Patent Description:
<CIT> relates to process plants and control systems, and more particularly to using mobile user interface devices in process plants and in process control systems. A process instrument is generally an instrument used to measure, monitor or check on-site signals such as temperature, pressure, flow rate, liquid level or valve positioning in an industrial (e.g. chemical industrial) process on a mass production operating line. Examples of process instruments include temperature sensors, pressure sensors, liquid level meters and flow meters, etc..

Process instruments generally always have certain basic self-diagnostic abilities for identifying abnormal operating situations, e.g. high electronic temperature, power supply voltage loss, etc. However, process instruments cannot diagnose certain other anomalies, such as pressure sensor pulse line blockage, sensor drift, and measurement errors caused by gas being entrained in process fluid; thus, measurement values of the process instruments will no longer be able accurately reflect process changes, and the abnormal measurement results outputted will affect future analysis results when used as historical data.

For the diagnosis of these anomalies, various anomaly diagnosis algorithms for process instruments are already in existence, e.g. statistical analysis, expert systems, etc. However, most anomaly diagnosis algorithms are data intensive and/or computationally intensive algorithms, and are difficult to use in low-power or low-cost process control apparatuses having limited resources; thus, at present, these anomaly diagnosis algorithms are generally used as independent applications.

In view of this, the object of the present invention is to propose an anomaly detection method for a process instrument, and also to propose an anomaly detection apparatus and system for a process instrument, and a storage medium, for the purpose of enhancing the functionality of a programmable logic controller (PLC), to realize anomaly diagnosis for process instruments.

The present invention defines an anomaly detection method according to the subject-matter of claim <NUM>.

According to the present invention, due to the fact that a mapping is set between the output of each anomaly diagnosis algorithm arranged on a service provision system and each input channel of at least one virtual function module integrated on the PLC, each anomaly diagnosis algorithm is disguised as an algorithm module of the PLC itself; thus, when each anomaly diagnosis algorithm on the service provision system is used to evaluate process instrument measurement data received by the PLC and determine that an anomaly has occurred in a particular process instrument, an anomaly indication will be outputted to the corresponding input channel of the PLC, and the anomaly indication participates in PLC control directly as an input parameter of the PLC, thereby enhancing the functionality of the PLC, and realizing anomaly diagnosis for the process instrument.

According to the invention a PLC is defined according to the subject-matter of claim <NUM>.

The present invention comprises: the service provision system acquiring measurement data of a process instrument from a historical database; the measurement data being measurement data which is received by the PLC from the process instrument and periodically stored in the historical database; wherein at least one anomaly diagnosis algorithm is arranged on the service provision system (<NUM>), and a mapping is set between an output of the at least one anomaly diagnosis algorithm and at least one input channel of at least one virtual function module (<NUM>) integrated on the programmable logic controller PLC (<NUM>); the service provision system using the corresponding anomaly diagnosis algorithm to diagnose the measurement data, and upon diagnosing that an anomaly has occurred in the process instrument, outputting an anomaly indication to the corresponding input channel of the virtual function module mapped to the anomaly diagnosis algorithm.

According to the present invention, due to the fact that each anomaly diagnosis algorithm is arranged on a service provision system, while at least one virtual function module is integrated on the PLC, and a mapping is established between each input channel of each virtual function module and the output of each anomaly diagnosis algorithm arranged on the service provision system, each anomaly diagnosis algorithm is disguised as an algorithm module of the PLC itself; thus, when each anomaly diagnosis algorithm on the service provision system is used to evaluate process instrument measurement data received by the PLC and determine that an anomaly has occurred in a particular process instrument, an anomaly indication will be outputted to the corresponding input channel of the PLC, and the anomaly indication participates in PLC control directly as an input parameter of the PLC, thereby enhancing the functionality of the PLC, and realizing anomaly diagnosis for the process instrument.

In one embodiment, the method further comprises: the service provision system replacing measurement data, corresponding to when an anomaly occurs in the process instrument, with a preset default datum; storing the default datum in the historical database instead of the measurement data corresponding to when an anomaly occurs in the process instrument. In this embodiment, by using a relatively accurate default datum to correct data in the historical database following the occurrence of an anomaly in a process instrument, it is possible to increase the accuracy of results when historical data is used to perform future analysis or statistical analysis.

The anomaly detection apparatus for a process instrument proposed in an embodiment of the present invention may be a PLC and a service provision system.

