Patent Description:
A process monitoring system typically operates in two steps: model development, followed by model deployment.

Typically some statistical techniques are applied to create a process model that represents a particular situation of interest during the model development.

During model deployment, incoming industrial process data relevant to the developed process model are constantly fed into the process model, which provides feedback on whether that situation of interest is present in the fed industrial process data.

One example of the development of statistical models is known from the international patent application no. <CIT> "System and method for continuous, online monitoring of a chemical plant or refinery". Another example is known from the international patent application no. <CIT> "Method and System for monitoring and controlling a multi-variable process throughout a plurality of distinct phases of the process". These and similar methods require the development of a statistical model of the situation of interest based on some (known) regimes of the process. These a-priori constructed models can then be evaluated with new incoming industrial process data to detect these situations of interest. These prior art methods require a significant amount of mathematical knowledge and background information about the process, from which the user develops the statistical models. These methods do not allow the user to define the statistical models on unseen situations and require a minimal amount of reoccurrence of the situation of interest in order to develop the statistical model from industrial process data.

The present invention relates to the use of a method, which acquires a first set of industrial process data, created by selecting a set of reference time periods and related to a single occurrence or multiple occurrences of a particular situation of interest, from one or more industrial process data sources and transforms this first set of industrial process data into a so-called "fingerprint". This fingerprint can subsequently be used in the system to detect automatically similar situations in a second set of industrial process data. The system enables feedback to a user when a similar situation of interest is detected.

In a process plant, the industrial process data originating from the data sources, such as a plurality of sensors, is continuously monitored, collected and stored together with maintenance information, control parameters, lab samples and many other types of data. These industrial process data contain information about the state of the process and form inputs for a variety of tools, such as trend viewers, alarm systems, monitoring systems, etc. The industrial process data will have data values and time stamps.

Systems that are of interest as the data sources for use in the present disclosure include, but are not limited to:.

A use of the collected industrial process data is the continuous monitoring of the process in order to automatically detect particular situations of interest, such as impeding process upsets, regime transitions, abnormal situations, etc..

In contrast to the prior art methods of developing a statistical model for a specific situation of interest of the process, the current disclosure teaches the creation of a pattern (not a model), termed a fingerprint, which is based on aggregation of the industrial process data of a selected set of variables in a selection of situations of interest.

The method of the current disclosure differs from the prior art on several counts:.

The proposed invention operates in three phases: fingerprint creation, fingerprint management, and fingerprint monitoring
During the fingerprint creation, the user selects a set of process variables and a set of time frames and the fingerprint will be built based on the aggregation of the values of the selected process variables in said selected time frames. This first phase of the fingerprint creation corresponds to the model development of the prior art approaches. In contrast to the prior art approaches, no training or learning is needed in the approach proposed.

The fingerprint management comprises the explicit activation, deactivation or deletion of the created fingerprints from the first phase. This fingerprint management allows the user to have only a selected set of the fingerprints being monitored at different times. It is possible to include this second step in the traditional approach framework, in which only certain ones of the statistical models are evaluated based on some selection criteria.

During the fingerprint monitoring, the incoming data on the process variables relevant to the fingerprint are periodically compared with the fingerprint. A distance calculation is performed comparing the incoming industrial process data with the fingerprint. The user is provided with feedback based on this comparison on whether the corresponding situation of interest is present in the incoming industrial process data. This corresponds to the model deployment of the traditional approaches.

A process monitoring system and method is disclosed. The system and method of this disclosure can be used in a process plant equipped with at least one industrial process data source, containing the industrial process data obtained from an industrial process. A data connection device is used to acquire the industrial process data, related to a set of variables and time periods selected by a user, from said industrial process data sources and said industrial process data is displayed on an output device to the user.

The data processing device is used to create a industrial process fingerprint based on said industrial process data.

At a predetermined rate, new sets of the industrial process data are obtained through the industrial process data connection device and the data processing device compares said new sets of data with the loaded fingerprint.

If a distance based on said comparison exceeds a predetermined threshold, feedback is given to the user by means of a feedback device. This enables pre-emptive action to be taken.

