CN117369392A - Equipment fault intelligent early warning method based on multiparameter logic relation - Google Patents
Equipment fault intelligent early warning method based on multiparameter logic relation Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses an intelligent equipment fault early warning method based on a multiparameter logic relation, which relates to the technical field of equipment fault early warning.
Description
Technical Field
The invention relates to the technical field of equipment fault early warning, in particular to an intelligent equipment fault early warning method based on a multiparameter logic relationship.
Background
The DCS control system is an abbreviation for distributed control system (Distributed Control System), which is a control system for centralized and automated management of industrial processes. DCS control systems typically consist of a number of remote computer-based control units connected together by a network to monitor and control various devices and components in an industrial process;
the DCS control system uses a modern computer technology and a communication technology, provides functions of real-time monitoring, operation scheduling, data processing, automatic control and the like, is used for controlling and managing equipment, flow parameters and operation instructions in an industrial process, and is widely applied to various industries such as petrifaction, electric power, water treatment, pharmacy, steel and the like based on the functions;
the existing intelligent early warning method for equipment faults in petrochemical industry carries out fault early warning on industrial equipment based on a DCS control system, however, when the monitoring value of an instrument and an instrument of the DCS control system is obviously different from the actual value of the instrument and the instrument, the DCS control system is stopped to solve the problem of post-treatment of the equipment faults, so that the time of handling pre-fault equipment by management personnel is influenced, and the early warning of the equipment faults has no obvious substantial effect;
in the prior art, a prediction model is adopted to predict the faults based on the faults of equipment, then the prediction model is aimed at the faults of the equipment, and in order to ensure normalization, training data used for model training is data taken from a plurality of petrochemical factories, so that the fault processing capacity of each petrochemical factory is not combined for carrying out priority assessment on the faults of the equipment, and the processing efficiency of pre-fault equipment is not optimized;
in order to solve the above problems, the present invention proposes a solution.
Disclosure of Invention
The invention aims to provide an intelligent equipment fault early warning method based on a multiparameter logic relation, which aims to solve the problem that a DCS control system is stopped to solve the post-treatment problem of equipment faults only when the monitoring value of an instrument and an instrument are obviously different from the actual value of the instrument and the instrument in the prior art, so that the time of processing pre-fault equipment by management personnel can be influenced, and the early warning of the equipment faults has no obvious substantial effect; in the prior art, a prediction model is adopted to predict the faults based on the faults of equipment, and the fault processing capability of each petrochemical factory is not combined by the priority rating of the faults aiming at the faults of the equipment, so that the processing efficiency of the pre-fault equipment cannot be optimized.
The aim of the invention can be achieved by the following technical scheme:
an intelligent equipment fault early warning method based on a multiparameter logic relation comprises the following steps:
step one: the data analysis module is used for analyzing the fault record data of all industrial equipment which have faults in the past of the petrochemical plant and is stored in the fault data recording unit, and obtaining corresponding maintenance response characteristics of all fault types which have at least P4 times in the past of the petrochemical plant based on the maintenance processing time and the response time of each fault type when each fault type is in the past;
step two: the equipment characteristic acquisition module acquires measurement data of all industrial equipment characteristic parameters in the petrochemical plant in the current equipment diagnosis period to generate characteristic measurement data of the current equipment diagnosis period, and transmits the characteristic measurement data to the model prediction unit;
step three: the model prediction unit inputs the received characteristic measurement data of the current equipment diagnosis period into an equipment fault prediction model to obtain prediction result data of the current equipment diagnosis period, and transmits the prediction result data of the current equipment diagnosis period to the joint prediction unit, wherein the prediction result data of the current equipment diagnosis period comprises a prediction fault type;
step four: the joint prediction unit searches whether all the prediction fault types contained in the prediction result data of the current equipment diagnosis period have corresponding maintenance response characteristics or not after receiving the prediction result data of the current equipment diagnosis period, obtains the joint priority corresponding to each prediction fault type based on the search result, generates balanced prediction result data of the current equipment diagnosis period according to the joint priority, and transmits the balanced prediction result data to the picture configuration module;
step five: the picture configuration module displays the forms of models for all industrial equipment in the petrochemical plant to management staff in the petrochemical plant, establishes an equipment operation model based on the operation states of all industrial equipment in the petrochemical plant, simulates the operation states of the industrial equipment, and adjusts the model states of the corresponding industrial equipment to be red flickering states after receiving balance prediction result data of the current equipment diagnosis period transmitted by the combined prediction unit.
