predicted output variable values, and the domain classifier 60 receives the feature values, estimates the domain of the received input variable values ​​based on the received feature values, and calculates a confidence flag corresponding to the predicted output variable value output by the predictor 40.

[0018] In the embodiment shown in Figure 1, input variable values ​​may come from one or more physical sensors of a prototype machine installed and operating in a test environment, i.e., the "first domain" (so-called "source domain"), or from one or more physical sensors of an industrial machine installed and operating in a production environment, i.e., the "second domain" (so-called "target domain"), but not from both machines simultaneously. In fact, especially (but not exclusively) during the training period, a set of input variable values ​​may be provided to a virtual sensor grouped into so-called "batches," which favorably include data from both the "source domain" and the "target domain," even if the data from the "source domain" and the "target domain" are not actually processed simultaneously but simply during the same period. Thus, the domain classifier 60 can understand (more precisely estimate) whether the input variable values ​​received by the virtual sensor at a given time came from the "first domain" or the "second domain," and determine the "domain loss." If the data batch is used during the training period, the output from the domain classifier may be the average value derived from all the data in the batch, e.g., the average domain loss. Typically, input variable values ​​come from both the "first domain" and the "second domain" during virtual sensor training, and thereafter from only the "second domain." When data batches are used, a single batch may contain data from only the "first domain," data from only the "second domain," or data from both the "first domain" and the "second domain." As already explained, there may be a delay between the time the data is detected by the physical sensor and the time the data is processed by the virtual sensor. In general, there may be more than three domains; for example, there may be two or more prototype machines used for training.

[0019] As will be explained later, the domain classifier 60 can also calculate a confidence flag corresponding to the predicted output variable value output by the predictor 40. In other words, according to the embodiment of Figure 1, the domain classifier 60 can also understand (more precisely, estimate) whether the output variable value output by the predictor 40 at a given time is reliable, and since the output variable value generally changes from time to time, the confidence flag changes from time to time.

[0020] According to the embodiment shown in Figure 1, while the virtual sensor 100 is operating, the discarder 80 receives the predicted output variable value output from the predictor 40 and the corresponding reliability flag from the domain classifier 60, discards values ​​that are considered unreliable based on the calculation / estimation of the domain classifier 60, and outputs only reliable values.

[0021] As shown in Figure 2, the innovative virtual sensor 100 may be a component of the innovative evaluation system 1000, i.e., a computer-implemented system capable of performing evaluations on industrial machinery (or equivalently, industrial systems or industrial plants) typically in a production state. In the embodiment of Figure 2, some inputs of the conventional evaluation system 300 are coupled to some physical sensors 200 of the industrial machinery, and some other inputs of the system 300 are coupled to the output of the virtual sensor 100. The inputs of the virtual sensor 100 are coupled to some physical sensors 200 of the industrial machinery. Note that the physical sensors 200 of the industrial machinery are outside the innovative evaluation system 1000 (see the inclined dashed line).

[0022] Figure 3 shows one embodiment of an innovative method for generating output signals from a virtual sensor of an industrial machine. This method (and other embodiments of the method) is a so-called "computer implementation method." Note that the arrows in this flowchart represent only the control flow, not the data flow, and in particular, the data generated by the activity of block 3800 is not intended to be provided as input to the activity of block 3400. Flowchart 3000 in Figure 3 has a BEGIN block 3100 and several subsequent blocks. Blocks 3200 and 3300 (described in detail later) correspond to method steps performed before machine operation time, i.e., during the training time of the innovative virtual sensor, while blocks 3400, 3500, 3600, 3700 and 3800 (described in detail later) correspond to method steps performed during machine operation time, i.e., after the training time of the innovative virtual sensor. In flowchart 3000 in Figure 3, there is no TERN block, as if the innovative virtual sensor never stops operating. However, it should be understood that the innovative virtual sensor can be stopped at any time by an “external command” that may be issued by an operator and / or one piece of software. In flowchart 3000 of Figure 3, blocks 3400, 3500, 3600, 3700 and 3800 form a loop and repeat cyclically, and depending on the particular embodiment, the iteration may be performed for each new set of input variable values, for example, for any new set of values ​​provided to the input of the innovative virtual sensor at a given time, or, for example, if the virtual sensor has only one input variable, the iteration may be performed for any new value of the input variable provided to the sensor input.