Still other aspects, features, and advantages of the present invention are readily apparent from the following detailed description, simply by illustrating a preferable embodiments and implementations. The present invention is also capable of other and different embodiments and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive. Additional objects and advantages of the invention will be set forth in part in the description which follows and in part will be obvious from the description, or may be learned by practice of the invention.

For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following description and the accompanying drawings, in which:.

Variable: Throughout this description, we will use the term variable to indicate process tags or other uniquely identifiable related measurement points, such as control parameters or categorized maintenance information concerning particular equipment.

Polling: We refer to the action of periodically acquiring data concerning a (set of) variable(s) from a process data source as polling. For online monitoring this is typically done each fixed time step (e.g. every minute) such that a new set of data over a specific predetermined time frame ending at the latest polling time can be evaluated.

<FIG> shows an online process monitoring system <NUM>. The online process monitoring system <NUM> of the disclosure can be used for analysis of multiple different types of industrial processes, including but not limited to chemical refineries, power plants, pharmaceutical manufacturing, car plants, and food production.

The industrial process monitoring system <NUM> comprises a process data connection device <NUM> connected to a plurality of data sources <NUM> through a local area network (LAN) <NUM>, which can be formed by an Ethernet cable connection or a Wi-Fi network using the <NUM>. xx standard, but this is not limiting of the invention. The process data connection device <NUM> enables the acquisition of ,data <NUM> and is connected to at least one of the process data sources <NUM>. Example of the data sources <NUM> include, but are not limited to a data historian system <NUM> such as a laboratory information management system (LIMS) <NUM> or process information management system (PIMS) <NUM>, a data logger <NUM> such as PLS <NUM>, computer maintenance management system (CMMS) <NUM> or IMMS <NUM>, and an object linking and embedding for process control (OPC) Server <NUM>.

The industrial process monitoring system <NUM> further comprises an input system <NUM> with a computer display and at least one device for user input allowing a user to create a process fingerprint <NUM> as will be explained later and store the process fingerprint <NUM> on a storage device <NUM>, such as a file server.

The industrial process monitoring system <NUM> further comprises a management device <NUM> managing which of the created process fingerprints are active for online monitoring.

The industrial process monitoring system <NUM> further comprises a data processing device <NUM> which loads one or more of the process fingerprint <NUM> from the storage device <NUM>, obtains the process data <NUM> from the process data connection device <NUM> and calculates the distance between the obtained process data <NUM> and the loaded fingerprint <NUM>. A feedback device <NUM>, for example a mail server, provides feedback to the user for instance by sending mails, adding context information in the data logger <NUM>, such as a CMMS <NUM>, or by sending the distance to an output device <NUM>, which can display the distance to the user.

The input system <NUM> and the output device <NUM> are connected to the data processing device <NUM> through the network and exchange information using the OSI layer <NUM> HTTP or HTTPS protocols. It will be appreciated that the input system <NUM> and the output device <NUM> might be identical.

It will be appreciated that multiple devices can be physically located on the same hardware. Further it will be appreciated that multiple input and output devices could be present in the configuration of the current invention. It will be appreciated that the described setup is one of several envisioned configurations of the invention. In another embodiment of the invention, either or all of the process data sources, data processing device, data storage device are located in a local, private or public data center (cloud).

The method of this disclosure will now be described. The method operates in three steps: fingerprint creation, fingerprint management and fingerprint monitoring.

<FIG> shows a flowchart of a method for creating a process fingerprint <NUM> of a selected situation from the process data <NUM>. The method starts at <NUM> with the user selecting a set of reference variables X={X<NUM>,. The set of selected reference variables are often variables that are important for a specific situation, such as a process upset. In the next step <NUM>, a set of equal length (l) time periods P ={[P<NUM>b, P<NUM>e],. , [Pmb, Pme]} are selected. Typically, these time periods P all contain the process data concerning the same type of situation. For instance in a non-limiting example, each time period P comprises two hours preceding a specific asset failure occurrence e.g. a pump failure. For each of the time periods [Pjb, Pje], the process data <NUM> [Xij1,. , Xijl], where Xijk represents the k'th value of variable Xi in time period [Pjb, Pje], for each variable Xi is retrieved from the corresponding process data source by means of the process data connection device <NUM> in step <NUM>.

