An expert database-based fault diagnosis and fault early warning method and system

Abstract

The application discloses a kind of based on expert library's fault diagnosis and fault early warning method and system, it is related to fault early warning technical field, including real-time collection automation equipment operation data, and automation equipment operation data transmission to processing center;Processing center is compared with the standard parameter in expert library to the operation data of single automation equipment collected periodically, obtains the result of comparative analysis, if parameter anomaly is found, alarm is carried out;Periodic inspection automation equipment, and the operation data of different equipment are compared periodically;Using alarm information and expert library, fault diagnosis is carried out, the reason of failure is analyzed, according to diagnostic result, generate fault report.The application significantly improves the timeliness, accuracy and efficiency of fault processing, not only optimizes maintenance response time, enhances the ability to prevent failure, also reduces production loss and safety risk.

Description

Technical Field

[0001] This invention relates to the field of fault early warning technology, and in particular to a fault diagnosis and fault early warning method and system based on an expert database. Background Technology

[0002] The widespread deployment and operation of automated equipment is of great significance for improving industrial production efficiency and quality control. With the advancement of Industry 4.0, the monitoring and maintenance technologies for automated equipment are also constantly evolving. Traditional equipment maintenance strategies typically rely on periodic inspections and predetermined maintenance plans, which can lead to wasted resources or failures due to untimely inspections. With advancements in sensor and data analytics technologies, Condition-Based Monitoring (CBM) and Predictive Maintenance (PdM) technologies are gradually becoming the trend. These technologies can assess the health status of equipment based on real-time data, predict potential failures, thereby optimizing maintenance plans and reducing downtime.

[0003] Nevertheless, existing fault warning and diagnosis technologies still face challenges in practical applications. First, the processing and analysis of massive amounts of data generated by numerous devices requires extremely high computational efficiency and precise algorithmic support to ensure the real-time nature and accuracy of fault warnings. Second, most existing systems use general-purpose algorithms to monitor equipment, making it difficult to fully consider the specific operating characteristics and environmental factors of various devices, thus affecting the accuracy of fault diagnosis.

[0004] This invention proposes a fault diagnosis and early warning method based on an expert database. This method effectively improves the accuracy and response speed of fault detection by intelligently comparing and analyzing real-time operating data of automated equipment with standard parameters in the expert database. Furthermore, by introducing the concept of periodic inspections and adding differentiated processing for primary and secondary inspections, the method can more accurately assess the equipment status based on real-time and historical operating data, and promptly detect and diagnose potential anomalies. This method not only improves the timeliness and accuracy of early warnings but also optimizes maintenance decisions by calculating the conditional probability of faults, significantly enhancing the targeting and effectiveness of maintenance operations. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem that this invention aims to solve is: how existing fault warning systems, which are often based on fixed thresholds or simple pattern recognition, not only limit the accuracy of warnings but also affect their timeliness.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a fault diagnosis and early warning method based on an expert database, comprising: real-time collection of automated equipment operation data and transmission of the automated equipment operation data to a processing center; the processing center periodically comparing the collected operation data of individual automated equipment with standard parameters in the expert database to obtain the results of the comparative analysis; and issuing an alarm if abnormal parameters are found; periodically inspecting the automated equipment and comparing the operation data of different equipment; using the alarm information and the expert database to perform fault diagnosis, analyze the causes of the fault, and generate a fault report based on the diagnosis results.

[0008] As a preferred embodiment of the fault diagnosis and fault early warning method based on an expert database described in this invention, the automated equipment operation data includes communication status, analog quantity, input quantity, soft and hard pressure plates, set value, software version, self-test alarm, time synchronization, current and voltage circuit, and GPS alarm.

