CN111882833B - Equipment fault early warning method, device, equipment and medium based on outlier parameters - Google Patents
Equipment fault early warning method, device, equipment and medium based on outlier parameters Download PDFInfo
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- CN111882833B CN111882833B CN202010707243.4A CN202010707243A CN111882833B CN 111882833 B CN111882833 B CN 111882833B CN 202010707243 A CN202010707243 A CN 202010707243A CN 111882833 B CN111882833 B CN 111882833B
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Abstract
The invention discloses an equipment fault early warning method, device, equipment and medium based on outlier parameters, wherein the equipment fault early warning method based on the outlier parameters comprises the following steps: acquiring calculation point values of the same kind of parameters in one or more running devices in real time, and generating a separation group according to the calculation point values; detecting whether an abnormal calculation point value exists in the cluster group, and if the abnormal calculation point value exists, removing the cluster group from the abnormal calculation point value to obtain a target cluster group; calculating to obtain the outlier deviation degree based on the target outlier; and carrying out fault early warning on the operating equipment according to the outlier deviation. The method and the device can acquire the calculated point values of the same type of parameters in the same type of operating equipment in real time to form the outlier, calculate the outlier deviation according to the outlier, perform fault early warning on the operating equipment according to the outlier deviation, obtain the parameter outlier condition before the operating equipment fails according to the outlier deviation, and realize fault sensing and research and judgment on the operating equipment according to the parameter outlier condition.
Description
Technical Field
The invention relates to the technical field of detection, in particular to an equipment fault early warning method, device, equipment and medium based on outlier parameters.
Background
Most of large-scale plants (such as thermal power plants) have various production equipment, the real-time parameter data point of each equipment is more, and when the point has an alarm, the equipment is possibly influenced or the production is influenced. At present, after a large-scale factory with higher informatization popularization is in an online asset management system, a defect short message reminding platform is developed, the short message reminding platform has certain pushing action for promoting production management, but only can process equipment with problems, and cannot detect and sense the state of the equipment before failure.
Disclosure of Invention
The invention mainly aims to provide an equipment fault early warning method, device, equipment and medium based on outlier parameters, and aims to solve the technical problems that in the prior art, only equipment with problems can be processed, and the state of the equipment before fault cannot be detected and sensed.
In order to achieve the above object, embodiments of the present invention provide an apparatus fault early warning method, device, apparatus, and medium based on an outlier parameter, where the apparatus fault early warning method based on the outlier parameter includes:
obtaining calculation point values of the same type of parameters in one or more running devices in real time, and generating a cluster group according to the calculation point values, wherein the running devices are the same in type;
detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster;
calculating an outlier bias based on the target outlier;
and carrying out fault early warning on the operating equipment according to the outlier deviation.
Preferably, the degree of outlier deviation comprises a maximum degree of outlier deviation and a minimum degree of outlier deviation; the step of calculating an outlier bias based on the target outlier comprises:
determining the number value of the calculation point with the largest value in the target outlier as the maximum outlier, and calculating a first average value of the number values of all the calculation points except the maximum outlier in the outlier;
determining the calculation point value with the minimum value in the target outlier as a minimum outlier, and calculating a second average value of all calculation point values except the minimum outlier in the outlier;
calculating a first difference value based on the maximum outlier and the first average value, and making a ratio of the first difference value and the first average value to obtain a maximum outlier deviation degree;
and calculating a second difference value based on the minimum outlier and the second average value, and making a ratio of the second difference value to the second average value to obtain a minimum outlier deviation degree.
Preferably, the step of performing fault early warning on the operating device according to the outlier deviation degree includes:
detecting whether the maximum outlier deviation degree in the outlier deviation degrees is larger than a first preset range maximum value and/or whether the minimum outlier deviation degree is smaller than a first preset range minimum value;
and if the maximum outlier deviation degree is greater than the first preset range maximum value and/or the minimum outlier deviation degree is smaller than the first preset range minimum value, generating a fault warning report based on the target outlier so that a worker corresponding to the operating device can check the fault warning report.
Preferably, the step of performing fault early warning on the operating device according to the outlier deviation further includes:
monitoring a first duration value that the maximum outlier deviation is greater than the maximum value of the first preset range and a second duration value that the minimum outlier deviation is less than the minimum value of the first preset range;
detecting whether any one of the first duration value and the second duration value is greater than a preset duration;
and if any one of the first duration value and the second duration value is greater than the preset duration, generating fault early warning information and sending the fault early warning information to a worker corresponding to the operating equipment so as to enable the worker to perform precaution processing.
Preferably, the step of performing fault early warning on the operating device according to the outlier deviation further includes:
detecting whether the maximum outlier deviation in the outlier deviations is greater than a second preset range maximum;
detecting whether the minimum outlier deviation in the outlier deviations is smaller than the second preset range minimum;
and if the maximum outlier deviation degree is greater than the second preset range maximum value and/or the minimum outlier deviation degree is less than the second preset range minimum value, judging that the operating equipment fails, and sending alarm information.
Preferably, the abnormal calculation point values include a bad point value, a limit point value, and a deviation point value; the step of detecting whether an abnormal computation point value exists in the cluster, and if the abnormal computation point value exists, removing the abnormal computation point value from the cluster comprises:
respectively detecting whether any one of the dead pixel numerical value, the limit point numerical value and the deviation point numerical value exists in the separation group;
if any one of the bad point numerical value, the limit point numerical value and the deviation point numerical value exists, removing one of the bad point numerical value, the limit point numerical value and the deviation point numerical value which has the separation group from the separation group.