The PLC comprises a virtual function module, an analog quantity input/output module, a central processor module, a function module and a power supply module, wherein a mapping is set between at least one input channel of the virtual function module and an output of at least one anomaly diagnosis algorithm arranged on a service provision system, and when an input channel receives an anomaly indication outputted by the service provision system, the virtual function module provides the anomaly indication to the function module as an input variable of the function module; wherein the input variable is used to indicate that an anomaly exists in a process instrument corresponding to the anomaly diagnosis algorithm mapped to the input channel; the analog quantity input/output module receives measurement data of a process instrument, converts the measurement data from an analog quantity to a digital quantity and then provides same to the function module; and receives a first control instruction or second control instruction from the function module, converts the first control instruction or second control instruction from a digital quantity to an analog quantity and then outputs same to a process execution mechanism; the central processor module caches and transfers the measurement data; the function module, upon receiving the input variable, determines that an anomaly exists in the corresponding process instrument, and outputs a first control instruction to the analog quantity input/output module according to a preset default value; and otherwise outputs a second control instruction to the analog quantity input/output module according to the measurement data; the power supply module supplies power to the central processor module, the analog quantity input/output module and the function module.

As can be seen, due to the fact that a mapping is set between the output of each anomaly diagnosis algorithm arranged on a service provision system and each input channel of at least one virtual function module integrated on the PLC, each anomaly diagnosis algorithm is disguised as an algorithm module of the PLC itself; thus, when each anomaly diagnosis algorithm on the service provision system is used to evaluate process instrument measurement data received by the PLC and determine that an anomaly has occurred in a particular process instrument, an anomaly indication will be outputted to the corresponding input channel of the PLC, and the anomaly indication participates in PLC control directly as an input parameter of the PLC, thereby enhancing the functionality of the PLC, and realizing anomaly diagnosis for the process instrument. In addition, when it is diagnosed that an anomaly has occurred in a process instrument, by using a preset, relatively accurate default datum to replace the measurement data to perform subsequent control, it is possible to make PLC control more accurate, thereby improving PLC control accuracy.

The service provision system may comprise an algorithm module, a first communication module, a data processing module and a second communication module; wherein the algorithm module comprises at least one anomaly diagnosis algorithm, each anomaly diagnosis algorithm being used to diagnose whether an anomaly exists in a process instrument; a mapping is established between an output of the at least one anomaly diagnosis algorithm and at least one input channel of at least one virtual function module integrated on a programmable logic controller PLC; the first communication module acquires measurement data of a process instrument from a historical database; the measurement data is measurement data which is received by the PLC from the process instrument and periodically collected in the historical database; the data processing module calls the corresponding anomaly diagnosis algorithm in the algorithm module to diagnose the measurement data, and outputs an anomaly indication to the second communication module upon diagnosing that an anomaly exists in the corresponding process instrument; the second communication module outputs the anomaly indication to the corresponding input channel of the virtual function module mapped to the anomaly diagnosis algorithm.

As can be seen, according to the invention, due to the fact that a mapping is set between the output of each anomaly diagnosis algorithm arranged on a service provision system and each input channel of at least one virtual function module integrated on the PLC, each anomaly diagnosis algorithm is disguised as an algorithm module of the PLC itself; thus, when each anomaly diagnosis algorithm on the service provision system is used to evaluate process instrument measurement data received by the PLC and determine that an anomaly has occurred in a particular process instrument, an anomaly indication will be outputted to the corresponding input channel of the PLC, and the anomaly indication participates in PLC control directly as an input parameter of the PLC, thereby enhancing the functionality of the PLC, and realizing anomaly diagnosis for the process instrument.

In one embodiment, the data processing module further replaces measurement data, corresponding to when an anomaly occurs in the process instrument, with a preset default datum; the first communication module further stores the default datum in the historical database instead of the measurement data corresponding to when an anomaly occurs in the process instrument. In this embodiment, by using a relatively accurate default datum to correct data in the historical database following the occurrence of an anomaly in a process instrument, it is possible to increase the accuracy of results when historical data is used to perform future analysis or statistical analysis.

In one embodiment, the service provision system is arranged on a PC, a server, or an industrial cloud. As can be seen, the service provision system can be flexibly arranged on a third party resource other than the PLC; in particular, as cloud technology develops, the efficiency of a service provision system arranged on a cloud resource is increased.

In one embodiment, the service provision system may also comprise: at least one memory and at least one processor,.

The anomaly diagnosis system for a process instrument proposed in an embodiment which is not claimed may comprise the abovementioned PLC, service provision system and historical database; wherein the historical database is configured to store measurement data of at least one process instrument collected periodically from the PLC.

The computer-readable storage medium proposed in an embodiment which is not claimed has a computer program stored thereon; the computer program is executable by a processor and realizes the anomaly detection method for a process instrument as described above.

