System and method for creation and detection of process fingerprints for monitoring in a process plant

An online monitoring method and system (10) is disclosed. The system comprises a process data connection device (20) for the acquisition of a first set of process data (30) coming from a process data source (40), an input system (50) for the creation of a process fingerprint (60), a storage device (70) for storing the created process fingerprint (60), a data processing device (80) for calculating the distance between the stored process fingerprint (60) and the new sets of process data (30), a management device (75) for managing which fingerprints need to be evaluated and a feedback device (77) which handles the actions that need to be performed based on detection such as displaying the distance (90) on an output device (100).

BACKGROUND OF THE INVENTION

Field of the Invention

This invention relates to a system and method for online process monitoring based on a user-defined fingerprint for a specific situation of interest.

Brief Description of the Related Art

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 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 process data.

One example of the development of statistical models is known from the International Patent Application No. WO2009/023659A1 entitled “System and method for continuous, online monitoring of a chemical plant or refinery”. 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 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 process data.

SUMMARY OF THE INVENTION

The present invention relates to the use of a method, which acquires a first set of process data, related to a single occurrence or multiple occurrences of a particular situation of interest, from one or more process data sources and transforms this first set of 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 process data. The system enables feedback to a user when a similar situation of interest is detected.

In a process plant, the 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 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 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:Data historian systems, such as process information management systems containing sensor process data or laboratory information management systems containing sample analysis results.Event logging systems, such as a maintenance management system or a process logbook, which contain contextual information concerning the process.OPC Data Access and/or OPC Unified Architecture servers, which implement a protocol for real-time data acquisition of for instance sensor measurements.
A use of the collected 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 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:No statistical model is created for the situation of interest, so there is no training involved.There is no restriction on the properties of the data pattern representing the situation of interest: i.e. the fingerprint can span any time frame, any number of variables, and contain continuous data, discrete data or both.The fingerprints are constructed and/or generated by the user and not by a mathematical model. Therefore, no background knowledge on statistical or other modeling techniques is necessary.The fingerprints are defined on the process data, as the process data is stored in the process data sources, so there is no need to preprocess the data.It is possible to add discrete/continuous levels of importance to selected subparts of the fingerprint, so the entire fingerprint does not need to be treated like a single entity block.The fingerprint can be created by combining multiple sources of process data and is not limited to only sensor measurement data.

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 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 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 process data source, containing the process data obtained from an industrial process. A data connection device is used to acquire the process data, related to a set of variables and time periods selected by a user, from said process data sources and said process data is displayed on an output device to the user.

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

At a predetermined rate, new sets of the process data are obtained through the 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.

DETAILED DESCRIPTION OF THE INVENTION

Notations and Definitions

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. 1shows an online process monitoring system10. The online process monitoring system10of 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 system10comprises a process data connection device20connected to a plurality of data sources40through a local area network (LAN)15, which can be formed by an Ethernet cable connection or a Wi-Fi network using the 802.xx standard, but this is not limiting of the invention. The process data connection device20enables the acquisition of process data30and is connected to at least one of the process data sources40. Example of the data sources40include, but are not limited to a data historian system26such as a laboratory information management system (LIMS)41or process information management system (PIMS)42, a data logger27such as PLS43, computer maintenance management system (CMMS)44or IMMS45, and an object linking and embedding for process control (OPC) Server46.

The industrial process monitoring system10further comprises an input system50with a computer display and at least one device for user input allowing a user to create a process fingerprint60as will be explained later and store the process fingerprint60on a storage device70, such as a file server.

The industrial process monitoring system10further comprises a management device75managing which of the created process fingerprints are active for online monitoring.

The industrial process monitoring system10further comprises a data processing device80which loads one or more of the process fingerprint60from the storage device70, obtains the process data30from the process data connection device20and calculates the distance between the obtained process data30and the loaded fingerprint60. A feedback device77, for example a mail server, provides feedback to the user for instance by sending mails, adding context information in the data logger27, such as a CMMS44, or by sending the distance to an output device100, which can display the distance to the user.

The input system50and the output device100are connected to the data processing device80through the network and exchange information using the OSI layer7HTTP or HTTPS protocols. It will be appreciated that the input system50and the output device100might 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.