Preferably, the step of obtaining the target group comprises:
detecting whether the number of the calculation point values in the target group is greater than or equal to a preset threshold value;
if the number of the calculation point values in the target outlier group is greater than or equal to a preset threshold value, judging that the condition for calculating the outlier deviation degree is achieved, and executing the step of calculating the outlier deviation degree based on the target outlier group;
and if the number of the numerical values of the calculation points in the target outlier group is smaller than a preset threshold value, judging that the condition of calculating the outlier deviation degree is not met, and displaying prompt information of calculation failure.
In order to achieve the above object, the present invention further provides an apparatus fault early warning device based on outlier parameters, which includes:
the acquisition module is used for acquiring the calculation point values of the same type of parameters in one or more running devices in real time and generating a cluster group according to the calculation point values, wherein the running devices are the same in type;
the detection module is used for detecting whether an abnormal calculation point value exists in the cluster, and if the abnormal calculation point value exists, the abnormal calculation point value is removed from the cluster to obtain a target cluster;
a calculation module for calculating an outlier bias based on the target outlier;
and the early warning module is used for carrying out fault early warning on the operating equipment according to the outlier deviation degree.
Further, in order to achieve the above object, the present invention further provides an equipment failure early warning device based on an outlier parameter, where the equipment failure early warning device based on an outlier parameter includes a memory, a processor, and an equipment failure early warning program based on an outlier parameter, which is stored in the memory and is executable on the processor, and the equipment failure early warning program based on an outlier parameter implements the steps of the above equipment failure early warning method based on an outlier parameter when executed by the processor.
Further, in order to achieve the above object, the present invention further provides a medium, where an equipment failure early warning program based on an outlier parameter is stored on the medium, and the equipment failure early warning program based on the outlier parameter implements the steps of the equipment failure early warning method based on the outlier parameter when executed by a processor.
The embodiment of the invention provides an equipment fault early warning method, device, equipment and medium based on outlier parameters, wherein the equipment fault early warning method based on the outlier parameters comprises the following steps: obtaining calculation point values of the same type of parameters in one or more running devices in real time, and generating a cluster group according to the calculation point values, wherein the running devices are the same in type; detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster; calculating an outlier bias based on the target outlier; and carrying out fault early warning on the operating equipment according to the outlier deviation. The method and the device can acquire the group of similar parameters in the same type of operation equipment in real time, calculate the outlier deviation degree according to the outlier, perform fault early warning on the operation equipment according to the outlier deviation degree, obtain the parameter outlier condition before the operation equipment fails according to the outlier deviation degree, and realize fault sensing and research and judgment on the operation equipment according to the parameter outlier condition.
Drawings
Fig. 1 is a schematic structural diagram of a hardware operating environment according to an embodiment of the method for early warning of device failure based on outliers of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the method for early warning of device failure based on outlier parameters according to the present invention;
FIG. 3 is a schematic flow chart of a third embodiment of the method for early warning of device failure based on outlier parameters according to the present invention;
fig. 4 is a functional block diagram of an apparatus failure early warning device based on outliers according to a preferred embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides an equipment fault early warning method, device, equipment and medium based on outlier parameters, wherein the equipment fault early warning method based on the outlier parameters comprises the following steps: obtaining calculation point values of the same type of parameters in one or more running devices in real time, and generating a cluster group according to the calculation point values, wherein the running devices are the same in type; detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster; calculating an outlier bias based on the target outlier; and carrying out fault early warning on the operating equipment according to the outlier deviation. The method and the device can acquire the group of similar parameters in the same type of operation equipment in real time, calculate the outlier deviation degree according to the outlier, perform fault early warning on the operation equipment according to the outlier deviation degree, obtain the parameter outlier condition before the operation equipment fails according to the outlier deviation degree, and realize fault sensing and research and judgment on the operation equipment according to the parameter outlier condition.
As shown in fig. 1, fig. 1 is a schematic structural diagram of an equipment fault early warning device based on an outlier parameter in a hardware operating environment according to an embodiment of the present invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The equipment fault early warning equipment based on the outlier parameter in the embodiment of the invention can be a PC, and can also be mobile terminal equipment such as a tablet computer and a portable computer.
As shown in fig. 1, the device fault pre-warning device based on the outlier may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the outlier parameter based device fault warning device shown in fig. 1 does not constitute a limitation of the outlier parameter based device fault warning device and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a medium, may include therein an operation failure warning system, a network communication module, a user interface module, and an equipment failure warning program based on an outlier parameter.
In the device shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the outlier parameter-based device failure warning program stored in the memory 1005, and perform the following operations:
obtaining calculation point values of the same type of parameters in one or more running devices in real time, and generating a cluster group according to the calculation point values, wherein the running devices are the same in type;
detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster;
calculating an outlier bias based on the target outlier;
and carrying out fault early warning on the operating equipment according to the outlier deviation.
Further, the degree of outlier bias comprises a maximum degree of outlier bias and a minimum degree of outlier bias; the step of calculating an outlier bias based on the target outlier comprises:
determining the number value of the calculation point with the largest value in the target outlier as the maximum outlier, and calculating a first average value of the number values of all the calculation points except the maximum outlier in the outlier;
determining the calculation point value with the minimum value in the target outlier as a minimum outlier, and calculating a second average value of all calculation point values except the minimum outlier in the outlier;
calculating a first difference value based on the maximum outlier and the first average value, and making a ratio of the first difference value and the first average value to obtain a maximum outlier deviation degree;
and calculating a second difference value based on the minimum outlier and the second average value, and making a ratio of the second difference value to the second average value to obtain a minimum outlier deviation degree.