Other features, characteristics and advantages of the present invention will become more obvious through the following detailed description in conjunction with the drawings.

The reference labels used in the drawings are as follows:.

In an embodiment of the present invention, in view of the fact that at present, many industrial control apparatuses are realized using a PLC, and existing anomaly diagnosis algorithms cannot be used in the PLC directly, each anomaly diagnosis algorithm is arranged on a service provision system such as a PC, server or industrial cloud; at the same time, at least one virtual function module is integrated on the PLC, and a mapping is established between each input channel of each virtual function module and an output of each anomaly diagnosis algorithm arranged on the service provision system. Thus, measurement data of each process instrument received by the PLC can be subjected to evaluation diagnosis using the corresponding anomaly diagnosis algorithm on the service provision system; subsequently, when an anomaly is detected in a particular process instrument, an anomaly indication is outputted to the corresponding input channel of the corresponding virtual function module, such that the anomaly indication can directly participate in a control process of the PLC as an input parameter of the control process of the PLC.

To clarify the object, technical solution and advantages of the present invention, the present invention is explained in further detail below, citing embodiments.

<FIG> is a structural schematic diagram of an application scenario of each embodiment of the present invention. As shown in <FIG>, the application scenario comprises a PLC <NUM>, a historical database <NUM> and a service provision system <NUM>.

At least one virtual function module <NUM> is integrated on the PLC <NUM>, and a mapping is provided between at least one input channel of each virtual function module <NUM> and an output of at least one anomaly diagnosis algorithm arranged on the service provision system <NUM>. The PLC <NUM> can receive measurement data of each process instrument <NUM>, and control a corresponding process execution mechanism <NUM> according to the measurement data; or, when an anomaly indication fed back by the service provision system <NUM> via the corresponding input channel of the virtual function module <NUM> is received, said anomaly indication indicating that an anomaly has occurred in a particular process instrument, the corresponding process execution mechanism <NUM> is controlled according to the anomaly indication.

The historical database <NUM> stores data related to various control processes collected periodically from the PLC, including measurement data of each process instrument <NUM>.

At least one anomaly diagnosis algorithm is arranged on the service provision system <NUM>, each anomaly diagnosis algorithm being used to diagnose whether an anomaly exists in a process instrument. The service provision system <NUM> can acquire measurement data of each process instrument <NUM> from the historical database <NUM>, use the corresponding anomaly diagnosis algorithm to diagnose the measurement data, and if it is determined that an anomaly has occurred in a particular process instrument, output an anomaly indication to the input channel of the corresponding virtual function module <NUM> of the PLC <NUM>, as an input parameter of the PLC <NUM>, such that the PLC <NUM> can control the corresponding process execution mechanism <NUM> according to the anomaly indication. The service provision system <NUM> may be arranged on a PC/server <NUM> or any cloud resource such as an industrial cloud <NUM>, etc.; in this embodiment, the case where it is arranged on the industrial cloud <NUM> is taken as an example.

A method for integrating the virtual function module <NUM> on the PLC <NUM> may comprise: creating a virtual function module description file comprising an input channel address of the virtual function module <NUM> to be created, installing the virtual function module description file in a programming tool of the PLC <NUM>, creating the virtual function module in the PLC <NUM> by means of a module catalogue provided by the programming tool, allocating the input channel address of the virtual function module on the PLC <NUM>, and creating a virtual function module configuration file comprising a mapping between the input channel of the virtual function module <NUM> and an output of the service provision system <NUM>.

The mapping between the input channel of the virtual function module <NUM> and the output of the anomaly diagnosis algorithm arranged on the service provision system <NUM> may be set by the following method: establishing a communication connection between the PLC <NUM> and the service provision system <NUM>, creating a virtual function module configuration file comprising a mapping between the input channel of the virtual function module <NUM> and the output of the service provision system <NUM>, and providing the virtual function module configuration file to the service provision system <NUM>; an output variable from the service provision system <NUM> can then be received via the corresponding input channel of the virtual function module <NUM>, and the output variable can be used to update a control parameter of the PLC.

<FIG> is a demonstrative flow chart of the anomaly detection method for a process instrument according to the present invention. The method may be applied to the PLC in <FIG>; as shown in <FIG>, the method comprises the following steps:.

In this embodiment, the PLC <NUM> can control a process execution mechanism <NUM> according to measurement data of the process instrument <NUM> when the anomaly indication is not received; and control the process execution mechanism <NUM> according to a preset default datum when the anomaly indication is received.

