CN118192500A - Diagnosis method and device for equipment based on PLC control, electronic equipment and medium - Google Patents
Diagnosis method and device for equipment based on PLC control, electronic equipment and medium Download PDFInfo
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Abstract
The embodiment of the application provides a diagnosis method and device of equipment based on PLC control, electronic equipment and medium. The method comprises the following steps: obtaining PLC time sequence data of each device under normal control actions of control periods of different process stages; based on the PLC time sequence data under the normal control behavior, constructing a PLC control behavior characteristic data model; identifying the PLC control behavior of the current PLC on the equipment by utilizing the PLC control behavior characteristic data model; the behavior of the device is determined based on the identified PLC control behavior. According to the embodiment of the application, the investment of detection equipment is not required to be increased at the equipment side, and the cost is low; and the diagnosis of various different devices is adapted, and the repeatability is high.
Description
Technical Field
The invention relates to the technical field of diagnosis of automatic equipment, in particular to a diagnosis method and device of equipment based on PLC control, electronic equipment and medium.
Background
Conventional diagnostic systems for automated equipment are primarily based on controlled equipment characteristics. For example: and detecting characteristic indexes such as vibration frequency spectrum, temperature, pressure and the like of equipment controlled by the PLC, and constructing a model by taking the detected characteristic indexes as characteristic values to finish the diagnosis function of the automatic equipment. This conventional diagnostic system has the following problems: (1) A great deal of extra hardware, software and manpower are needed, for example, detection equipment is added on the original system; (2) The equipment characteristics controlled by the PLC are required to be very known, the diagnosis methods are customized, and the replicability is poor; (3) Due to high investment and high technical threshold, a diagnosis system cannot be arranged on a plurality of small-sized automation equipment with low single machine price; (4) The diagnosis system has single diagnosis function, is mainly used for predicting high-value equipment faults and lacks other functions.
Disclosure of Invention
In view of the above, the present invention provides a diagnostic method and apparatus for a PLC-controlled device, and an electronic device, a medium, for at least partially solving the above technical problems.
In a first aspect, the present disclosure provides a diagnostic method of a PLC-controlled device, comprising:
Obtaining PLC time sequence data of each device under normal control actions of control periods of different process stages;
Based on the PLC time sequence data under the normal control behavior, constructing a PLC control behavior characteristic data model;
identifying the PLC control behavior of the current PLC on the equipment by utilizing the PLC control behavior characteristic data model;
The behavior of the device is determined based on the identified PLC control behavior.
In one possible implementation, the PLC timing data includes at least input timing data, output timing data, critical intermediate variable timing data, and their correspondence over time.
In one possible implementation manner, the constructing a PLC control behavior characteristic data model based on the PLC time sequence data under the normal control behavior further includes:
Converting the input data, the output data and the key intermediate variable data of each device at a plurality of time points in the control period of each process stage into pixel values of pixel points;
And expanding the pixel values of the pixel points of each device at a plurality of time points in the control period of each process stage based on a time axis to obtain a target pixel portrait set under the normal control action of the PLC serving as the PLC control action characteristic data model for each device.
In one possible implementation manner, the identifying the PLC control behavior of the current PLC to the device using the PLC control behavior characteristic data model further includes:
Obtaining PLC time sequence data of a current PLC for a control period of equipment in a current process stage;
Converting the input data, the output data and the key intermediate variable data of the PLC at a plurality of time points in the control period of the current control stage of the equipment into pixel values of pixel points;
expanding pixel values of the pixel points at each preset time point under the control period of the current control stage based on a time axis to obtain a pixel portrait to be identified;
And comparing the pixel image to be identified with each target pixel image in the target pixel image set, and determining whether the PLC control behavior of the current PLC on the equipment belongs to normal control behavior according to the comparison result.
In one possible implementation, the obtaining PLC timing data under normal control behavior of the PLC at the control cycle of the different process stages for each device further includes:
collecting real-time data in the PLC;
Performing sequential logic analysis on the PLC internal real-time data to obtain PLC sequential data;
and obtaining the PLC time sequence data under the normal control action in a plurality of control periods for each device based on the obtained PLC time sequence data.