Further, the step of performing fault early warning on the operating device according to the outlier deviation degree comprises:
detecting whether the maximum outlier deviation degree in the outlier deviation degrees is larger than a first preset range maximum value and/or whether the minimum outlier deviation degree is smaller than a first preset range minimum value;
and if the maximum outlier deviation degree is greater than the first preset range maximum value and/or the minimum outlier deviation degree is smaller than the first preset range minimum value, generating a fault warning report based on the target outlier so that a worker corresponding to the operating device can check the fault warning report.
Further, the step of performing fault early warning on the operating device according to the outlier deviation degree further includes:
monitoring a first duration value that the maximum outlier deviation is greater than the maximum value of the first preset range and a second duration value that the minimum outlier deviation is less than the minimum value of the first preset range;
detecting whether any one of the first duration value and the second duration value is greater than a preset duration;
and if any one of the first duration value and the second duration value is greater than the preset duration, generating fault early warning information and sending the fault early warning information to a worker corresponding to the operating equipment so as to enable the worker to perform precaution processing.
Further, the step of performing fault early warning on the operating device according to the outlier deviation degree further includes:
detecting whether the maximum outlier deviation in the outlier deviations is greater than a second preset range maximum;
detecting whether the minimum outlier deviation in the outlier deviations is smaller than the second preset range minimum;
and if the maximum outlier deviation degree is greater than the second preset range maximum value and/or the minimum outlier deviation degree is less than the second preset range minimum value, judging that the operating equipment fails, and sending alarm information.
Further, the abnormal calculation point values comprise a bad point value, a limit point value and a deviation point value; the step of detecting whether an abnormal computation point value exists in the cluster, and if the abnormal computation point value exists, removing the abnormal computation point value from the cluster comprises:
respectively detecting whether any one of the dead pixel numerical value, the limit point numerical value and the deviation point numerical value exists in the separation group;
if any one of the bad point numerical value, the limit point numerical value and the deviation point numerical value exists, removing one of the bad point numerical value, the limit point numerical value and the deviation point numerical value which has the separation group from the separation group.
Further, after the step of obtaining the target outlier, the processor 1001 may be configured to call the device malfunction early warning program based on the outlier parameter stored in the memory 1005, and perform the following operations:
detecting whether the number of the calculation point values in the target group is greater than or equal to a preset threshold value;
if the number of the calculation point values in the target outlier group is greater than or equal to a preset threshold value, judging that the condition for calculating the outlier deviation degree is achieved, and executing the step of calculating the outlier deviation degree based on the target outlier group;
and if the number of the numerical values of the calculation points in the target outlier group is smaller than a preset threshold value, judging that the condition of calculating the outlier deviation degree is not met, and displaying prompt information of calculation failure.
For a better understanding of the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 2, a first embodiment of the present invention provides a flow chart of an apparatus fault early warning method based on outliers. In this embodiment, the method for early warning of device failure based on outlier parameters includes the following steps:
step S10, obtaining the calculation point values of the same kind of parameters in one or more operation devices in real time, and generating a cluster group according to the calculation point values, wherein the types of the operation devices are the same;
the device fault early warning method based on the outlier parameter is applied to a fault early warning System, the fault early warning System is in communication connection with a Safety Instrument System (SIS) of a factory, all parameter data of various operating devices are stored in the SIS System, so that the fault early warning System can conveniently obtain the parameter data from the SIS System, and the fault early warning System is provided with calculation software for calculation.
Further, the fault early warning system acquires the numerical values of the calculation points of the same type of parameters in one or more operation devices of the same type in real time from the SIS system. The calculation points are data detection points of the same type of parameters in the operating equipment, and the parameters can be temperature, pressure, vibration and the like. It can be understood that, since different parameters of different types of operating devices, such as temperature, pressure, vibration, and the like, have different influences on the devices, the calculated point values obtained in real time in this embodiment are calculated point values of the same type of parameter in one or more operating devices of the same type. Further, the fault early warning system generates an outlier according to the acquired values of the calculation points, wherein the outlier is a set of all the values of the calculation points which have the same operation conditions for participating in the outlier.
Step S20, detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster;
further, the fault early warning system detects whether an abnormal calculation point value which does not accord with the condition of participating in the outlier operation exists in the generated outlier group, wherein the abnormal calculation point value comprises a dead point value, a limit point value and a deviation point value, the dead point is an inaccurate value detection point which is acquired due to the damage of the detection instrument, the limit point is a value detection point which enables the detection instrument to acquire a fixed value due to the fact that the equipment is not operated, the limit point can be the starting and stopping state, the rotating speed and the current of the equipment, and the deviation point is a value detection point which enables the detection instrument to acquire a value deviating from a preset range due to the damage of the equipment. Specifically, the fault early warning system detects whether a bad point value or a limit point numerical value or a deviation point numerical value exists in the cluster, and if the bad point value or the limit point numerical value or the deviation point numerical value exists, the bad point numerical value or the limit point numerical value or the deviation point numerical value is removed from the cluster. Further, the fault early warning system determines the outlier from which the abnormal calculation point value is removed as a target outlier, calculates the outlier deviation degree according to the target outlier, and performs fault early warning on the operating equipment according to the outlier deviation degree.
Step S30, calculating the degree of outlier deviation based on the target outlier;
further, the fault early warning system determines a calculation point value with the largest value and a calculation point value with the smallest value from a plurality of calculation point values in the target cluster group respectively, determines the calculation point value with the largest value as a maximum cluster point and determines the calculation point value with the smallest value as a minimum cluster point, and calculates the maximum cluster deviation degree and the minimum cluster deviation degree according to the maximum cluster point and the minimum cluster point in the target cluster group and other calculation point values of the target cluster group respectively, so as to perform fault early warning on the operating equipment according to the maximum cluster deviation degree and the minimum cluster deviation degree.
And step S40, performing fault early warning on the operating equipment according to the outlier deviation degree.