<FIG> is a schematic diagram of a control process of a PLC in an embodiment of the present invention. As shown in <FIG>, arrow <NUM> corresponds to measurement data of a process instrument, arrow <NUM> corresponds to a preset default datum, arrow <NUM> corresponds to an anomaly indication, and arrow <NUM> corresponds to a processing value used by the PLC; the process execution mechanism is controlled according to the processing value. As can be seen, when no anomaly indication is received, the processing value used by the PLC is the measurement data of the process instrument corresponding to arrow <NUM>; when an anomaly indication is received, the processing value used by the PLC is the preset default datum corresponding to arrow <NUM>.

<FIG> is a demonstrative flow chart of the anomaly detection method for a process instrument in another embodiment of the present invention. The method may be applied to the service provision system in <FIG>; as shown in <FIG>, the method may comprise the following steps:.

In this embodiment, the method may further comprise: step <NUM>: the service provision system <NUM> replaces measurement data, corresponding to when an anomaly occurs in the process instrument, with a preset default datum; and stores the default datum in the historical database <NUM> instead of the measurement data corresponding to when an anomaly occurs in the process instrument.

<FIG> is a demonstrative structural diagram of a PLC according to the present invention. As shown in <FIG>, the PLC comprises: a virtual function module <NUM>, an analog quantity input/output module <NUM>, a central processor module <NUM>, a function module <NUM> and a power supply module <NUM>.

A mapping is established between at least one input channel of the virtual function module <NUM> and an output of at least one anomaly diagnosis algorithm arranged on a service provision system <NUM>; the virtual function module <NUM> is configured to, when an input channel receives an anomaly indication outputted by the service provision system <NUM>, provide the anomaly indication to the function module <NUM> as an input variable of the function module <NUM>; the input variable is used to indicate that an anomaly exists in the process instrument corresponding to the anomaly diagnosis algorithm mapped to the input channel.

The analog quantity input/output module <NUM> is configured to receive measurement data of a process instrument <NUM>, convert the measurement data from an analog quantity to a digital quantity and then provide same to the function module <NUM>; and receive a first control instruction or second control instruction from the function module <NUM>, convert the first control instruction or second control instruction from a digital quantity to an analog quantity and then output same to a process execution mechanism <NUM>.

The central processor module <NUM> is configured to cache and transfer measurement data of the process instrument <NUM>. For example, after the analog quantity input/output module <NUM> has converted the measurement data from an analog quantity to a digital quantity, the measurement data can be stored in a cache, which may be internal memory of the central processor module <NUM>, and the function module <NUM> can then acquire the measurement data from the cache. In addition, the measurement data of each process instrument <NUM> stored in the historical database <NUM> may be periodically read from the central processor module <NUM>.

The function module <NUM> is configured to, upon receiving the input variable, determine that an anomaly exists in the corresponding process instrument, and output a first control instruction to the analog quantity input/output module <NUM> according to a preset default value; and otherwise output a second control instruction to the analog quantity input/output module <NUM> according to the measurement data.

The power supply module <NUM> is configured to supply power to the entire PLC, including the central processor module <NUM>, the analog quantity input/output module <NUM> and the function module <NUM>.

In this embodiment, the function module <NUM> may comprise a proportional-integral-derivative (PID) controller, etc..

<FIG> is a demonstrative structural diagram of a service provision system <NUM> in an embodiment of the present invention. As shown in <FIG>, the service provision system <NUM> may comprise an algorithm module <NUM>, a first communication module <NUM>, a data processing module <NUM> and a second communication module <NUM>.

The algorithm module <NUM> comprises at least one anomaly diagnosis algorithm, each anomaly diagnosis algorithm being used to diagnose whether an anomaly exists in a process instrument. A mapping is established between an output of the at least one anomaly diagnosis algorithm and at least one input channel of at least one virtual function module <NUM> integrated on a PLC <NUM>.

The first communication module <NUM> is configured to acquire measurement data of a process instrument from a historical database <NUM>; the measurement data is measurement data which is received by the PLC <NUM> from the process instrument and periodically stored in the historical database <NUM>.

The data processing module <NUM> is configured to call the corresponding anomaly diagnosis algorithm in the algorithm module <NUM> to diagnose the measurement data acquired by the first communication module <NUM>, and output an anomaly indication to the second communication module <NUM> upon diagnosing that an anomaly exists in the corresponding process instrument.

The second communication module <NUM> is configured to output the anomaly indication to the corresponding input channel of the virtual function module <NUM> mapped to the anomaly diagnosis algorithm.