[0009] As a preferred embodiment of the fault diagnosis and early warning method based on an expert database described in this invention, the periodic inspection comparison includes a primary inspection and a secondary inspection; the primary inspection includes checking whether the operating mode and operating conditions of the automated equipment are consistent with the standard, where the standard is the equipment operating condition information that conforms to the current operating mode; an inspection alarm is generated when an anomaly occurs; after the primary inspection task is completed, a message indicating that the primary inspection was completed without anomalies or with anomalies is sent, and an inspection report is generated for each inspection; the secondary inspection includes comparing the operating data of the automated equipment that was completed without anomalies or with anomalies in the primary inspection with the preset standard values ​​or normal operating thresholds in the expert database, and an inspection report is generated for each inspection; logical detection is used to identify anomalies or deviations in the data, and for communication status, it monitors whether communication is interrupted. For analog signals, set upper and lower thresholds for each analog signal and monitor for any exceeding of these limits. For digital inputs, monitor the status changes of the switching signals. For soft and hard pressure plates, check if the status of the soft and hard pressure plates matches the preset normal status. For setpoints, monitor the operating parameters and setpoints of the equipment and check if they match the predetermined parameter settings. For software versions, confirm that the software version is up-to-date. For self-test alarms, determine whether the equipment has a self-test fault or configuration error based on the content of the self-test alarm. For time synchronization, detect the deviation between the system time and the standard time to confirm whether there is a time synchronization problem. For current and voltage loops, set upper and lower thresholds for current and voltage and monitor for current imbalance or voltage abnormalities. For GPS alarms, monitor the GPS alarm status and check for signal interruption or location information deviation.

[0010] As a preferred embodiment of the fault diagnosis and fault early warning method based on an expert database described in this invention, the alarms include: for communication status, an alarm is immediately triggered when the communication status changes from normal to interrupted, triggering a communication interruption alarm; for analog quantities, an analog quantity exceeding a set threshold triggers an analog quantity out-of-bounds alarm; for input quantities, an unexpected status change alarm is triggered when the status of a switch quantity is not changed by command; for soft or hard pressure plates, a status inconsistency alarm is triggered if the status of the soft or hard pressure plate is inconsistent with the preset status; for setpoints, a setpoint inconsistency alarm is triggered if any setpoint or parameter of the device does not match the predetermined setpoint; for software versions, an outdated software version alarm is triggered if the software version is not updated to the latest version; for self-test alarms, a self-test failure alarm is triggered if the device reports any errors during self-testing; for time synchronization, a time synchronization error alarm is triggered if the deviation between the system time and the standard time exceeds the set allowable range; for current and voltage circuits, an abnormal current or voltage alarm is triggered if the current or voltage exceeds the set upper or lower thresholds; and for GPS alarms, a GPS function failure alarm is triggered if the GPS signal is interrupted or the location information is deviated.

[0011] As a preferred embodiment of the fault diagnosis and fault early warning method based on an expert database described in this invention, the periodic inspection of the automated equipment includes inspection items for the measurement and control device, communication gateway device, monitoring host, switch, time synchronization device, and operation and maintenance management substation; the inspection items for the measurement and control device include input quantities, soft pressure plates, hard pressure plates, set values, software versions, self-test alarms, time synchronization, current and voltage circuits; the inspection items for the communication gateway device include input quantities, set values, software versions, self-test alarms, and time synchronization; the inspection items for the monitoring host include software versions, self-test alarms, and time synchronization; the inspection items for the switch include input quantities, software versions, self-test alarms, and time synchronization; the inspection items for the time synchronization device include input quantities, software versions, self-test alarms, and time synchronization; and the inspection items for the operation and maintenance management substation include communication status.

[0012] As a preferred embodiment of the fault diagnosis and fault early warning method based on an expert database described in this invention, the fault diagnosis includes: if no abnormality is found after the first inspection and no abnormality is found after the second inspection, then the automated equipment is not abnormal; if no abnormality is found after the first inspection but an abnormality is found after the second inspection, then it is a single-two-abnormality; if an abnormality is found after the first inspection but no abnormality is found after the second inspection, then it is a single-abnormality; if an abnormality is found after the first inspection and an abnormality is found after the second inspection, then it is a full-abnormality.