Further, after the outlier deviation degree is calculated, the fault early warning system detects whether the outlier deviation degree exceeds a first preset range, and if the outlier deviation degree exceeds the first preset range, a fault early warning report is generated to be checked by a worker, wherein the first preset range is an outlier percentage range obtained by the fault early warning system through parameter information operation of equipment. Further, under the condition that the outlier deviation exceeds a first preset range, the system monitors a duration numerical value of the outlier deviation exceeding the first preset range, if the duration numerical value is larger than a preset duration numerical value, fault early warning information is generated and sent to a worker corresponding to the operating device, and the worker can take precautions according to the fault early warning information for processing, wherein the preset duration is set by the user according to the use condition of the device hardware. Further, the fault early warning system detects whether the outlier deviation degree exceeds a second preset range, wherein the second preset range represents the maximum range of acceptable parameter deviation when the equipment normally operates. And if the detected outlier deviation exceeds a second preset range, judging that the operating equipment fails, and sending alarm information by the fault early warning system to inform workers to process the failed equipment.
The embodiment of the invention provides an equipment fault early warning method, device, equipment and medium based on outlier parameters, wherein the equipment fault early warning method based on the outlier parameters comprises the following steps: obtaining calculation point values of the same type of parameters in one or more running devices in real time, and generating a cluster group according to the calculation point values, wherein the running devices are the same in type; detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster; calculating an outlier bias based on the target outlier; and carrying out fault early warning on the operating equipment according to the outlier deviation. The method and the device can acquire the calculated point values of the same type of parameters in the same type of operating equipment in real time to form the outlier, calculate the outlier deviation according to the outlier, perform fault early warning on the operating equipment according to the outlier deviation, obtain the parameter outlier condition before the operating equipment fails according to the outlier deviation, and realize fault sensing and research and judgment on the operating equipment according to the parameter outlier condition.
Further, based on the first embodiment of the method for early warning of device failure based on outlier parameters of the present invention, a second embodiment of the method for early warning of device failure based on outlier parameters of the present invention is provided, and in the second embodiment, the step of performing failure early warning on the operating device according to the outlier deviation degree includes:
step S41, detecting whether the maximum outlier deviation of the outlier deviations is greater than a first preset range maximum and/or the minimum outlier deviation is less than a first preset range minimum;
step S42, if the maximum outlier deviation is greater than the first preset range maximum and/or the minimum outlier deviation is smaller than the first preset range minimum, generating a fault warning report based on the target outlier, so that a worker corresponding to the operating device views the fault warning report.
Further, the fault early warning system sets a first preset range in the target outlier, detects whether the maximum outlier deviation degree in the outlier deviation degrees is larger than the maximum value of the first preset range, and detects whether the minimum outlier deviation degree in the outlier deviation degrees is smaller than the minimum value of the first preset range, so as to determine whether the maximum deviation degree and the minimum deviation degree are within the normal deviation range. Further, if the maximum outlier deviation is larger than the maximum value of the first preset range, it is determined that the maximum outlier deviation exceeds the normal deviation range, the fault early warning system records each calculation point data of the target outlier, and generates a fault early warning report according to each calculation point data, so that a worker corresponding to the operating device can check the fault early warning report. Further, if the minimum outlier deviation is smaller than the minimum value of the first preset range, it is determined that the minimum outlier deviation exceeds the normal deviation range, the fault early warning system records each calculation point data of the target outlier, and generates a fault early warning report according to each calculation point data, so that a worker corresponding to the operating device can check the fault early warning report to know the parameter outlier condition of the operating device.
When the maximum outlier deviation degree is detected to be greater than the maximum value of the first preset range or the minimum outlier deviation degree is detected to be smaller than the minimum value of the first preset range, the representation running device has a smaller fault occurrence trend, and the fault early warning system generates a fault early warning report form for a worker corresponding to the running device to check the fault early warning report form. Further, the fault early warning system detects a first time length value that the maximum outlier deviation degree is greater than the maximum value of a first preset range and a second time length value that the minimum outlier deviation degree is less than the minimum value of the first preset range, so as to ensure normal operation of the equipment, and specifically, the step of performing fault early warning on the operating equipment according to the outlier deviation degree further includes:
step S43, monitoring a first duration value that the maximum outlier deviation is greater than the maximum value of the first preset range, and a second duration value that the minimum outlier deviation is less than the minimum value of the first preset range;
step S44, detecting whether any one of the first duration value and the second duration value is greater than a preset duration;
and step S45, if any one of the first duration value and the second duration value is greater than a preset duration, generating fault early warning information and sending the fault early warning information to a worker corresponding to the operating equipment so that the worker can take precautionary treatment.
Further, the fault early warning system is connected with the short message platform so as to send information related to the operation equipment to the working personnel through the short message platform. Further, if the maximum outlier deviation degree is detected to be larger than the maximum value of the first preset range, the fault early warning system monitors a first duration time of the maximum outlier deviation degree larger than the maximum value of the first preset range and records the first duration time as a first time value; and if the detected minimum outlier deviation degree is smaller than the minimum value of the first preset range, the fault early warning system monitors a second duration time of which the minimum outlier deviation degree is smaller than the minimum value of the first preset range and records the second duration time as a second duration time value. It can be understood that if the time length of the parameter of the operating equipment exceeding the normal deviation range is longer than the preset time length, the operating equipment has a tendency of failure. Further, the fault early warning system detects whether any one of the first duration value and the second duration value is greater than a preset duration set by a user, if any one of the first duration value and the second duration value is greater than the preset duration set by the user, the fault early warning system performs authority configuration in the short message platform, generates fault early warning information according to the recorded first duration value and the second duration value, and sends the fault early warning information to a worker related to the operating equipment through a short message, so that the worker such as a professional engineer, a supervisor and the like can take precautions against the operating equipment according to the fault early warning information, for example, the operating equipment is checked, and the worker focuses on equipment components related to the parameter, so as to ensure that the equipment normally operates.