In one embodiment, the data processing module <NUM> is further configured to replace measurement data, corresponding to when an anomaly occurs in the process instrument, with a preset default datum. Correspondingly, the first communication module <NUM> is further configured to store the default datum in the historical database <NUM> instead of the measurement data corresponding to when an anomaly occurs in the process instrument.

In an embodiment of the present invention, the service provision system <NUM> may be arranged on a PC, a server, or any cloud resource such as an industrial cloud.

<FIG> is a demonstrative structural diagram of another service provision system <NUM> in an embodiment of the present invention. As shown in <FIG>, the service provision system may comprise: at least one memory <NUM>, at least one processor <NUM> and at least one port <NUM>. These components communicate via a bus <NUM>.

The at least one memory <NUM> is configured to store a computer program. The computer program may be understood to comprise the various modules shown in <FIG>, i.e. the algorithm module <NUM>, the first communication module <NUM>, the data processing module <NUM> and a second communication module <NUM>.

In addition, the at least one memory <NUM> may also store an operating system, etc. The operating system comprises but is not limited to: Android operating systems, Symbian operating systems, Windows operating systems, Linux operating systems, etc..

The at least one port <NUM> is configured to send and receive data.

The at least one processor <NUM> is configured to call a computer program stored in the at least one memory <NUM>, in order to execute the anomaly detection method for a process instrument in an embodiment of the present invention on the basis of the function of sending and receiving data of the at least one port <NUM>. The processor <NUM> may be a CPU, a processing unit/module, an ASIC, a logic module or a programmable gate array, etc..

The anomaly detection system for a process instrument in an embodiment of the present invention may comprise the abovementioned historical database <NUM>, PLC <NUM> and service provision system <NUM>.

In addition, also provided in an embodiment of the present invention is a computer-readable storage medium, having a computer program stored thereon; the computer program is executable by a processor and realizes the anomaly detection method for a process instrument in an embodiment of the present invention.

As can be seen, according to the present invention, due to the fact that a mapping is set between the output of each anomaly diagnosis algorithm arranged on a service provision system and each input channel of at least one virtual function module integrated on the PLC, each anomaly diagnosis algorithm is disguised as an algorithm module of the PLC itself; thus, when each anomaly diagnosis algorithm on the service provision system is used to evaluate process instrument measurement data received by the PLC and determine that an anomaly has occurred in a particular process instrument, an anomaly indication will be outputted to the corresponding input channel of the PLC, and the anomaly indication participates in PLC control directly as an input parameter of the PLC, thereby enhancing the functionality of the PLC, and realizing anomaly diagnosis for the process instrument. In addition, when it is diagnosed that an anomaly has occurred in a process instrument, by using a preset, relatively accurate default datum to replace the measurement data to perform subsequent control, it is possible to make PLC control more accurate, thereby improving PLC control accuracy.

In addition, when it is detected that an anomaly has occurred in a process instrument, the PLC uses a preset, relatively accurate default datum to replace the measurement data to perform subsequent control, such that PLC control is more accurate, and PLC control accuracy is thereby improved.

Claim 1:
An anomaly detection method for a process instrument, comprising:
receiving by a programmable logic controller PLC (<NUM>) measurement data of a process instrument, wherein the measurement data is periodically stored in a historical database (<NUM>), and wherein at least one virtual function module (<NUM>) is integrated on the programmable logic controller PLC (<NUM>), wherein a mapping is set between at least one input channel of each virtual function module (<NUM>) and an output of at least one anomaly diagnosis algorithm arranged on a service provision system (<NUM>), each anomaly diagnosis algorithm being used to diagnose whether an anomaly exists in a process instrument;
upon receiving, via an input channel of the virtual function module (<NUM>), an anomaly indication outputted by the service provision system (<NUM>), the programmable logic controller PLC (<NUM>) determines that an anomaly exists in the process instrument corresponding to the anomaly diagnosis algorithm,
wherein the anomaly indication is an indication outputted when the service provision system (<NUM>) determines that an anomaly has occurred in the process instrument after acquiring the measurement data from the historical database (<NUM>) and using the corresponding anomaly diagnosis algorithm to diagnose the measurement data, wherein each anomaly diagnosis algorithm is disguised as an algorithm module of the PLC, such that the anomaly indication is outputted to a corresponding input channel of the PLC (<NUM>) and the anomaly indication participates in PLC control directly as an input parameter of the PLC; and
wherein the programmable logic controller PLC (<NUM>) controls a process execution mechanism according to the measurement data when the anomaly indication is not received, and controls the process execution mechanism according to a preset default datum when the anomaly indication is received.