The next step <NUM> is to align the process data <NUM> from the different time periods P. In step <NUM>, the hulls H<NUM>,. , Hn of the aligned data are created for each of the variables, which form a process fingerprint <NUM> F < H<NUM>,. A hull Hi for a variable Xi consists of {Hi1,. , Hil, Hi1,. , Hil} and is constructed as follows: <MAT> <MAT>.

The resulting process fingerprint <NUM> < H<NUM>,. , Hn > is stored on the storage device <NUM> together with some unique process fingerprint identifier.

It will be appreciated that in the special case where there is only one time period, such that P ={[P<NUM>b, P<NUM>e]}, and corresponding process data <NUM> [Xi<NUM>,. , Xi<NUM>], the hulls are simply the values of the process data and thus Hik, = Hik = Xi1k.

<FIG> shows a non-limiting example of the creation of a fingerprint. In the top axes the values of two variables X<NUM> and X<NUM> are shown for three time periods of length <NUM> units. In the bottom axes the hulls of the corresponding variables are shown. The fingerprint <NUM> F corresponding to this example consists of the tuple <H<NUM>, H<NUM>>.

The management device <NUM> keeps a list of all of the process fingerprints <NUM> created by the user. The management device <NUM> allows the possibility to activate, deactivate and delete existing ones of the process fingerprints <NUM>. During the fingerprint monitoring, only the activated process fingerprints <NUM> will be evaluated.

The list of all (active and inactivate) process fingerprint identifiers can be displayed to the user on the output device <NUM>. The user can use the input system <NUM> to select which ones of the process fingerprints <NUM> to activate, deactivate or delete. Once the process fingerprint <NUM> is deleted, its corresponding fingerprint identifier is no longer visible in the list of process fingerprints outputted to the user.

In one aspect of this method, the activation and the deactivation of the process fingerprints <NUM> can be automated, for instance based on real-time context information stored in the data logger <NUM>.

Figure <NUM> shows a non-limiting example of a potential screen design for managing the process fingerprints <NUM>. The exemplary screen design comprises an overview of all of the created process fingerprints with their corresponding fingerprint identifier <NUM> and an indication of whether they are active or deactivated by means of checkboxes <NUM>. Furthermore, there is the possibility to delete any of the process fingerprints <NUM> by means of clicking the delete icons <NUM>. Potentially extra information such as a fingerprint description <NUM> is shown as well.

During fingerprint monitoring for each of the active process fingerprints <NUM>, the following steps are undertaken:.

The OPC server <NUM> DA provides real-time data from data sources <NUM>, such as sensors. Whenever new measurements of the process data are received for all of the relevant variables making up the process fingerprint <NUM>, the distance of the process data received up until the latest relevant time frame to the process fingerprint <NUM> is calculated. Alternatively, the process data can be acquired by polling the data sources <NUM>. The actual method, which will be applied in practice, will depend on the configuration of each particular process.

<FIG> shows a flowchart of a method for detecting process fingerprints <NUM> from the process data <NUM>. In step <NUM> the process fingerprint <NUM> F is loaded from the storage device <NUM>. The process fingerprint <NUM> F consists of a tuple < H<NUM>,. , Hn > corresponding to the hulls of values variables X<NUM>,. , Xn over time periods of length l as described above. Next in step <NUM>, the new process data <NUM> over a new time period of length l of the corresponding variables X<NUM>,. , Xn is retrieved by the process data connection device <NUM>. Denote by χij the j'th value of the variable Xi in the new process data <NUM>.

In step <NUM>, a distance d is calculated between this new data and the fingerprint F. In one embodiment of the invention the distance between the new data and the fingerprint F is calculated as follows: <MAT> where δHij≤χij≤Hij is <NUM> when Hij ≤ χij ≤ Hij and <NUM> otherwise.

In step <NUM> the distance d is compared to some predefined threshold. If a threshold was passed, then feedback is given to the user by means of the feedback device <NUM> in step <NUM>.

When multiple process fingerprints F <NUM> are active the steps <NUM>, <NUM>, <NUM>, <NUM> and <NUM> are applied for all of the multiple process fingerprints F <NUM>.

<FIG> shows a non-limiting example of the distance calculation of a fingerprint <NUM> represented by the hulls on the figure and process data represented by the lines. The distance of the process data to the fingerprint according to the example distance provided above is the sum of the lengths of the two-sided arrows. This corresponds to parts of the process data that fall out of the corresponding hulls of the fingerprint <NUM>.