The method comprises the steps that when any one of the first duration value and the second duration value is detected to be longer than the preset duration set by a user, the representation equipment has a relatively obvious fault trend, and the fault early warning system sends fault early warning information to workers related to the operation equipment so that the workers such as professional engineers and supervisors can take precautions against the operation equipment according to the fault early warning information. In order to further ensure the safety of the currently operating device, the fault early warning system detects whether the degree of outlier deviation exceeds a second preset range to determine whether the operating device has failed, and specifically, the step of performing fault early warning on the operating device according to the degree of outlier deviation further includes:
step S46, detecting whether the maximum outlier deviation of the outlier deviations is greater than a second preset range maximum;
step S47, detecting whether the minimum outlier deviation of the outlier deviations is smaller than the second preset range minimum;
and step S48, if the maximum outlier deviation is greater than the second preset range maximum and/or the minimum outlier deviation is less than the second preset range minimum, determining that the operating equipment has a fault, and sending alarm information.
Furthermore, the fault early warning system is provided with an alarm device, so that when the equipment fault is judged, alarm information is sent out, and a worker is reminded to carry out corresponding processing on the equipment. Further, in order to ensure safe operation of the whole fault early warning system, the fault early warning system detects whether the maximum outlier deviation degree in the outlier deviation degrees is greater than the second preset range maximum value and detects whether the minimum outlier deviation degree in the outlier deviation degrees is less than the second preset range minimum value. Further, if the detected maximum outlier deviation degree is larger than a second preset range maximum value of the maximum acceptable parameter deviation range when the representation equipment normally operates, or the minimum outlier deviation degree is smaller than a second preset range minimum value of the maximum acceptable parameter deviation range when the representation equipment normally operates, the fault early warning system judges that the operation equipment has a fault, and calls an alarm device to send out alarm information to remind a worker to stop the operation of the equipment and maintain the equipment, so that accidents caused by the fact that the equipment operates under the condition of the fault for a long time are avoided, wherein the alarm information can be a flashing alarm lamp or a ringing alarm bell.
Further, the fault early warning system analyzes the numerical values of the plurality of calculation points of the target from the group when the alarm information is sent out according to the fixed interval time, extracts valuable alarm data, and optimizes the second preset range according to the extracted alarm data, so that the accuracy of the alarm information is improved. Furthermore, the fault early warning system collects fault early warning information within a certain time through big data, compares the collected fault early warning information with alarm data, analyzes and obtains regular information of aging or abnormity sent by the operating equipment, and achieves the beneficial effect of intelligent abnormity early warning of the operating equipment.
According to the method, the fault early warning is carried out on the operation equipment through the maximum outlier deviation degree and the minimum outlier deviation degree obtained through calculation, so that the working personnel can know the parameter outlier condition of the operation equipment, check the operation equipment, stop the operation of the equipment and maintain the equipment, and the normal operation of the equipment is guaranteed. In addition, the fault early warning system collects early warning information within a certain time through big data, compares the collected early warning information with the warning information, analyzes and obtains regular information of aging or abnormity sent by the operation equipment, and achieves the beneficial effect of intelligent abnormity early warning of the operation equipment.
Further, referring to fig. 3, a third embodiment of the device fault early warning method based on outlier parameters is provided based on the first embodiment or the second embodiment of the device fault early warning method based on outlier parameters of the present invention, and in the third embodiment, the step of calculating the outlier deviation degree based on the target outlier group includes:
step S31, determining the maximum value of the calculation point in the target outlier as the maximum outlier, and calculating a first average value of the calculation point values except the maximum outlier in the outlier;
step S32, determining the calculation point value with the minimum value in the target outlier as the minimum outlier, and calculating a second average value of all calculation point values except the minimum outlier in the outlier;
step S33, calculating a first difference value based on the maximum outlier and the first average, and obtaining a ratio of the first difference value and the first average to obtain a maximum outlier deviation;
step S34, calculating a second difference value based on the minimum outlier and the second average value, and taking a ratio of the second difference value and the second average value to obtain a minimum outlier deviation.
Further, the fault early warning system extracts the calculation point value with the maximum value in the target outlier, determines the extracted calculation point value with the maximum value as the maximum outlier, and calculates a first average value of all calculation point values except the maximum outlier in the target outlier. Further, the fault early warning system calls calculation software to subtract the first average value from the maximum group value to obtain a first difference value. Further, the fault early warning system performs ratio operation on the first difference value and the first average value to obtain the maximum outlier deviation degree representing the deviation degree of the maximum outlier group and the first average value. Further, the fault early warning system extracts the calculation point value with the minimum value in the target outlier, determines the extracted calculation point value with the minimum value as the minimum outlier, and calculates a second average value of all calculation point values except the minimum outlier in the target outlier. Further, the fault early warning system calls calculation software to subtract the minimum group value from the second average value to obtain a second difference value. Further, the fault early warning system performs ratio operation on the second difference value and the second average value to obtain the minimum outlier deviation degree representing the deviation degree of the minimum outlier group and the second average value.
In this embodiment, the outlier deviation degree including the maximum outlier deviation degree and the minimum outlier deviation degree is calculated through the calculation point values in the target outlier group, so as to perform fault early warning on the operating device according to the outlier deviation degree.