In a second aspect, the present disclosure provides a diagnostic apparatus of a PLC-controlled device, comprising:
the acquisition module is used for acquiring PLC time sequence data of each device under normal control behaviors of control periods of different process stages;
the model construction module is used for constructing a PLC control behavior characteristic data model based on the PLC time sequence data under the normal control behavior;
the behavior recognition module is used for recognizing the PLC control behavior of the current PLC on the equipment by utilizing the PLC control behavior characteristic data model;
and the equipment diagnosis module is used for determining the working condition of the equipment based on the identified PLC control behavior.
In one possible implementation manner, the PLC timing data at least includes input timing data, output timing data, key intermediate variable timing data, and a corresponding relationship between the three in time;
The model construction module is further used for converting the input data, the output data and the key intermediate variable data of each device at a plurality of time points in the control period of each process stage into pixel values of pixel points, and expanding the pixel values of the pixel points of each device at the plurality of time points in the control period of each process stage based on a time axis to obtain a target pixel portrait set under the normal control action of the PLC serving as the PLC control action characteristic data model.
In one possible implementation manner, the behavior recognition module is further configured to obtain PLC time sequence data of a control period of the current PLC on the device in the current control stage, convert the input data, the output data and the key intermediate variable data of the PLC on a plurality of time points of the device in the control period of the current control stage into pixel values of pixel points, expand the pixel values of the pixel points on each predetermined time point of the control period of the current control stage based on a time axis to obtain a pixel portrait to be recognized, compare the pixel portrait to be recognized with each target pixel portrait in the target pixel portrait set, and determine whether the PLC control behavior of the current PLC on the device belongs to a normal control behavior according to the comparison result.
In a third aspect, the present disclosure provides an electronic device comprising: the device comprises a processor, a communication interface, a memory and a bus, wherein the processor, the communication interface and the memory are communicated with each other through the bus;
The memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform operations corresponding to any one of the methods described above.
In a fourth aspect, the present disclosure provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any preceding claim.
In an embodiment of the application, compared to the prior art, at least the following advantages are included: 1) Only needs to collect PLC data and diagnose the abnormal working condition of the equipment controlled by the PLC data based on the PLC data, does not need a great deal of extra hardware, software and labor investment, and has low cost. 2) The device characteristics controlled by the PLC are not required to be known very, and the abnormal working condition of the controlled device can be found out in time by analyzing and monitoring the same type of PLC control behaviors. 3) Can be arranged and implemented in a plurality of small-sized automation systems with single machine and low price.
Drawings
Fig. 1 is a flowchart of a diagnostic method of a PLC-control-based device according to one embodiment of the present disclosure.
Fig. 2 (a) and 2 (b) show schematic diagrams of an example implementation of step S120 in fig. 1.
Fig. 3 is a block diagram of a diagnostic apparatus of a PLC-controlled device according to one embodiment of the present disclosure.
Fig. 4 is a block diagram of an electronic device according to one embodiment of the present disclosure.
List of reference numerals:
D: data; t: a control period;
I0.0-I0.7: inputting data; Q0.0-Q0.7: outputting data;
t0, t1, real0, real1: key intermediate variable data; t0 to Tn: a time point;
300: a diagnostic device of the device based on PLC control; 310: obtaining a module;
320: a model building module; 330: a behavior recognition module;
340: a device diagnostic module; 400: an electronic device;
402: a processor; 404: a communication interface;
406: a memory; 408: a bus;
410: and (5) program.
Detailed Description
The present application will be described in further detail below with reference to the drawings and examples in order to make the objects, technical solutions, and advantages of the present application more apparent. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other technical solutions obtained by a person skilled in the art based on the embodiments of the present application fall within the scope of protection of the present application.
Fig. 1 illustrates a diagnostic method of a PLC-controlled device according to an embodiment of the present invention. As shown in fig. 1, the method includes: in step S110, PLC timing data under normal control behavior of the PLC at the control cycle of different process stages for each device is obtained; in step S120, a PLC control behavior characteristic data model is constructed based on the PLC timing data under the normal control behavior; in step S130, the PLC control behavior characteristic data model is used to identify the PLC control behavior of the current PLC to the device; in step S140, the operation of the device is determined based on the identified PLC control behavior.
Optionally, the foregoing step S110 may further include sub-steps S110a to S110c: s110a, acquiring real-time data inside the PLC; s110b, performing sequential logic analysis on the real-time data in the PLC to obtain the PLC sequential data; and S110c, obtaining the PLC time sequence data under the normal control action in a plurality of control periods of each device based on the obtained PLC time sequence data.