[0023] According to the method of FIG. 3 (and embodiments of other methods), the input signal of the innovative virtual sensor corresponds to a sequence of values of the input variables of the software model, and the output signal of the innovative virtual sensor corresponds to a sequence of values of the output variables of the software model. Generally, there are a number of input variables, and thus a corresponding number of input signals. Generally, there may be two or more output variables, and thus two or more output signals, which means that the innovative virtual sensor can "virtually" measure two or more variables in the industrial system simultaneously. The input variables are related to the physical quantities of the industrial system, and the output variables are related to the measurement or evaluation of the parameters of the industrial system (which may be physical quantities).

[0024] The method of FIG. 3 (and embodiments of other methods) includes at least the following steps executed during machine operation time, a) receiving at least one input sequence of values of the input variables of the software model (block 3400 in FIG. 3), b) predicting an output sequence of values of the output variables through the software model based on the at least one input sequence (block 3500 in FIG. 3), c) calculating corresponding flags and / or values regarding the reliability and / or statistical evaluation of the predicted values for one or more or all of the values of the predicted output sequence (block 3600 in FIG. 3), d) based on the corresponding flags and / or values, determining whether to discard none or one or more of the values of the predicted output sequence (the number of discarded values depends on the values of the flags and / or values) (block 3700 in FIG. 3), and e) using only the non-discarded values of the predicted output sequence as the output signal (block 3800 in FIG. 3).

[0025] Step b) follows step a) after some time. Depending on the embodiment, this may be a very short time, e.g., in microseconds if the delay is only due to the processing time of the microprocessor, or a very long time, e.g., in days or weeks or months if the delay is due to the storage of data previously collected from industrial machines. In other words, the delay may depend on where the value is received from.

[0026] Note that in step b), typically, for each input value in the input sequence, there is an output value in the output sequence.

[0027] Furthermore, note that the calculation in step c) is not necessarily performed for each of the predicted output sequence values.

[0028] The software model of the method is preferably an artificial intelligence software model, particularly a machine learning artificial intelligence software model.

[0029] Step c) basically offers two possibilities, which may be alternative or even used simultaneously. According to the first possibility that is easier to implement, a prediction reliability flag is calculated for each of the predicted values in step b), for example. In particular, according to this first possibility, if the corresponding predicted value is considered reliable, the flag is "true". According to the second possibility, a statistical evaluation value is calculated for each of the predicted values in step b), for example. In particular, according to this second possibility, the value is a confidence level and corresponds to the probability that the predicted value is correct.

[0030] Regarding step e), further details are provided below. Note that the output of step b), i.e., the output before the discard in step d), may also have useful purposes, especially during the training time.

[0031] The method of FIG. 3 (and embodiments of other methods) is preferably the following preliminary step performed before the machine operation time, i.e., - This includes at least the step of training a software model on a source domain different from the target domain corresponding to the machine operating time (blocks 3200 and 3300 in Figure 3).

[0032] More advantageously, the software model is trained in both the source domain and the target domain. In Figure 3, blocks 3200 and 3300 are in parallel because, generally speaking, training with source domain data and training with target domain data can be considered to be simultaneous, and therefore completely or partially overlap. As already explained, for example, there may be a sequence of training data batches containing both source domain data and target domain data, or for example, a sequence of training data batches containing only source domain data alternating with training data batches containing only target domain data.

[0033] The above preliminary steps are as follows: -This may include a step of training a software model on data collected from prototype machines that are at least different from industrial machines (block 3200 in Figure 3) (in this case, the values ​​of input and output variables from the prototype should be used).

[0034] It should be noted that, typically, "prototype machines" include a much larger number of physical sensors than "fleet machines," for example, ten times more.

[0035] The above preliminary steps are as follows: -This may include a step to train the software model with data collected from industrial machinery (block 3300 in Figure 3) (in this case, only the values ​​of the input variables from the industrial machinery should be used).