Further, based on the first, second or third embodiment of the method for early warning of an equipment fault based on an outlier of the present invention, a fourth embodiment of the method for early warning of an equipment fault based on an outlier of the present invention is proposed, in the fourth embodiment, the abnormal calculation point values include a bad point value, a limit point value, and a deviation point value; the step of detecting whether an abnormal computation point value exists in the cluster, and if the abnormal computation point value exists, removing the abnormal computation point value from the cluster comprises:
step S21 of detecting whether the group has any one of the bad point numerical value, the limit point numerical value, and the deviation point numerical value, respectively;
step S22, if any one of the bad point numerical value, the limit point numerical value, and the deviation point numerical value exists, removing one of the bad point numerical value, the limit point numerical value, and the deviation point numerical value that has the departure group from the departure group.
Further, the fault early warning system detects whether a bad point numerical value representing an inaccurate numerical value collected due to damage of the detection instrument exists in the plurality of calculation point numerical values forming the separation group. Further, if the bad point value is detected, the bad point value is removed from the outlier, and the accuracy of the calculation result is prevented from being reduced due to the fact that the meaningless bad point value is used for calculation. Further, the fault early warning system detects whether a limit point position numerical value representing a numerical value point, which enables a detection instrument to acquire a fixed numerical value due to the fact that the equipment does not operate, exists in a plurality of calculation point numerical values forming the outlier, specifically, the fault early warning system detects the limit point position numerical value of the operating equipment, and judges whether the calculation point numerical value in the outlier participates in the outlier operation conditionally. For example, when the motor is subjected to fault early warning according to the temperature of the running motor, the limit point position of the motor is in a starting and stopping state of the motor, and it can be understood that if the motor is started, the detected limit point position value of the motor is a positive number, and the motor is judged to participate in the outlier operation; and if the motor is stopped, the detected limit point position numerical value of the motor is a negative number, and the limit point position numerical value is judged to be removed from the outlier operation. Further, if the start-stop state of the motor cannot be acquired, the limit point position may be set to detect whether the motor rotation speed value is greater than a first set value set by a user or whether the motor current value is greater than a second set value set by the user, and if the motor rotation speed value is greater than the first set value set by the user or the motor current value is greater than the second set value set by the user, it is determined that the motor is involved in the outlier operation; if the motor rotating speed value is less than or equal to a first set value set by a user or the motor current value is less than or equal to a second set value set by the user, the point limiting position value is judged to be removed from the outlier operation, and the reduction of the accuracy of the calculation result due to the fact that the point limiting position value corresponding to the meaningless un-operated equipment value is used for calculation is avoided. Further, the fault early warning system detects whether deviation point values representing that the values acquired by the detection instrument deviate from a preset range due to damage of equipment parts exist in the plurality of calculation point values forming the outlier group, and if the deviation point values exist, the deviation point values are removed from the outlier group, so that the accuracy of calculation results is prevented from being reduced due to the fact that the meaningless deviation point values are used for calculation. Abnormal calculation point values such as meaningless bad point values, limit point values, deviation point values and the like in the separation group are removed from the separation group, and therefore calculation is carried out by avoiding the use of meaningless calculation point values, and accuracy of calculation results is improved.
The implementation removes abnormal calculation point values such as meaningless bad point values, limit point values, deviation point values and the like in the separation group from the separation group, so that the accuracy of the calculation result is improved due to the fact that the meaningless calculation point values are not used for calculation.
Further, based on the first, second, third or fourth embodiments of the method for warning device failure based on outlier parameters of the present invention, a fifth embodiment of the method for warning device failure based on outlier parameters of the present invention is provided, in the fifth embodiment, the step of obtaining the target outlier group includes:
step S50, detecting whether the number of the calculated point values in the target group is greater than or equal to a preset threshold;
step S60, if the number of the calculated point values in the target outlier is greater than or equal to a preset threshold, determining that the condition for calculating the outlier deviation is reached, and performing the step of calculating the outlier deviation based on the target outlier;
step S70, if the number of the calculated point values in the target outlier is smaller than a preset threshold, determining that the condition for calculating the outlier deviation is not met, and displaying a prompt message indicating that the calculation has failed.
Further, after the outlier is removed from the calculation point value corresponding to the abnormal point to obtain the target outlier, the fault early warning system detects whether the number of the calculation point values in the target outlier is greater than or equal to a preset threshold, and understandably, the preset threshold is set by a user according to the user's own requirements, and when the number of the calculation point values in the target outlier is greater than or equal to the preset threshold, the calculated outlier deviation degree has a certain early warning significance. Further, if the number of the calculation point values in the detected target outlier group is greater than or equal to the preset threshold value, the fault early warning system judges that the number of the calculation point values in the target outlier group reaches the condition of calculating the outlier deviation degree, and then the step of calculating the outlier deviation degree based on the target outlier group is executed. Further, if the number of the calculation point values in the detected target outlier group is smaller than a preset threshold value, the number of the calculation point values in the target outlier group is judged not to reach the condition of calculating the outlier deviation degree, the fault early warning system displays the prompt information of calculation failure, the next type of equipment fault early warning based on the outlier parameters is carried out, the manpower and material resources are avoided being consumed due to the fact that the result of the meaningless outlier deviation degree is calculated, the efficiency of the equipment fault early warning based on the outlier parameters is improved, and the effectiveness of the equipment fault early warning based on the outlier parameters is guaranteed.
In this embodiment, by detecting whether the number of the calculated point values in the target outlier is greater than or equal to a preset threshold, if so, calculating an outlier deviation according to the plurality of calculated point values in the target outlier; if the deviation degree is smaller than the preset threshold value, the prompt information of the calculation failure is displayed, the manpower and material resources are avoided being consumed due to the fact that the result of the meaningless deviation degree of the outlier is calculated, the efficiency of the equipment fault early warning based on the outlier parameters is improved, and the effectiveness of the equipment fault early warning based on the outlier parameters is guaranteed.