Further, the data analysis module obtains the corresponding maintenance response characteristics of the petrochemical plant according to the maintenance processing time and the response time of each fault type in each fault in the past for all fault types which have occurred at least P4 times in the past, and the specific analysis steps are as follows:
s21: for one fault type that has occurred at least P4 times in the past in petrochemical plants, the formula is usedCalculating and acquiring maintenance response characteristics C1 of the fault type, wherein n refers to the total number of times that the fault type appears in the past, A1, A2, aa refer to corresponding maintenance processing time of the fault type when each occurrence happens in the past, B1, B2, ba refer to corresponding response time of the fault type when each occurrence happens in the past, namely the interval time from the fault equipment discovery by maintenance personnel of the petrochemical factory to the fault equipment departure is equal to the preset value, and alpha 1 and alpha 2 are respectively;
s22: and (3) respectively calculating and acquiring maintenance response characteristics C1, C2, cc and C more than or equal to 1 of all fault types of the petrochemical factory which at least occur P4 times in the past according to S21, wherein P4 is a preset diagnostic analysis frequency threshold value.
The invention has the beneficial effects that:
(1) According to the invention, the monitoring data on the instruments and meters of the monitoring industrial equipment are collected through the fault characteristic collection module, the equipment fault is predicted by the equipment fault prediction model based on the characteristics of the monitoring data of the instruments and meters, the post-treatment condition of the equipment fault is prevented from being stopped when the monitoring value of the instruments and meters is obviously different from the actual value of the suspected equipment fault based on the equipment fault prediction model, and maintenance personnel are further given time for processing the pre-fault equipment, so that the early warning function of the equipment fault is improved;
(2) According to the invention, the data analysis module is used for obtaining the corresponding maintenance response characteristic of each fault type according to the maintenance processing time and the response time of each fault type when the fault occurs at least P4 times in the past for the petrochemical plant, and the data analysis module is combined with the priority of the fault prediction type assessed in the equipment fault prediction model, so that the final combined priority of the prediction equipment comprises the response speed and the processing capacity of the petrochemical plant for each fault type, the final combined priority of the prediction equipment has the characteristics of the petrochemical plant, and the processing efficiency of the prediction equipment is improved relative to the petrochemical plant.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an intelligent equipment fault early warning method based on a multi-parameter logic relationship is realized according to an intelligent equipment fault early warning based on a multi-parameter logic relationship, and as shown in fig. 2, the system comprises an equipment characteristic acquisition module, an early warning combination module, a picture configuration module and a data analysis module;
the equipment characteristic acquisition module is used for periodically acquiring measurement data of characteristic parameters of industrial equipment in the petrochemical plant, and the industrial equipment particularly refers to industrial equipment fixedly provided with instruments and meters;
the characteristic parameters of the industrial equipment at least comprise one or more of temperature, pressure, flow, humidity, speed, acceleration, vibration, voltage, current, power, resistance and capacitance; the measurement data of the characteristic parameters of the industrial equipment refer to measurement data obtained by measuring the corresponding characteristic parameters by an instrument and meter equipment fixedly arranged on the industrial equipment, wherein the measurement data comprises equipment numbers of the industrial equipment, and the equipment numbers of the industrial equipment are character strings formed by 16-bit digits;
the equipment characteristic acquisition module acquires measurement data of all industrial equipment characteristic parameters in a petrochemical plant in a current equipment diagnosis period, generates characteristic measurement data of the current equipment diagnosis period according to the measurement data, and transmits the characteristic measurement data to the early warning combined module, wherein the interval time of one equipment diagnosis period is P1, and the P1 is a preset equipment fault diagnosis interval time threshold;