[0036] It should be noted that training virtual sensors can be based on more prototype machine data than industrial machine data; in other words, advantageously, training virtual sensors can be done without requiring long activities using industrial machines during production. In fact, loss weighting mechanisms can be placed in place to compensate for different quantities; for example, the domain loss when training with industrial machine data may have a higher weight than the training domain loss when processing prototype machine data if the amount of training prototype machine data is greater than the amount of training prototype machine data.

[0037] According to the method in Figure 3 (and other embodiments of the method), the software model is typically implemented via a neural network, and advantageously, training is domain adversarial training of the neural network. In particular, the software model is realized through a first neural network configured to embody a feature extractor, a second neural network configured to embody a domain classifier, and a third neural network configured to embody a predictor, and training is domain adversarial training of the entire neural network, which is the set of the first, second, and third neural networks. More specifically, the first neural network (i.e., the "feature extractor") is trained in both the source and target domains, the second neural network (i.e., the "domain classifier") is trained in both the source and target domains, and the third neural network (i.e., the "predictor") is trained in the source domain only.

[0038] The calculation (of the flag and / or value) in step c) may also be performed by a domain classifier based on at least one input sequence of values, for example, to provide a domain loss flag, and preferably such calculation is performed through the same single software model used for prediction.

[0039] Software model validation can be performed without measuring the output variables on the industrial machine (corresponding to the output of virtual sensors) by considering another variable that is strictly correlated with the output variables.

[0040] The above method may be implemented through a machine learning artificial intelligence virtual sensor for industrial machinery (or equivalently, an industrial system or industrial plant), such as the sensor 100 shown in Figure 1.

[0041] Innovative virtual sensors are generally, - A feature extractor (labeled 20 in the embodiment of Figure 1) having one or more variable inputs (labeled 22 in the embodiment of Figure 1) and one or more feature outputs (labeled 24 in the embodiment of Figure 1), wherein the one or more variable inputs are configured to be coupled to one or more corresponding physical sensors of an industrial machine, and the feature extractor is configured to output feature values ​​in one or more feature outputs, A predictor (labeled 40 in the embodiment of Figure 1) having one or more feature inputs (labeled 42 in the embodiment of Figure 1) and at least one variable output (labeled 44 in the embodiment of Figure 1), wherein the feature inputs of the predictor are coupled to the feature outputs of a feature extractor, and the predictor is configured to output a sequence of variable prediction values ​​in at least one variable output, - A domain classifier (labeled 60 in the embodiment of Figure 1) having one or more feature inputs (labeled 62 in the embodiment of Figure 1) and a domain loss output (labeled 64 in the embodiment of Figure 1), wherein the feature inputs of the domain classifier are coupled to the feature outputs of a feature extractor, and the domain classifier is configured to output a sequence of domain loss flags in the domain loss output, - A discarder (labeled 80 in the embodiment of Figure 1) having a loss input (labeled 83 in the embodiment of Figure 1), wherein the loss input is coupled to the domain loss output of a domain classifier, and the discarder is configured to signal internally and / or externally a discard action (or even just the possibility of a discard action) on a variable prediction value if the corresponding domain loss value exceeds a predetermined threshold, and such signaling may be performed via an internal signal (not shown in Figure 1) of the discarder (also provided to a specific output of the discarder (not shown in Figure 1), Depending on the specific embodiment, the output of the virtual sensor (labeled 100 in the embodiment of Figure 1) may correspond to the output of the discarder (labeled 80 in the embodiment of Figure 1), or a combination of the output of the predictor (labeled 40 in the embodiment of Figure 1) and the output of the discarder (labeled 80 in the embodiment of Figure 1). The virtual sensor (labeled 100 in the embodiment shown in Figure 1) -During training time, the feature extractor, classifier, and predictor are active, while the discarder is inactive. - The system is configured such that the feature extractor, classifier, predictor, and discarder are active during the machine's operating time.