Optionally, the PLC timing data in the present embodiment includes at least input timing data, output timing data, critical intermediate variable timing data, and a time-to-time correspondence of the three. Further, the aforementioned step S120 may include, for example, substeps S120a to S120b: s120a, converting the input data, the output data and the key intermediate variable data of each device at a plurality of time points in a control period of each process stage into pixel values of pixel points by the PLC; and S120b, expanding the pixel values of the pixel points of each device at a plurality of time points in the control period of each process stage based on a time axis to obtain a target pixel portrait set under the normal control action of the PLC for each device, wherein the target pixel portrait set is used as a PLC control action characteristic data model. Fig. 2 (a) and 2 (b) show schematic diagrams of example implementations of step S120, where fig. 2 (a) is an example PLC for the input data I0.0-I0.7, output data Q0.0-Q0.7 and critical intermediate variable data T0, T1, real0, real1 of an apparatus at several points in time T0-Tn within the control period T of a process stage, and fig. 2 (b) is a converted target pixel representation of fig. 2 (a). The PLC control behavior characteristic data model in the present disclosure is not limited to the expression form of the target pixel image set in the normal control behavior of the PLC for each device, and may be, for example, an expression form such as a data table in fig. 2 (a).
And, further, the aforementioned step S130 may include, for example, substeps S130a to S130d: s130a, obtaining PLC time sequence data of a control period of the current PLC on the equipment in the current process stage; s130b, converting the input data, the output data and the key intermediate variable data of the PLC at a plurality of time points in the control period of the current control stage of the equipment into pixel values of pixel points; s130c, expanding the pixel value of the pixel point at each preset time point under the control period of the current control stage based on a time axis to obtain a pixel portrait to be identified; s130d, comparing the pixel image to be identified with each target pixel image in the target pixel image set, and determining whether the PLC control behavior of the current PLC on the equipment belongs to normal control behavior according to the comparison result. It can be appreciated that step S130d may be implemented by a manual observation method or an automatic image recognition and comparison technique.
In the aforementioned sub-steps S130a to S130d: on the basis of S130a, step S140 may also be exemplarily implemented as: the device is determined to be in a normal operation state if the PLC control behavior identified in step S130d is a normal control behavior, and is determined to be in an abnormal operation state if the PLC control behavior identified in step S130d is an abnormal control behavior.
It will be appreciated that the PLC can be set and changed as required during the commissioning phase for each device at several points in time within the control cycle of each process phase, and should not be changed once put into use.
In order to implement the diagnosis method of the PLC-controlled device according to the above embodiments and alternative embodiments, another embodiment of the present disclosure further provides a diagnosis apparatus 300 of the PLC-controlled device, as shown in fig. 3, the apparatus 300 includes an obtaining module 310, a model constructing module 320, a behavior recognizing module 330, and a device diagnosis module 340. It should be noted that, since the apparatus 300 is for implementing the foregoing method embodiment, each module in the apparatus 300 is for implementing each step of the foregoing method, and although some of the same matters as those of the foregoing method embodiment are omitted here for saving the space, each technical detail in the foregoing method is applicable to the apparatus of the present embodiment. Furthermore, the present disclosure is not limited to the following embodiments, and any apparatus, unit or module capable of implementing the above method should be included in the scope of the present disclosure.
The obtaining module 310 is configured to obtain PLC timing data for each device under normal control behavior of the control cycle of the different process stages. Illustratively, the obtaining module 310 may be further configured to perform a sequential logic analysis on the PLC internal real-time data to obtain PLC sequential data, and obtain PLC sequential data under normal control behavior for a number of control cycles for each device based on the obtained PLC sequential data. In addition, the apparatus 300 may also illustratively include an acquisition module for acquiring real-time data within the PLC.
The model construction module 320 is configured to construct a PLC control behavior characteristic data model based on the PLC time sequence data under the normal control behavior. The PLC timing data may include at least input timing data, output timing data, critical intermediate variable timing data, and a corresponding relationship between the three in time, for example. And illustratively, the model building module 320 may be further configured to convert the input data, the output data, and the key intermediate variable data of each device at a plurality of time points within the control period of each process stage into pixel values of pixel points, and expand the pixel values of the pixel points of each device at the plurality of time points within the control period of each process stage based on the time axis, to obtain a target pixel representation set under normal control behavior of the PLC for each device as the PLC control behavior characteristic data model.