Further, the invention also provides an equipment fault early warning device based on the outlier parameter.
Referring to fig. 4, fig. 4 is a functional module diagram of a device failure early warning apparatus based on outliers according to a first embodiment of the present invention.
The equipment fault early warning device based on the outlier parameter comprises:
the system comprises an acquisition module 10, a processing module and a control module, wherein the acquisition module is used for acquiring the calculation point values of the same type of parameters in one or more running devices in real time and generating a cluster group according to the calculation point values, and the types of the running devices are the same;
the detection module 20 is configured to detect whether an abnormal calculation point value exists in the cluster, and if the abnormal calculation point value exists, remove the abnormal calculation point value from the cluster to obtain a target cluster;
a calculating module 30, configured to calculate an outlier bias based on the target outlier;
and the early warning module 40 is used for carrying out fault early warning on the operating equipment according to the outlier deviation degree.
Further, the detection module 20 includes:
a first detection unit, configured to detect whether any one of the bad point numerical value, the limit point numerical value, and the deviation point numerical value exists in the separate group;
a removing unit, configured to remove one of the bad point numerical value, the limit point numerical value, and the deviation point numerical value from the group if any one of the bad point numerical value, the limit point numerical value, and the deviation point numerical value exists.
Further, the detection module 20 further includes:
the second detection unit is used for detecting whether the number of the calculation point values in the target group is greater than or equal to a preset threshold value or not;
an execution unit, configured to determine that a condition for calculating the outlier deviation is met if the number of the calculated point values in the target outlier is greater than or equal to a preset threshold, and execute a step of calculating the outlier deviation based on the target outlier;
and the display unit is used for judging that the condition of calculating the outlier deviation degree is not met if the number of the calculation point numerical values in the target outlier group is smaller than a preset threshold value, and displaying prompt information of calculation failure.
Further, the calculation module 30 includes:
the first calculating unit is used for determining the value of the calculating point with the largest value in the target outlier as the largest outlier and calculating a first average value of the values of all the calculating points except the largest outlier in the outlier;
the second calculating unit is used for determining the calculation point value with the minimum value in the target outlier as a minimum outlier and calculating a second average value of all calculation point values except the minimum outlier in the outlier;
a third calculating unit, configured to calculate a first difference value based on the maximum outlier and the first average value, and obtain a ratio between the first difference value and the first average value to obtain a maximum outlier deviation;
and the fourth calculating unit is used for calculating a second difference value based on the minimum outlier and the second average value, and making a ratio of the second difference value and the second average value to obtain a minimum outlier deviation degree.
Further, the early warning module 40 includes:
a third detecting unit, configured to detect whether the maximum outlier deviation of the outlier deviations is greater than a first preset range maximum and/or whether the minimum outlier deviation is smaller than a first preset range minimum;
and the first generating unit is used for generating a fault warning report based on the target outlier if the maximum outlier deviation is greater than the first preset range maximum value and/or the minimum outlier deviation is smaller than the first preset range minimum value, so that a worker corresponding to the operating device can check the fault warning report.
Further, the early warning module 40 further includes:
the monitoring unit is used for monitoring a first duration value of which the maximum outlier deviation degree is greater than the maximum value of the first preset range and a second duration value of which the minimum outlier deviation degree is less than the minimum value of the first preset range;
the fourth detection unit is used for detecting whether any one of the first duration value and the second duration value is greater than the preset duration;
and the second generation unit is used for generating fault early warning information and sending the fault early warning information to a worker corresponding to the operating equipment if any one of the first duration value and the second duration value is greater than the preset duration, so that the worker can take precautionary treatment.
Further, the early warning module 40 further includes:
a fifth detecting unit, configured to detect whether the maximum outlier deviation of the outlier deviations is greater than a second preset range maximum;
a sixth detecting unit, configured to detect whether the minimum outlier deviation of the outlier deviations is smaller than the second preset range minimum;
and the judging unit is used for judging that the running equipment has a fault and sending alarm information if the maximum outlier deviation degree is greater than the second preset range maximum value and/or the minimum outlier deviation degree is less than the second preset range minimum value.
In addition, the present invention also provides a medium, preferably a computer readable storage medium, on which an equipment failure early warning program based on an outlier parameter is stored, where the equipment failure early warning program based on the outlier parameter implements the steps of the embodiments of the equipment failure early warning method based on the outlier parameter when being executed by a processor.
In the embodiments of the device failure early warning apparatus and medium based on outlier parameters of the present invention, all technical features of the embodiments of the device failure early warning method based on outlier parameters are included, and the description and explanation contents are substantially the same as those of the embodiments of the device failure early warning method based on outlier parameters, and are not repeated herein.
It should be noted that, in this document, 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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or a part contributing to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk), and includes a plurality of instructions for enabling a terminal device (which may be a fixed terminal, such as an internet of things smart device including smart homes, such as a smart air conditioner, a smart lamp, a smart power supply, a smart router, etc., or a mobile terminal, including a smart phone, a wearable networked AR/VR device, a smart sound box, an autonomous driving automobile, etc.) to execute the method according to each embodiment of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An equipment fault early warning method based on outlier parameters is characterized by comprising the following steps:
obtaining calculation point values of the same type of parameters in one or more running devices in real time, and generating a cluster group according to the calculation point values, wherein the running devices are the same in type;
detecting whether an abnormal calculation point value exists in the cluster, if so, removing the abnormal calculation point value from the cluster to obtain a target cluster;
calculating an outlier bias based on the target outlier, the outlier bias comprising a maximum outlier bias and a minimum outlier bias;
carrying out fault early warning on the operating equipment according to the outlier deviation degree;
wherein the step of calculating an outlier bias based on the target outlier comprises:
determining the number value of the calculation point with the largest value in the target outlier as the maximum outlier, and calculating a first average value of the number values of all the calculation points except the maximum outlier in the outlier;
determining the calculation point value with the minimum value in the target outlier as a minimum outlier, and calculating a second average value of all calculation point values except the minimum outlier in the outlier;
calculating a first difference value based on the maximum outlier and the first average value, and making a ratio of the first difference value and the first average value to obtain a maximum outlier deviation degree;
and calculating a second difference value based on the minimum outlier and the second average value, and making a ratio of the second difference value to the second average value to obtain a minimum outlier deviation degree.