[0042] Essentially, the innovative virtual sensor assumes that if the domain classifier determines there is domain loss—that is, if it cannot correctly estimate the domain—then the output value predicted by the predictor should also be considered unreliable. If the domain classifier can also provide a specific confidence level for a domain, for example, then the corresponding confidence level (not necessarily the same) may be applicable to the output value predicted by the predictor.

[0043] Note that in the embodiment shown in Figure 1 (and other sensor embodiments), the predictor 40 and classifier 60 have specific outputs used during training. In particular, output 48 corresponds to output 44, output 68 corresponds to output 64, and output 66 is additional, which will be described below.

[0044] The sensor 100 in Figure 1 (and other sensor embodiments) includes a domain classifier 60, which also has a domain label output 66, configured to output a sequence of domain labels (i.e., domains estimated from input variable values ​​over consecutive time periods) primarily used during training.

[0045] In the embodiment shown in Figure 1 (and other sensor configurations), the feature extractor 20 may comprise a first neural network, or may be a first neural network; the domain classifier 60 may comprise a second neural network, or may be a second neural network; and the predictor 40 may comprise a third neural network, or may be a third neural network.

[0046] In the embodiment shown in Figure 1 (and other sensor embodiments), the discarder 80 is further advantageously configured to discard variable prediction values ​​in at least one variable output 44 of the predictor 40 based on the corresponding domain loss value.

[0047] In the embodiment shown in Figure 1 (and other sensor embodiments), the discarder 80 is further configured to receive variable prediction values ​​(at input 82) from at least one variable output 44 of the predictor 40 and to output variable prediction values ​​that have not been discarded (at output 84).

[0048] According to some sensor embodiments, the discarder is configured to store variable prediction values ​​that have not been discarded.

[0049] According to some sensor embodiments, the discarder is configured to store information regarding discard actions. For example, all predicted values ​​may be output and / or stored by the discarder along with their reliability flag and / or corresponding values ​​for, for example, their reliability level, so that the decision of whether and how to use the values ​​predicted by the innovative virtual sensor is left to another device or system.

[0050] As already mentioned, the innovative virtual sensor may be a component of an innovative computer-implemented system for performing evaluations on industrial machinery (or equivalently, an industrial system or industrial plant), as shown in Figure 2, for example. Generally, such an innovative evaluation system may include several virtual sensors, some or all of which are innovative virtual sensors. Generally, such an innovative evaluation system may be configured to be coupled with several physical sensors, as shown in Figure 2, for example.

[0051] An innovative computer-implemented evaluation system may be configured to run a computational analysis computer program and process data from at least one innovative virtual sensor (labeled 100 in the embodiment of Figure 2) of the industrial machine under evaluation, optionally from one or more non-innovative virtual sensors (not shown in the embodiment of Figure 2), and / or from one or more physical sensors (labeled 200 in the embodiment of Figure 2) of the industrial machine under evaluation.

[0052] Figure 2 shows a physical sensor 200 of an industrial machine having corresponding outputs 210. A first set 220 of outputs 210 is coupled to input 102 of the innovative virtual sensor 100 (corresponding to input 22 of the feature extractor 20 in Figure 1), and a second set 230 of outputs 210 is coupled to a first set of input 330 of the "conventional" evaluation system 300. Output 104 of the innovative virtual sensor 100 (which may correspond to output 84 of the discarder 80 in Figure 1) is coupled to a second set of input 320 of the "conventional" evaluation system 300. The "conventional" evaluation system 300 has an output 340 for providing evaluation results based on the data in its inputs 320 and 330.

[0053] It should be noted that evaluation systems, including innovative evaluation systems, may be designed to evaluate two or more industrial machines (or, equivalently, industrial systems or industrial plants).