In an additional aspect of the method it is possible to assign positive weights to subparts of the process fingerprint <NUM> in order to penalize dissimilarities corresponding to these subparts between the fingerprint <NUM> and the new data based on these weights. For each hull Hi consisting of {Hi1,. , Hil, Hi1,. , Hil} a corresponding list of positive weights <MAT> can be provided. It is then possible to calculate the weighted distance dw between the new data and the process fingerprint <NUM> F as follows: <MAT> where again δHij≤χij≤Hij is <NUM> when Hij ≤ χij ≤ Hij and <NUM> otherwise. Note that for parts which are not assigned weight all <MAT>, and for parts which can be ignored all <MAT>.

In an alternative aspect of the method, it is possible to assign negative weights to subparts of the process fingerprint <NUM> in order to reward dissimilarities corresponding to these subparts between the process fingerprint <NUM> and the new process data <NUM> based on these weights. For each hull Hi consisting of {Hi1,. , Hil, Hi1,. , Hil} a corresponding list of negative weights <MAT> can be provided. It is then possible to calculate the weighted distance dw between the new data and the process fingerprint <NUM> F as follows: <MAT> where again δHij≤χij≤Hij is <NUM> when Hij ≤ χij ≤ Hij and <NUM> otherwise and A represents some predetermined positive value. Note that for parts, which are not assigned, weight all <MAT>, and for parts, which can be, ignored all <MAT>.

It will be appreciated that both positive and negative weights can be combined.

<FIG> shows a non-limiting clarification of the addition of weights to the process fingerprint <NUM>. In this example, the positive weights <MAT> are assigned to the corresponding part of the hull of the first variable and <MAT> to the whole hull of the second variable. The negative weights <MAT> are assigned to the corresponding part of the hull of the first variable. This particular fingerprint <NUM> would indicate that the most important part of the situation is the presence of the peak at relative times <NUM>-<NUM> together with a pretty flat trend of the second variable. Furthermore, flatness in the first variable at relative times <NUM>-<NUM> is being penalized.

In an alternative embodiment of the method, it is possible to create smoother hulls based on some smoothing parameter ρ. This reduces the risk to overfit the hull based on a limited number of periods. A smoothed hull <IMG> consists of {<IMG>} and is constructed as follows: <MAT> <MAT>.

It is then possible to calculate the smooth distance ds between the new data and the process fingerprint <NUM> F by replacing Hij with <IMG> and Hij with <IMG>: <MAT> where again <IMG> is <NUM> when <IMG> ≤ χij ≤ <IMG> and <NUM> otherwise.

It will be appreciated that this equation can be adapted similarly to those above to use both positive and negative weights.

<FIG> shows a non-limiting clarification of the assignment of different distance measures to a process fingerprint <NUM> with smoothing parameter ρ = <NUM>.

The methods described in this invention are generic in their application and not dependent on the specific distance measure used in the calculations. In additional aspects of the current method, different distances based on measures, including but not limited to, the following can also be used: dynamic time warping, Euclidean distance, other Minkowski distances, correlation, etc..

In an alternative aspect of the method different subparts of the process fingerprint <NUM> can be assigned different distance measures for calculating the dissimilarities between these subparts between the process fingerprint <NUM> and the new data.

<FIG> shows a non-limiting clarification of the assignment of distance measures to subparts of a process fingerprint <NUM>. In this example the Euclidean, Manhattan distance and DTW are assigned to the corresponding parts of the hulls of the variables.

The feedback device <NUM> will handle which actions need to be undertaken when detecting a situation of interest based on a process fingerprint <NUM>. Actions can be defined for each process fingerprint <NUM> and need not be the same for each.

Feedback actions include but are not limited to:.

As an extension to the current method, the monitoring step can be mimicked on historical process data. Instead of polling or querying the OPC server <NUM> to obtain the new process data <NUM>, the process data connection device <NUM> acquires historical data from the process data sources <NUM> starting from the earliest point for which data was available. It obtains the historical data in time frames with the same length as the corresponding fingerprint <NUM>.