The behavior recognition module 330 is configured to recognize the PLC control behavior of the current PLC with respect to the device using the PLC control behavior characteristic data model. The behavior recognition module 330 may be further configured to obtain PLC time sequence data of a current PLC for a control period of a device in a current control stage, convert the input data, the output data and the key intermediate variable data of the PLC for a plurality of time points of the device in the control period of the current control stage into pixel values of pixel points, expand the pixel values of the pixel points for each predetermined time point of the control period of the current control stage based on a time axis to obtain a pixel portrait to be recognized, compare the pixel portrait to be recognized with each target pixel portrait in a target pixel portrait set, and determine whether the PLC control behavior of the current PLC for the device belongs to a normal control behavior according to the comparison result.
The device diagnostic module 340 is configured to determine the operation of the device based on the identified PLC control behavior. Specifically, if the behavior recognition module 330 determines that the PLC control behavior of the current PLC for the device belongs to the normal control behavior, the device diagnosis module 340 determines that the device is in the normal operating state based on the determination, and if the behavior recognition module 330 determines that the PLC control behavior of the current PLC for the device belongs to the abnormal control behavior, the device diagnosis module 340 determines that the device is in the abnormal operating state based on the determination.
For example, the apparatus 300 may further include a human-machine interaction module for setting an automation device fault baseline, setting an alarm, analyzing characteristics, and the like.
Fig. 4 shows a schematic diagram of an electronic device according to an embodiment of the disclosure, and it should be noted that the embodiment is not limited to a specific implementation of the electronic device 400. As shown in fig. 4, the electronic device 400 includes: a processor 402, a communication interface (Communications Interface) 404, a memory 406, and a bus 408. Wherein:
processor 402, communication interface 404, and memory 406 communicate with each other via bus 408.
A communication interface 404 for communicating with other electronic devices or servers.
Processor 402 is configured to execute program 410, and may specifically perform relevant steps in the foregoing method embodiments.
In particular, program 410 may include program code including computer-operating instructions.
The processor 402 may be a central processing unit CPU, or an Application-specific integrated Circuit ASIC (Application SPECIFIC INTEGRATED Circuit), or one or more integrated circuits configured to implement embodiments of the present disclosure. The one or more processors comprised by the smart device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 406 for storing programs 410. The memory 406 may include a high-speed RAM memory 406 and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Program 410 may be specifically configured to cause processor 402 to perform the method of any of the foregoing embodiments.
The specific implementation of each step in the procedure 410 may refer to the corresponding step and corresponding description in the unit in the method of the foregoing embodiment, which is not repeated herein. It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and modules described above may refer to corresponding procedure descriptions in the foregoing method embodiments, which are not repeated herein.
The present disclosure also provides a computer readable storage medium storing instructions for causing a machine to perform method embodiments as described herein. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may implement the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present disclosure.
Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
It should be noted that, according to implementation requirements, each component/step described in the embodiments of the present application may be split into more components/steps, or two or more components/steps or part of operations of the components/steps may be combined into new components/steps, so as to achieve the objects of the embodiments of the present application.
The above-described methods according to embodiments of the present application may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and to be stored in a local recording medium downloaded through a network, so that the methods described herein may be stored on such software processes on a recording medium using a general purpose computer, special purpose processor, or programmable or special purpose hardware such as an ASIC or FPGA. It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by a computer, processor, or hardware, performs the methods described herein. Furthermore, when a general purpose computer accesses code for implementing the methods illustrated herein, execution of the code converts the general purpose computer into a special purpose computer for performing the methods illustrated herein.
It should be noted that not all the steps and modules in the above flowcharts and the system configuration diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution sequence of the steps is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or may be implemented jointly by some components in multiple independent devices.
In the above embodiments, the hardware module may be mechanically or electrically implemented. For example, a hardware module may include permanently dedicated circuitry or logic (e.g., a dedicated processor, FPGA, or ASIC) to perform the corresponding operations. The hardware modules may also include programmable logic or circuitry (e.g., a general-purpose processor or other programmable processor) that may be temporarily configured by software to perform the corresponding operations. The particular implementation (mechanical, or dedicated permanent, or temporarily set) may be determined based on cost and time considerations.
It should be noted that in the claims and the description of this patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
While the present disclosure has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure.