the picture configuration module is used for displaying the form of the models for all the industrial equipment in the petrochemical plant to management staff in the petrochemical plant, and the picture configuration module establishes an equipment operation model based on the operation states of all the industrial equipment in the petrochemical plant to simulate the operation states of the industrial equipment, wherein the fact that the industrial equipment which does not have faults is normally in a static display state is required to be described;
the early warning combined module is used for early warning the faults of industrial equipment in the petrochemical factory and comprises a fault data recording unit, a model prediction unit and a combined prediction unit;
the fault data recording unit is used for storing fault record data of all industrial equipment which have faults in the past of the petrochemical plant, wherein the fault record data comprise fault types, fault disposal strategies corresponding to the fault types, fault-causing characteristic parameters, measurement data of the fault-causing characteristic parameters and predicted fault processing time length in P2 time before the occurrence of the faults, and the P2 is a preset fault backtracking time threshold;
the model prediction unit stores an equipment fault prediction model for performing industrial equipment fault prediction, the early warning joint module receives the characteristic measurement data of the current equipment diagnosis period transmitted by the equipment characteristic acquisition module and inputs the characteristic measurement data into the model prediction unit, and the model prediction unit inputs the characteristic measurement data into the equipment fault prediction model to obtain prediction result data of the current equipment diagnosis period;
the predicted result data of the current equipment diagnosis period comprises a predicted fault equipment number, a predicted fault type, a corresponding fault treatment strategy, a fault-causing characteristic parameter and a fault priority;
the fault priority is represented by a number, the range of the fault priority is from the number 1 to P3, the P3 is a preset priority threshold value, the smaller the number is, the higher the fault priority is, and the higher the fault priority is, the more the treatment of the equipment is prioritized;
the model prediction unit transmits prediction result data of the current equipment diagnosis period to the joint prediction unit;
the combined prediction unit receives the prediction result data of the current equipment diagnosis period transmitted by the model prediction unit and generates balanced prediction result data of the current diagnosis period according to a certain balanced diagnosis rule, and the method specifically comprises the following steps:
s11: based on all fault types stored in the joint prediction unit of one prediction fault type carried in the current equipment diagnosis period prediction result data, whether matching exists or not is carried out;
s12: if the predicted fault type exists, a maintenance response characteristic D1 corresponding to the matched fault type is obtained, and a joint priority F1 corresponding to the predicted fault type is obtained through calculation by using a formula F1=E1×β1+D1×β2, wherein E1 is a fault priority corresponding to the predicted fault type, and β1 and β2 are preset first joint fault priority adjusting factors;
s13: if not, then use the formulaCalculating and obtaining a joint priority F1 corresponding to the predicted fault type, wherein P6 is a preset normal fault constant, and lambda 1 is a preset second joint fault priority adjustment factor;
s14: calculating and obtaining the joint priorities corresponding to all the predicted fault types carried in the predicted result data of the current equipment diagnosis period according to S11 to S13, and sequentially and re-marking the joint priorities as G1, G2, gg according to the values from small to large, wherein G is more than or equal to 1;
the combined prediction unit generates prediction alarm data of the current equipment diagnosis period according to the combined priorities G1, G2, G and the current equipment diagnosis period prediction result data corresponding to all the prediction fault types carried in the current equipment diagnosis period prediction result data and transmits the prediction alarm data to the picture configuration module;
the picture configuration module receives the prediction alarm data of the current equipment diagnosis period transmitted by the joint prediction unit, finds corresponding industrial equipment in the generated equipment operation model based on all the prediction fault equipment numbers carried in the prediction alarm data, adjusts the display state of the industrial equipment to be red flashing state, and marks the prediction fault type of the prediction fault equipment, the corresponding fault treatment strategy and the characteristic parameters causing faults on the side of the prediction fault equipment in red;