One can imagine that it might be interesting for a user to discover all matches to a selected one of the fingerprints <NUM> over the entire lifetime of an industrial plant. This could be beneficial for a process engineer trying to analyze a particular recurring failure as it allows the process engineer to identify all prior occurrences without having to rely on memory or error-prone manual loggings.

As a non-limiting example, let us assume we want to detect all of the historical matches of the process fingerprint <NUM> F concerning variable X<NUM> of length l.

By means of the process data connection device <NUM>, the data processing device <NUM> will load data of X<NUM> concerning each time frame of length l present in the data sources. For each set of historical process data <NUM> the distance between it and the fingerprint F will be calculated.

In an extended embodiment, a histogram of historical detection rates for a given threshold can be generated. A histogram can be kept storing the distances for each time frame in the historical data. This provides the user with information on the expected detection rate based on the historical detection rate.

The method can be modified by means of providing an automated way to identify time periods that correspond to similar process situations, which will be used to create the process fingerprint <NUM>.

The method can be modified by means of providing a graphical user interface, such as but not limited to a trend monitor, allowing a user to select the set of reference variables and reference time periods.

The method can be modified by means of providing a graphical user interface, such as but not limited to a trend monitor, allowing a user to create the process fingerprint <NUM> by drawing the hulls and assigning process variables to those hulls.

The method can be modified by means of providing an interpolation step, which, whenever process data <NUM> is acquired by means of the process data connection device <NUM>, it is interpolated to guarantee equidistantly, sampled data.

Non-limiting examples showing use of the method will now be described.

Let us assume that the process plant experiences recurrent pump failures at a certain location in the process. The process engineer tasked with analyzing these failures has retrieved all of the times at which these failures occurred and is looking at trends of a set of potentially relevant variables in the two hours prior to each occurrence of the failure. The process engineer notices that there is similar behavior of several tags prior to each failure. It is often impossible for the process engineer to define this behavior in simple (combinations of) threshold conditions (e.g. reactor temperature is greater than> <NUM>), which could be included in traditional alarming systems. The engineer can use this idea to create the process fingerprint <NUM> based on said tags and said time frames. The process fingerprint <NUM> is created and stored.

The engineer selects to activate this process fingerprint <NUM> by means of the management device <NUM>. During monitoring of the process, the relevant measurements of the process data are fed the to data processing device <NUM>. The data processing device <NUM> process the process data and compares the process data with the activated process fingerprint <NUM> and detects similar behavior as identified by means of the fingerprint <NUM>.

Since the detection, in this example, is a warning for an upcoming pump failure, this would allow taking of preventive action in order to avoid the pump failure. In this manner, the fingerprint <NUM> can be used as a leading indicator of major failures, allowing pro-active maintenance of the pump to prevent the failure.

This process does not require any training or modeling of the prospective pump failure.

Claim 1:
A computer-implemented method for the identification of a situation of interest in an industrial process of an industrial plant comprising:
activating a set of industrial process fingerprints (<NUM>), wherein the set of industrial process fingerprints (<NUM>) comprises hulls representing bounds of a set of reference process variables (X) from a plurality of sensors monitoring the industrial process over a set of reference time periods, wherein the hulls are replaced by smoothed hulls,
wherein the reference process variables (X) are smoothed based on a smoothing parameter ρ for creating the smoothed hulls prior to creating the set of industrial process fingerprints (<NUM>), wherein the industrial process data (<NUM>) is at least one of discrete data sets, categorical data sets, ordinal data sets or continuous data sets;
loading (<NUM>) the activated set of industrial process fingerprints (<NUM>) into a processor;
acquiring (<NUM>) industrial process data (<NUM>) by a management device (<NUM>) from one or more of the plurality of sensors monitoring the industrial process;
calculating (<NUM>) a smoothed distance between the acquired industrial process data (<NUM>) and one or more of the activated set of industrial process fingerprints (<NUM>) by replacing the hulls with the smoothed hulls;
comparing (<NUM>) the calculated distance to a threshold value to determine whether a situation of interest in the industrial process of the industrial plant represented by one or more of the activated set of industrial process fingerprints (<NUM>) is present; and
initiating an action by a feedback device (<NUM>) on detection of the threshold value by the management device (<NUM>) based on the industrial process fingerprint (<NUM>).