[0013] As a preferred embodiment of the fault diagnosis and fault early warning method based on an expert database described in this invention, the fault diagnosis further includes: if it is a single-two anomaly, it indicates that the automated equipment was initially in good condition but a sudden fault occurred. Fault localization is performed, focusing on checking the abnormal parameters found during the second inspection, identifying the affected components, and generating a fault report based on the fault diagnosis results by retrieving similar faults from the expert database, and repairing the affected components; if it is a single fault, it indicates that the anomaly detected during the first inspection was temporary or sporadic, or had already recovered before the second inspection, and the first and second inspections are repeated; if it is a total fault, inspection reports from the first and second inspections are collected, and the probability of the automated equipment malfunctioning is calculated as follows:

[0014] ,

[0015] in, This represents the conditional probability of equipment failure when both inspection data show abnormalities. This represents the probability of two abnormal inspection data points under the condition that a known equipment failure has occurred. Let be the prior probability of the fault. The probability of anomalies occurring in both the first and second inspections is considered. This is an abnormal data point from a single inspection. This is abnormal data from a second inspection. For automated equipment malfunctions; set a malfunction probability threshold, if... If the probability exceeds the fault probability threshold, the automated equipment should be stopped immediately. Based on the anomalies observed during the first and second inspections, a fault report of similar faults should be generated from the expert database, and repairs should be performed accordingly. If the probability is less than the fault probability threshold, a new inspection is performed. If the first inspection is abnormal, the same type of fault is retrieved from the expert database and repaired until the first inspection is normal. Then a second inspection is performed. If the second inspection fails, it is handled as a single fault.

[0016] Another objective of this invention is to provide a system for fault diagnosis and early warning based on an expert database, which solves the problem of fault diagnosis and early warning based on an expert database by constructing a fault diagnosis and early warning system.

[0017] To address the aforementioned technical problems, this invention provides the following technical solution: a fault diagnosis and early warning system based on an expert database, comprising a data acquisition module, a comparison module, a periodic inspection module, and a fault diagnosis module; the data acquisition module collects operational data of automated equipment in real time and transmits the operational data to a processing center; the comparison module compares the collected operational data of individual automated equipment with standard parameters in the expert database periodically, obtaining the results of the comparison analysis, and issuing an alarm if abnormal parameters are found; the periodic inspection module periodically inspects the automated equipment and performs periodic comparisons of operational data from different equipment; the fault diagnosis module uses alarm information and the expert database to perform fault diagnosis, analyze the causes of the fault, and generate a fault report based on the diagnosis results.

[0018] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the fault diagnosis and fault early warning method based on an expert database as described above.

[0019] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the fault diagnosis and fault early warning method based on an expert database as described above.

[0020] The beneficial effects of this invention are as follows: The fault diagnosis and early warning method based on an expert database provided by this invention collects equipment operating data, compares the data with standard parameters in the expert database to identify deviations, and uses alarm information and the expert database to accurately diagnose the cause of the fault, significantly improving the timeliness, accuracy, and efficiency of fault handling. It not only optimizes maintenance response time and enhances the ability to prevent faults, but also reduces production losses and safety risks, providing an efficient and systematic solution for equipment management. Furthermore, the use of periodic inspections reduces consumption. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 The flowchart illustrates a fault diagnosis and fault early warning method based on an expert database, as provided in the first embodiment of the present invention.

[0023] Figure 2 The diagram shows a structure of a fault diagnosis and early warning system based on an expert database, as provided in the second embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0026] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a fault diagnosis and fault early warning method based on an expert database, including: collecting automated equipment operation data in real time and transmitting the automated equipment operation data to a processing center; the processing center periodically inspects and compares the collected operation data of individual automated equipment with standard parameters in the expert database to obtain the results of the comparative analysis, and if abnormal parameters are found, an alarm is issued; periodically inspects the automated equipment and performs periodic inspection and comparison of the operation data of different equipment; using the alarm information and the expert database, fault diagnosis is performed, the cause of the fault is analyzed, and a fault report is generated based on the diagnosis results.

[0027] S1. Collect automated equipment operation data in real time and transmit the automated equipment operation data to the processing center.

[0028] Equipment operation data includes communication status, analog signals, input signals, hardware and software pressure plates, set values, software version, self-test alarms, time synchronization, current and voltage circuits, and GPS alarms.

[0029] Automated equipment includes measurement and control devices, communication gateways, monitoring hosts, network switches, time synchronization devices, and operation and maintenance management substations. Automated equipment provides equipment ledger information, communication status information, self-test alarm information, equipment resource information, internal environment information, and time synchronization status information to meet the requirements of online monitoring and anomaly analysis.

[0030] Real-time collection of automated equipment operation data ensures that data can be accessed and compared at any time during periodic inspections or manual inspections.