Fingerprint Creation

FIG. 2shows a flowchart of a method for creating a process fingerprint60of a selected situation from the process data30. The method starts at200with the user selecting a set of reference variables X={X1, . . . , Xn}. The set of selected reference variables are often variables that are important for a specific situation, such as a process upset. In the next step205, a set of equal length (l) time periods P={[Plb, Ple], . . . , [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 data30[Xij1, . . . , Xijl], where Xijkrepresents the k'th value of variable Xiin time period [Pjb, Pje], for each variable Xiis retrieved from the corresponding process data source by means of the process data connection device20in step210.

The next step215is to align the process data30from the different time periods P. In step220, the hulls H1, . . . , Hnof the aligned data are created for each of the variables, which form a process fingerprint60F<H1, . . . , Hn>. A hull Hifor a variable Xiconsists of {Hi1, . . . ,Hil,Hi1, . . . ,Hil} and is constructed as follows:

The resulting process fingerprint60<H1, . . . , Hn> is stored on the storage device70together 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={[Plb, Ple]}, and corresponding process data30[Xill, . . . , Xi1l], the hulls are simply the values of the process data and thusHik=Hik=Xi1k.

FIGS. 3A and 3Bshow a non-limiting example of the creation of a fingerprint. In the top axes the values of two variables X1and X2are shown for three time periods of length 7 units. In the bottom axes the hulls of the corresponding variables are shown. The fingerprint60F corresponding to this example consists of the tuple <H1, H2>.

Fingerprint Management

The management device75keeps a list of all of the process fingerprints60created by the user. The management device75allows the possibility to activate, deactivate and delete existing ones of the process fingerprints60. During the fingerprint monitoring, only the activated process fingerprints60will be evaluated.

The list of all (active and inactivate) process fingerprint identifiers can be displayed to the user on the output device100. The user can use the input system50to select which ones of the process fingerprints60to activate, deactivate or delete. Once the process fingerprint60is 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 fingerprints60can be automated, for instance based on real-time context information stored in the data logger27.

FIG. 9shows a non-limiting example of a potential screen design for managing the process fingerprints60. The exemplary screen design comprises an overview of all of the created process fingerprints with their corresponding fingerprint identifier1020and an indication of whether they are active or deactivated by means of checkboxes1010. Furthermore, there is the possibility to delete any of the process fingerprints60by means of clicking the delete icons1040. Potentially extra information such as a fingerprint description1030is shown as well.

Fingerprint Monitoring

During fingerprint monitoring for each of the active process fingerprints60, the following steps are undertaken:Systematically obtain corresponding the process data30from either the OPC Server46or by polling the data sources to obtain a data stream.Calculate the distance of the data stream to the process fingerprint60.Provide feedback to the user by means of the feedback device77if said calculated distance exceeds a predetermined threshold.

The OPC server46DA provides real-time data from data sources40, such as sensors. Whenever new measurements of the process data are received for all of the relevant variables making up the process fingerprint60, the distance of the process data received up until the latest relevant time frame to the process fingerprint60is calculated. Alternatively the process data can be acquired by polling the data sources40. The actual method, which will be applied in practice, will depend on the configuration of each particular process.

FIG. 4shows a flowchart of a method using the system ofFIG. 1for detecting process fingerprints60from the process data30. In step300the process fingerprint60F is loaded from the storage device70. The process fingerprint60F consists of a tuple <H1, . . . , Hn> corresponding to the hulls of values variables X1, . . . , Xnover time periods of length l as described above. Next in step305, the new process data30over a new time period of length l of the corresponding variables X1, . . . , Xnis retrieved by the process data connection device20. Denote by χijthe j'th value of the variable Xiin the new process data30.

In step310, 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:

In step315the 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 device77in step320.

When multiple process fingerprints F60are active the steps300,305,310,315and320are applied for all of the multiple process fingerprints F60.

FIG. 5shows a non-limiting example of the distance calculation of a fingerprint60represented 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 fingerprint60.

Weights

In an additional aspect of the method it is possible to assign positive weights to subparts of the process fingerprint60in order to penalize dissimilarities corresponding to these subparts between the fingerprint60and the new data based on these weights. For each hull Hiconsisting of {Hi1, . . . ,Hil,Hi1, . . . ,Hil} a corresponding list of positive weights {Wi1+, . . . , Wil+} can be provided. It is then possible to calculate the weighted distance dw between the new data and the process fingerprint60F as follows:

dw=∑i=1n⁢⁢∑j=11⁢⁢(1-δHij_≤χij≤Hij_)*min⁡(Hij_-χij,Hij_-χij)*Wij+
where again

δHij_≤χij≤Hij_
is 1 whenHij≤χij≤Hijand 0 otherwise. Note that for parts which are not assigned weight all Wij+=1, and for parts which can be ignored all Wij+=0.