Claims

1. A computer implementation method (3000) for generating output signals from virtual sensors of an industrial machine, wherein the output signals correspond to a sequence of values ​​of output variables in a software model, the output variables are related to the measurement or evaluation of parameters of the industrial machine, and the method comprises the following steps performed during machine operation time, namely: a) Step (3400) of receiving at least one input sequence of values ​​of at least one input variable of the software model, which is related to at least one physical quantity of the industrial machine, b) A step (3500) of predicting an output sequence of values ​​of the output variable via the software model based on the at least one input sequence, c) For one or more of the values ​​of the predicted output sequence, the step (3600) of calculating corresponding flags and / or values ​​with respect to the reliability and / or statistical evaluation of the predicted values, d) A step (3700) of discarding the predicted output sequence values ​​based on the corresponding flags and / or values ​​calculated in the calculation step (3600), e) a step (3800) of using only the non-discarded values ​​of the predicted output sequence as the output signal, The above method includes the following preliminary steps performed before the machine operating time, namely - A computer implementation method (3000) comprising the step (3200, 3300) of training the software model on a source domain different from the target domain corresponding to the machine operating time.

2. The aforementioned software model is implemented via a neural network (20, 40, 60), The method according to claim 1 (3000), wherein the training is domain adversarial training of the neural network (20, 40, 60).

3. The aforementioned preliminary step is, - The method according to claim 1 (3000), comprising the step (3200) of training the software model with data collected from at least one prototype machine different from the industrial machine.

4. The aforementioned preliminary step is, The method according to claim 3, further comprising the step (3300) of training the software model with respect to data collected from the industrial machine.

5. The method according to claim 1, wherein in step c), the flag and / or value is calculated via a domain classifier (60) configured to output a domain loss flag and / or value based on the at least one input sequence via the software model.

6. A machine learning artificial intelligence virtual sensor (100) for industrial machinery, wherein the output of the virtual sensor is related to the measurement or evaluation of parameters of the industrial machinery, and the virtual sensor is - A feature extractor (20) having one or more variable inputs (22) and one or more feature outputs (24), wherein the one or more variable inputs (22) are configured to be coupled to one or more corresponding physical sensors (200) of the industrial machine, and the feature extractor (20) is configured to output feature values ​​in the one or more feature outputs (24), - A domain classifier (60) having one or more feature inputs (62) and a domain loss output (64), wherein the feature inputs (62) are coupled to the feature output (24), and the domain classifier (60) is configured to output a sequence of domain loss flags in the domain loss output (64), - A predictor (40) having one or more feature inputs (42) and at least one variable output (44), wherein the feature inputs (42) are coupled to the feature output (24), and the predictor (40) is configured to output a sequence of variable prediction values ​​for at least one variable output (44), - A discarder (80) having a loss input (83), wherein the loss input (83) is coupled to the domain loss output (64), and the discarder (80) is configured to signal a discard action on a variable prediction value when the corresponding domain loss value exceeds a predetermined threshold, The output of the virtual sensor (100) corresponds to the output of the discarder (80), or a combination of the output of the predictor (40) and the output of the discarder (80). The virtual sensor (100) is - During training time, the feature extractor (20), the classifier (60), and the predictor (40) are active, and the discarder (80) is inactive. - A machine learning artificial intelligence virtual sensor (100) for industrial machinery, configured such that the feature extractor (20), the classifier (60), the predictor (40), and the discarder (80) are active during the machine's operating time.

7. The feature extractor (20) comprises a first neural network, The domain classifier (60) is equipped with a second neural network, The virtual sensor (100) according to claim 6, wherein the predictor (40) comprises a third neural network.

8. The virtual sensor according to claim 6, wherein the discarder (80) is further configured to discard variable prediction values ​​in the at least one variable output (44) based on the corresponding domain loss value.

9. The virtual sensor according to claim 8, wherein the discarder (80) is further configured to receive a variable prediction value from the at least one variable output (44) (82) and output a variable prediction value that has not been discarded (84).

10. The virtual sensor according to claim 8, wherein the discarder (80) is further configured to store variable prediction values ​​that have not been discarded.

11. The virtual sensor according to claim 10, wherein the discarder (80) is further configured to store information relating to a discard action.

12. A computer-implemented system (1000) for evaluating industrial machinery, - A computer-implemented system (1000) comprising at least one virtual sensor (100) of an industrial machine according to any one of claims 6 to 11.

13. The computer implementation system (1000) according to claim 12, wherein the system is configured to execute a computational analysis computer program and process data from at least the virtual sensor (100) of the industrial machine, and optionally data from one or more physical sensors (200) of the industrial machine.

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