[0031] S2. The processing center will periodically inspect and compare the collected operating data of individual automated devices with the standard parameters in the expert database to obtain the results of the comparative analysis. If abnormal parameters are found, an alarm will be issued.

[0032] Periodic inspection and comparison includes primary inspection and secondary inspection. Primary inspection involves checking whether the operating mode and conditions of automated equipment are consistent with the standards. The standards are the operating condition information of equipment that conforms to the current operating mode. An inspection alarm is generated when there is an anomaly. After the primary inspection task is completed, a message is sent indicating that the primary inspection was completed without any anomalies or with anomalies. An inspection report is generated for each inspection. Secondary inspection involves comparing the operating data of automated equipment that was completed without any anomalies or with anomalies in the primary inspection with the preset standard values ​​or normal operating thresholds in the expert database. An inspection report is generated for each inspection.

[0033] Periodic inspections are conducted at regular intervals, consisting of one or two inspections, with the option to manually select when to conduct an inspection. The intervals are determined based on actual needs and can be adjusted. Inspection reports should display both normal and abnormal results; abnormal results require a more detailed description of the discrepancies.

[0034] The expert database includes preset standard values, normal operating thresholds, fault types, and handling measures for the equipment, and is updated regularly.

[0035] The collected data is compared with preset standard values ​​or normal operating thresholds in the expert database; logic detection is used to identify anomalies or deviations in the data; for communication status, communication is monitored for interruption; for analog quantities, upper and lower limit thresholds are set for each analog quantity, and out-of-bounds checks are performed; for input quantities, the status changes of switch quantities are monitored, especially changes not triggered by operation commands; for soft and hard pressure plates, the status of the soft and hard pressure plates is checked to ensure they are consistent with the preset normal status; for setpoints, the operating parameters and setpoints of the equipment are monitored to check if they are consistent with the predetermined parameter settings; for software versions, the software version is confirmed to be the latest; for self-test alarms, the content of the self-test alarms is used to determine if there are specific faults or configuration errors in the equipment; for time synchronization, the deviation between the system time and the standard time is detected to confirm if there are time synchronization problems; for current and voltage loops, upper and lower limit thresholds are set for current and voltage, and current imbalance or voltage abnormalities are monitored; for GPS alarms, the GPS alarm status is monitored to check for signal interruption or location information deviation. See Table 1.

[0036] Table 1 Inspection Item List

[0037] ,

[0038] For communication status, an alarm is immediately triggered when the communication status changes from normal to interrupted, triggering a communication interruption alarm. For analog quantities, an analog quantity exceeding the set threshold triggers an analog quantity exceeding the limit alarm. For input quantities, if the status of a switch quantity changes unexpectedly from closed to open without a command, an unexpected status change alarm is triggered. For soft and hard pressure plates, if the status of the soft or hard pressure plate is inconsistent with the preset status, a status inconsistency alarm is triggered. For setpoints, if any setpoint or parameter of the device does not match the predetermined setpoint, a setpoint inconsistency alarm is triggered.

[0039] For software version, if the software version is not updated to the latest version, an outdated software version alarm will be issued. For self-test alarm, if any error is reported during device self-test, a device self-test failure alarm will be issued. For time synchronization, if the deviation between the system time and the standard time exceeds the set allowable range, a time synchronization error alarm will be issued. For current and voltage circuits, if the current or voltage exceeds the set upper or lower threshold, a current or voltage abnormality alarm will be issued. For GPS alarm, if the GPS signal is interrupted or the location information is deviated, a GPS function failure alarm will be issued.

[0040] S3. Periodic inspection of automated equipment: Periodic inspection and comparison of operating data of different equipment.

[0041] The equipment inspection scope includes inspection items for measurement and control devices, communication gateways, monitoring hosts, switches, time synchronization devices, and operation and maintenance management substations. Inspection items for measurement and control devices include other analog quantities, input quantities, soft and hard control boards, setpoints, software versions, self-test alarms, time synchronization, and current and voltage loops. Inspection items for communication gateways include other analog quantities, input quantities, setpoints, software versions, self-test alarms, and time synchronization. Inspection items for monitoring hosts include other analog quantities, software versions, self-test alarms, and time synchronization. Inspection items for switches include other analog quantities, input quantities, software versions, self-test alarms, and time synchronization. Inspection items for time synchronization devices include other analog quantities, input quantities, software versions, self-test alarms, and time synchronization. Inspection items for operation and maintenance management substations include communication status and other analog quantities. See Table 2. Other analog quantities include all analog quantities related to the equipment except for current and voltage.