In an alternative aspect of the method, it is possible to assign negative weights to subparts of the process fingerprint60in order to reward dissimilarities corresponding to these subparts between the process fingerprint60and the new process data30based on these weights. For each hull Hiconsisting of {Hi1, . . . ,Hil,Hi1, . . . ,Hil} a corresponding list of negative weights {Wi1−, . . . , Wil−} can be provided. It is then possible to calculate the weighted distance dwbetween the new data and the process fingerprint60F as follows:

dw=∑i=1n⁢⁢∑j=11⁢⁢(1-δHij_≤χij≤Hij_)*max⁡(Λ-min⁡(Hij_-χij,Hij_-χij),0)*Wij_
where again

δHij_≤χij≤Hij_
is 1 whenHij≤χij≤Hijand 0 otherwise and Λ represents some predetermined positive value. Note that for parts, which are not assigned, weight all Wij−=1, and for parts, which can be, ignored all Wij−=0.

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

FIG. 6shows a non-limiting clarification of the addition of weights to the process fingerprint60. In this example, the positive weights {W14+, W15+, W16+} are assigned to the corresponding part of the hull of the first variable and {W21+, . . . , W27+} to the whole hull of the second variable. The negative weights {W12−, W13−} are assigned to the corresponding part of the hull of the first variable. This particular fingerprint60would indicate that the most important part of the situation is the presence of the peak at relative times 4-6 together with a pretty flat trend of the second variable. Furthermore, flatness in the first variable at relative times 2-3 is being penalized.

Smoothing

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 hullconsists of {, . . . ,, . . . ,} and is constructed as follows:

It is then possible to calculate the smooth distance dsbetween the new data and the process fingerprint60F by replacingHijwithandHijwith:

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

FIGS. 7A and 7Bshow a non-limiting clarification of the assignment of different distance measures to a process fingerprint60with smoothing parameter ρ=1.

Distance Measures

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 fingerprint60can be assigned different distance measures for calculating the dissimilarities between these subparts between the process fingerprint60and the new data.

FIG. 8shows a non-limiting clarification of the assignment of distance measures to subparts of a process fingerprint60. In this example the Euclidean, Manhattan distance and DTW are assigned to the corresponding parts of the hulls of the variables.

Feedback Mechanism

The feedback device77will handle which actions need to be undertaken when detecting a situation of interest based on a process fingerprint60. Actions can be defined for each process fingerprint60and need not be the same for each.

Feedback actions include but are not limited to:Sending an email to relevant parties including information concerning the process fingerprint60that was responsible for the detection.Logging the detection in the data logger27, such as a PLS43or CMMS44.Providing visual feedback on the output device100such as a message indicating the identifier of the process fingerprint60and the time at which the detection was made.A combination of any of the above based on the roles of different users
Historical Fingerprint Matching

As an extension to the current method, the monitoring step can be mimicked on historical process data. Instead of polling or querying the OPC server46to obtain the new process data30, the process data connection device20acquires historical data from the process data sources40starting from the earliest point for which data was available. It obtains the historical data in time frames with the same length as the corresponding fingerprint60.

One can imagine that it might be interesting for a user to discover all matches to a selected one of the fingerprints60over 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 fingerprint60F concerning variable X1of length l. By means of the process data connection device20, the data processing device80will load data of X1concerning each time frame of length l present in the data sources. For each set of historical process data30the 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.

Alternative Aspects

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 fingerprint60.

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 fingerprint60by 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 data30is acquired by means of the process data connection device20, it is interpolated to guarantee equidistantly, sampled data.

Example

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 >100), which could be included in traditional alarming systems. The engineer can use this idea to create the process fingerprint60based on said tags and said time frames. The process fingerprint60is created and stored.

The engineer selects to activate this process fingerprint60by means of the management device75. During monitoring of the process, the relevant measurements of the process data are fed the to data processing device80. The data processing device80process the process data and compares the process data with the activated process fingerprint60and detects similar behavior as identified by means of the fingerprint60.

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 fingerprint60can 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.