2. The method of claim 1, wherein the step of fault early warning the operating device according to the degree of outlier deviation comprises:
detecting whether the maximum outlier deviation degree in the outlier deviation degrees is larger than a first preset range maximum value and/or whether the minimum outlier deviation degree is smaller than a first preset range minimum value;
and if the maximum outlier deviation degree is greater than the first preset range maximum value and/or the minimum outlier deviation degree is smaller than the first preset range minimum value, generating a fault warning report based on the target outlier so that a worker corresponding to the operating device can check the fault warning report.
3. The method of claim 2, wherein the step of fault early warning the operating device according to the degree of outlier deviation further comprises:
monitoring a first duration value that the maximum outlier deviation is greater than the maximum value of the first preset range and a second duration value that the minimum outlier deviation is less than the minimum value of the first preset range;
detecting whether any one of the first duration value and the second duration value is greater than a preset duration;
and if any one of the first duration value and the second duration value is greater than the preset duration, generating fault early warning information and sending the fault early warning information to a worker corresponding to the operating equipment so as to enable the worker to perform precaution processing.
4. The method of claim 1, wherein the step of fault early warning the operating device according to the degree of outlier deviation further comprises:
detecting whether the maximum outlier deviation in the outlier deviations is greater than a second preset range maximum;
detecting whether the minimum outlier deviation in the outlier deviations is smaller than the second preset range minimum;
and if the maximum outlier deviation degree is greater than the second preset range maximum value and/or the minimum outlier deviation degree is less than the second preset range minimum value, judging that the operating equipment fails, and sending alarm information.
5. The outlier parameter-based equipment fault early warning method of claim 1, wherein the abnormal calculation point values comprise a bad point value, a limit point value, a deviation point value; the step of detecting whether an abnormal computation point value exists in the cluster, and if the abnormal computation point value exists, removing the abnormal computation point value from the cluster comprises:
respectively detecting whether any one of the dead pixel numerical value, the limit point numerical value and the deviation point numerical value exists in the separation group;
if any one of the bad point numerical value, the limit point numerical value and the deviation point numerical value exists, removing one of the bad point numerical value, the limit point numerical value and the deviation point numerical value which has the separation group from the separation group.
6. The outlier parameter based device fault warning method of claim 1, wherein said step of obtaining a target outlier is followed by the step of:
detecting whether the number of the calculation point values in the target group is greater than or equal to a preset threshold value;
if the number of the calculation point values in the target outlier group is greater than or equal to a preset threshold value, judging that the condition for calculating the outlier deviation degree is achieved, and executing the step of calculating the outlier deviation degree based on the target outlier group;
and if the number of the numerical values of the calculation points in the target outlier group is smaller than a preset threshold value, judging that the condition of calculating the outlier deviation degree is not met, and displaying prompt information of calculation failure.
7. The utility model provides an equipment trouble early warning device based on outlier parameter which characterized in that, equipment trouble early warning device based on outlier parameter includes:
the acquisition module is used for acquiring the calculation point values of the same type of parameters in one or more running devices in real time and generating a cluster group according to the calculation point values, wherein the running devices are the same in type;
the detection module is used for detecting whether an abnormal calculation point value exists in the cluster, and if the abnormal calculation point value exists, the abnormal calculation point value is removed from the cluster to obtain a target cluster;
a calculation module for calculating an outlier bias based on the target outlier;
the early warning module is used for carrying out fault early warning on the operating equipment according to the outlier deviation degree;
the calculating module is further configured to determine a maximum outlier from the maximum computation point values in the target outlier, and calculate a first average of all the computation point values in the outlier except the maximum outlier; determining the calculation point value with the minimum value in the target outlier as a minimum outlier, and calculating a second average value of all calculation point values except the minimum outlier in the outlier; calculating a first difference value based on the maximum outlier and the first average value, and making a ratio of the first difference value and the first average value to obtain a maximum outlier deviation degree; and calculating a second difference value based on the minimum outlier and the second average value, and making a ratio of the second difference value to the second average value to obtain a minimum outlier deviation degree.
8. An outlier parameter based device fault early warning device, comprising a memory, a processor and an outlier parameter based device fault early warning program stored on the memory and executable on the processor, wherein the outlier parameter based device fault early warning program when executed by the processor implements the steps of the outlier parameter based device fault early warning method as claimed in any of the claims 1-6.
9. A medium having stored thereon an outlier parameter based device failure warning program, which when executed by a processor performs the steps of the outlier parameter based device failure warning method as claimed in any of the claims 1-6.
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| CN116106465B (en) * | 2023-04-11 | 2023-06-16 | 华谱科仪(北京)科技有限公司 | Chromatograph fault diagnosis method and device based on parameter analysis |
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| US7620523B2 (en) * | 2007-04-30 | 2009-11-17 | Integrien Corporation | Nonparametric method for determination of anomalous event states in complex systems exhibiting non-stationarity |
| CN101509839A (en) * | 2009-03-12 | 2009-08-19 | 上海交通大学 | Cluster industrial robot failure diagnosis method based on outlier excavation |
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