[0042] Table 2 Equipment Inspection Scope

[0043] ,

[0044] S4. Utilize alarm information and expert database to perform fault diagnosis, analyze the cause of the fault, and generate a fault report based on the diagnosis results.

[0045] If no abnormalities are found after the first inspection and no abnormalities are found after the second inspection, then the automated equipment is normal. If no abnormalities are found after the first inspection but an abnormality is found after the second inspection, then it is a single-two-abnormality. If an abnormality is found after the first inspection but no abnormalities are found after the second inspection, then it is a single-abnormality. If an abnormality is found after the first inspection and an abnormality is found after the second inspection, then it is a complete abnormality.

[0046] If it is a single-two anomaly, it indicates that the automated equipment was initially in good condition, but a sudden failure occurred in a short period of time. This may be due to operational errors, changes in environmental factors, or sudden failure of equipment components. To locate the fault, focus on checking the abnormal parameters found during the second inspection, identify the affected components, and generate a fault report based on the fault diagnosis results by retrieving similar faults from the expert database. Repair the affected components.

[0047] If it is a single fault, it indicates that the abnormality detected during the first inspection was temporary or sporadic, or that it had recovered on its own before the second inspection, so the first and second inspections should be carried out again.

[0048] If it is a total failure, then collect the inspection reports from the first and second inspections, and calculate the probability of the automated equipment failing as follows:

[0049] ,

[0050] in, This represents the conditional probability of equipment failure when both inspection data show abnormalities. To determine the probability of two abnormal inspection data points under a known equipment failure, a statistical method is used based on historical data at the time of the failure. The prior probability of the fault is given based on historical fault records. The probability of anomalies occurring in both the first and second inspections is considered. This is an abnormal data point from a single inspection. This is abnormal data from a second inspection. The problem is a malfunction in the automated equipment.

[0051] Set a fault probability threshold based on historical data, if If the probability exceeds the fault probability threshold, this usually indicates strong evidence that the equipment has experienced or is about to experience a serious fault. In this case, the automated equipment should be stopped immediately, and fault reports of similar faults should be generated from the expert database based on the first and second inspection anomalies, and repairs should be carried out accordingly. If the error rate is below the fault probability threshold, a new inspection is performed. If the first inspection fails, similar faults from the expert database are retrieved and repaired until the first inspection is normal. Then, a second inspection is performed. If the second inspection fails, it is handled as a single fault. Sensors and monitoring tools in automated systems may generate false alarms due to various reasons (such as environmental factors, sensor aging, etc.). By setting a reasonable fault probability threshold and re-checking when the error rate is below that threshold, unnecessary maintenance or downtime caused by false alarms can be reduced, thus optimizing operational efficiency.

[0052] The fault report includes information on the first inspection, the second inspection, and the overall anomaly, as well as handling measures from the expert database. If the expert database contains similar faults, handling measures for similar faults are provided; if the expert database does not contain similar faults, emergency handling is carried out, and the handling measures and fault information are entered into the expert database for storage.

[0053] Example 2, refer to Figure 2 This is the second embodiment of the present invention, which differs from the previous embodiment in that it provides a fault diagnosis and fault early warning system based on an expert database, including: a data acquisition module 100, a comparison module 200, a periodic inspection module 300, and a fault diagnosis module 400.

[0054] The data acquisition module 100 collects the operating data of the automated equipment in real time and transmits the operating data of the automated equipment to the processing center.

[0055] The comparison module 200 processing center periodically compares the collected operating data of individual automated devices with the standard parameters in the expert database to obtain the results of the comparison analysis. If abnormal parameters are found, an alarm is issued.

[0056] The periodic inspection module 300 periodically inspects automated equipment and compares the operating data of different equipment during the periodic inspection process.

[0057] The fault diagnosis module 400 uses alarm information and an expert database to perform fault diagnosis, analyze the cause of the fault, and generate a fault report based on the diagnosis results.

[0058] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0060] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0061] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0062] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that: it is used to verify and explain the technical effects adopted in the present invention, so as to verify the real effect of the method.