While the present disclosure has been illustrated and described in detail in the drawings and in the preferred embodiments, the present disclosure is not limited to the disclosed embodiments, and it will be appreciated by those skilled in the art that the code audits in the various embodiments described above may be combined to obtain further embodiments of the present disclosure, which are also within the scope of the present disclosure.
Claims (10)
1. A method for diagnosing a PLC-controlled device, comprising:
Obtaining PLC time sequence data of each device under normal control actions of control periods of different process stages;
Based on the PLC time sequence data under the normal control behavior, constructing a PLC control behavior characteristic data model;
identifying the PLC control behavior of the current PLC on the equipment by utilizing the PLC control behavior characteristic data model;
The behavior of the device is determined based on the identified PLC control behavior.
2. The method of claim 1, wherein the PLC timing data comprises at least input timing data, output timing data, critical intermediate variable timing data, and their correspondence over time.
3. The method of claim 2, wherein constructing a PLC control behavior characteristic data model based on the PLC timing data under the normal control behavior further comprises:
Converting the input data, the output data and the key intermediate variable data of each device at a plurality of time points in the control period of each process stage into pixel values of pixel points;
And expanding the pixel values of the pixel points of each device at a plurality of time points in the control period of each process stage based on a time axis to obtain a target pixel portrait set under the normal control action of the PLC serving as the PLC control action characteristic data model for each device.
4. The method of claim 3, wherein the identifying the PLC control behavior of the current PLC to the device using the PLC control behavior characteristic data model further comprises:
Obtaining PLC time sequence data of a current PLC for a control period of equipment in a current process stage;
Converting the input data, the output data and the key intermediate variable data of the PLC at a plurality of time points in the control period of the current control stage of the equipment into pixel values of pixel points;
expanding pixel values of the pixel points at each preset time point under the control period of the current control stage based on a time axis to obtain a pixel portrait to be identified;
And comparing the pixel image to be identified with each target pixel image in the target pixel image set, and determining whether the PLC control behavior of the current PLC on the equipment belongs to normal control behavior according to the comparison result.
5. The method of any of claims 1-4, wherein the obtaining PLC timing data for each device under normal control behavior of the control cycle of the different process stages, further comprises:
collecting real-time data in the PLC;
Performing sequential logic analysis on the PLC internal real-time data to obtain PLC sequential data;
and obtaining the PLC time sequence data under the normal control action in a plurality of control periods for each device based on the obtained PLC time sequence data.
6. A diagnostic apparatus for a PLC-controlled device, the apparatus (300) comprising:
An obtaining module (310) for obtaining PLC timing data for each device under normal control behavior of the control cycle of the different process stages;
the model construction module (320) is used for constructing a PLC control behavior characteristic data model based on the PLC time sequence data under the normal control behavior;
The behavior recognition module (330) is used for recognizing the PLC control behavior of the current PLC on the equipment by utilizing the PLC control behavior characteristic data model;
A device diagnostic module (340) for determining the operation of the device based on the identified PLC control behavior.
7. The apparatus of claim 6, wherein the PLC timing data comprises at least input timing data, output timing data, critical intermediate variable timing data, and their correspondence over time;
The model construction module (320) is further configured to convert the input data, the output data and the key intermediate variable data of each device at a plurality of time points within the control period of each process stage into pixel values of pixel points, and expand the pixel values of the pixel points of each device at the plurality of time points within the control period of each process stage based on a time axis, so as to obtain a target pixel representation set under the normal control behavior of the PLC for each device, which is the PLC control behavior characteristic data model.
8. The apparatus of claim 7, wherein the behavior recognition module (330) is further configured to obtain PLC timing data of a control period of the current PLC for the device in the current control phase, convert the input data, the output data, and the key intermediate variable data of the PLC for a plurality of time points of the control period of the current control phase into pixel values of pixel points, expand the pixel values of the pixel points at each predetermined time point of the control period of the current control phase based on a time axis, obtain a pixel representation to be recognized, compare the pixel representation to be recognized with each target pixel representation in the target pixel representation set, and determine whether a PLC control behavior of the current PLC for the device belongs to a normal control behavior according to the comparison result.
9. An electronic device, the electronic device (400) comprising: -a processor (402), a communication interface (404), a memory (406) and a bus (408), said processor (402), said communication interface (404) and said memory (406) completing communication with each other via said bus (408);
the memory (406) is configured to store at least one executable instruction that causes the processor (402) to perform operations corresponding to the method according to any one of claims 1-5.
10. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-5.
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