[0063] The method of this invention and conventional methods are used to simultaneously test automated equipment.

[0064] This embodiment utilizes both conventional methods and the method of the present invention for simultaneous detection, and the comparison results are shown in Table 3 below:

[0065] Table 3 Comparison of Traditional Methods and the Method of the Invention

[0066] ,

[0067] This invention's method diagnoses problems by collecting equipment data in real time and periodically comparing it with standard parameters in an expert database. Traditional threshold monitoring methods set separate, fixed parameter thresholds. When monitored data exceeds the threshold, a fault alarm is triggered. Based on the equipment manufacturer's recommended maintenance plan or past experience, the equipment is maintained periodically (e.g., monthly, quarterly), regardless of its actual operating condition. This approach may lead to over-maintenance or failure to perform necessary maintenance before a fault occurs, resulting in inspection and repair only after a fault has occurred, often leading to unexpected downtime and production losses.

[0068] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for fault diagnosis and fault early warning based on expert library, characterized in that: The utility model relates to an automatic fault diagnosis method and system for automation equipment, and belongs to the field of automation equipment fault diagnosis. Real-time collection of automation equipment operation data and transmission of the automation equipment operation data to a processing center; The processing center compares the collected operation data of the individual automation equipment with standard parameters in an expert database in periodic patrol, obtains a comparison and analysis result, and if an abnormal parameter is found, an alarm is given; Periodic patrol of the automation equipment and periodic patrol comparison of the operation data of different equipment; Fault diagnosis using the alarm information and the expert database, analysis of the cause of the fault, and generation of a fault report according to the diagnosis result; The periodic patrol comparison includes a first patrol and a second patrol; The first patrol includes checking whether the operation mode and operation condition of the automation equipment are consistent with the standard, the standard being the equipment operation condition information consistent with the operation mode at the time, and generating a patrol alarm when an abnormality occurs; after the first patrol task is completed, information about whether there is an abnormality in the first patrol is sent, and a patrol report is formed each time a patrol is performed; The second patrol includes comparing the operation data of the automation equipment for which the first patrol is completed without an abnormality and the first patrol is completed with an abnormality with preset standard values or normal operation threshold values in the expert database, and a patrol report is formed each time a patrol is performed; Logical detection is used to identify abnormalities or deviations in the data, for communication status, monitoring whether communication is interrupted, for analog quantity, setting upper and lower threshold values for each analog quantity and monitoring whether the threshold values are exceeded, for input quantity, monitoring the state change of the switch quantity, for soft and hard pressure plates, checking whether the states of the soft and hard pressure plates are consistent with the preset normal state, for fixed value, monitoring the operation parameters and fixed value of the equipment and checking whether they are consistent with the predetermined parameter setting value, for software version, confirming whether the software version is the latest, for self-checking alarm, judging whether the equipment has a self-checking fault or a configuration error according to the content of the self-checking alarm, for time synchronization, detecting the deviation of the system time from the standard time and confirming whether there is a time synchronization problem, for the current and voltage loop, setting upper and lower threshold values for the current and voltage and monitoring whether there is current imbalance or voltage abnormality, and for GPS alarm, monitoring the GPS alarm state and checking whether there is a problem of signal interruption or position information deviation; The alarm includes, for communication status, triggering an alarm immediately when the communication status changes from normal to interruption and giving a communication interruption alarm, for analog quantity, giving an analog quantity out-of-range alarm when any analog quantity exceeds the set threshold value, for input quantity, giving a state unexpected change alarm when the state of the switch quantity is changed without a command, for soft and hard pressure plates, giving a state inconsistency alarm when the state of the soft or hard pressure plate is inconsistent with the preset state, and for fixed value, giving a fixed value inconsistency alarm when any fixed value or parameter of the equipment is not matched with the predetermined setting value; For software version, giving a software version out-of-date alarm when the software version is not updated to the latest version, for self-checking alarm, giving a device self-checking failure alarm when any error problem is reported during device self-checking, and for time synchronization, giving a time synchronization error alarm when the deviation of the system time from the standard time exceeds the set allowed range. For current-voltage loop, current or voltage exceeds the set upper and lower threshold, current or voltage abnormal alarm, for GPS alarm, GPS signal interruption or position information deviation, GPS function failure alarm.

2. The expert database-based fault diagnosis and fault early warning method according to claim 1, characterized in that: The automation equipment operation data includes communication state, analog quantity, input quantity, soft and hard pressure plate, setting value, software version, self-checking alarm, time synchronization, current-voltage loop, GPS alarm.

3. The expert database-based fault diagnosis and fault early warning method according to claim 2, characterized in that: The periodic inspection automation equipment includes measurement and control device inspection item, communication gateway machine inspection item, monitoring host inspection item, switch inspection item, time service device inspection item and operation and maintenance management substation inspection item; The measurement and control device inspection item includes input quantity, soft pressure plate, hard pressure plate, setting value, software version, self-checking alarm, time synchronization, current and voltage loop; The communication gateway machine inspection item includes input quantity, setting value, software version, self-checking alarm and time synchronization; The monitoring host inspection item includes software version, self-checking alarm and time synchronization; The switch inspection item includes input quantity, software version, self-checking alarm and time synchronization; The time service device inspection item includes input quantity, software version, self-checking alarm and time synchronization; The operation and maintenance management substation inspection item includes communication state.

4. The expert database-based fault diagnosis and fault early warning method according to claim 3, characterized in that: The fault diagnosis includes that if one-time inspection is completed without exception and two-time inspection is completed without exception, the automation equipment has no exception, if one-time inspection is completed without exception and two-time inspection is completed with exception, it is single two exceptions, if one-time inspection is completed with exception and two-time inspection is completed without exception, it is single exception, and if one-time inspection is completed with exception and two-time inspection is completed with exception, it is full exception.

5. The expert database-based fault diagnosis and fault early warning method according to claim 4, characterized in that: The fault diagnosis further includes that if it is single two exceptions, it indicates that the initial state of the automation equipment is good, but a sudden fault occurs, fault positioning is performed, the abnormal parameters found in the two-time inspection are checked, the affected parts are identified, according to the fault diagnosis result, the fault report is generated according to the same fault in the expert library, and the affected parts are maintained; If it is single fault, it indicates that the exception detected in one-time inspection is temporary or occasional, or it has been restored before two-time inspection, and one-time inspection and two-time inspection are performed again; If it is full fault, the inspection reports in one-time inspection and two-time inspection are collected, and the probability of automation equipment fault is calculated as, wherein, P (F | A1, A2) is the conditional probability of a failure of the equipment given that both the first and second patrol data show an anomaly, P (A1, A2 | F) is the probability of both the first and second anomaly patrol data given that the equipment has failed, P (F) is the prior probability of a failure, P (A1, A2) is the probability of both the first and second patrol data showing an anomaly, P (A1) is the first patrol anomaly data, P (A2) is the second patrol anomaly data, F is a failure of the automated equipment; A fault probability threshold is set, and if If the fault probability is greater than the fault probability threshold, the automation equipment is immediately stopped, a fault report is generated according to the first and second patrol abnormalities and the same type of fault in the expert database, and maintenance is performed respectively. If If the probability of failure is less than the threshold value, a first inspection is re-performed, if the first inspection is abnormal, the expert database of similar failures is called to perform maintenance until the first inspection is normal, a second inspection is performed, if the second inspection is abnormal, a single fault is handled according to the single-two fault.

6. A system using the expert database-based fault diagnosis and fault warning method according to any one of claims 1 to 5, characterized in that: It includes a data acquisition module (100), a comparison module (200), a periodic inspection module (300) and a fault diagnosis module (400). The data acquisition module (100) collects automation equipment operation data in real time, and transmits the automation equipment operation data to the processing center; The comparison module (200) compares the collected operation data of a single automation equipment with the standard parameters in the expert library for periodic inspection, obtains the comparison analysis result, and alarms if the parameters are found to be abnormal; The periodic inspection module (300) periodically inspects the automation equipment, and compares the operation data of different equipment for periodic inspection; The fault diagnosis module (400) uses the alarm information and the expert library to perform fault diagnosis, analyzes the cause of the fault, and generates a fault report according to the diagnosis result. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The computer program is executed by the processor to implement the steps of the expert database-based fault diagnosis and fault warning method in any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the expert database-based fault diagnosis and fault warning method in any one of claims 1-5.

Images