then, the joint priorities G1, G2, G.and G corresponding to all the predicted fault types carried in the predicted alarm data of the current equipment diagnosis period are sequentially arranged and displayed to a manager of the petrochemical plant from a large order to a large order according to the order, and maintenance staff is arranged to check and maintain the predicted fault equipment, wherein the smaller the joint priorities are in value, the more preferentially the corresponding predicted fault equipment is processed;
the data analysis module is used for periodically analyzing the fault record data of all industrial equipment which have faults in the past of the petrochemical industry and are stored in the fault data recording unit, and the analysis steps are as follows:
s21: for one fault type that has occurred at least P4 times in the past in petrochemical plants, the formula is usedCalculating and acquiring maintenance response characteristics C1 of the fault type, wherein n refers to the total number of times that the fault type appears in the past, A1, A2, aa refer to corresponding maintenance processing time of the fault type when each occurrence happens in the past, B1, B2, ba refer to corresponding response time of the fault type when each occurrence happens in the past, namely the interval time from the fault equipment discovery by maintenance personnel of the petrochemical factory to the fault equipment departure is equal to the preset value, and alpha 1 and alpha 2 are respectively;
s22: respectively calculating and acquiring maintenance response characteristics C1, C2, cc, C is more than or equal to 1 of all fault types of the petrochemical factory which at least appear P4 times in the past according to S21, wherein P4 is a preset diagnostic analysis frequency threshold;
the data analysis module transmits maintenance response characteristics C1, C2, cc of all fault types of the petrochemical plant, which have occurred at least P4 times in the past, to the joint prediction unit for updating and storing;
in the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (9)
1. An intelligent equipment fault early warning method based on a multiparameter logic relationship is characterized by comprising the following steps:
step one: the data analysis module is used for analyzing the fault record data of all industrial equipment which have faults in the past of the petrochemical plant and is stored in the fault data recording unit, and obtaining corresponding maintenance response characteristics of all fault types which have at least P4 times in the past of the petrochemical plant based on the maintenance processing time and the response time of each fault type when each fault type is in the past;
step two: the equipment characteristic acquisition module acquires measurement data of all industrial equipment characteristic parameters in the petrochemical plant in the current equipment diagnosis period to generate characteristic measurement data of the current equipment diagnosis period, and transmits the characteristic measurement data to the model prediction unit;
step three: the model prediction unit inputs the received characteristic measurement data of the current equipment diagnosis period into an equipment fault prediction model to obtain prediction result data of the current equipment diagnosis period, and transmits the prediction result data of the current equipment diagnosis period to the joint prediction unit, wherein the prediction result data of the current equipment diagnosis period comprises a prediction fault type;
step four: the joint prediction unit searches whether all the prediction fault types contained in the prediction result data of the current equipment diagnosis period have corresponding maintenance response characteristics or not after receiving the prediction result data of the current equipment diagnosis period, obtains the joint priority corresponding to each prediction fault type based on the search result, generates balanced prediction result data of the current equipment diagnosis period according to the joint priority, and transmits the balanced prediction result data to the picture configuration module;
step five: the picture configuration module displays the forms of models for all industrial equipment in the petrochemical plant to management staff in the petrochemical plant, establishes an equipment operation model based on the operation states of all industrial equipment in the petrochemical plant, simulates the operation states of the industrial equipment, and adjusts the model states of the corresponding industrial equipment to be red flickering states after receiving balance prediction result data of the current equipment diagnosis period transmitted by the combined prediction unit.
2. The intelligent equipment fault early warning method based on the multi-parameter logic relation according to claim 1, wherein the industrial equipment is especially industrial equipment fixedly provided with instruments and meters, and the characteristic parameters of the industrial equipment at least comprise one or more of temperature, pressure, flow, humidity, speed, acceleration, vibration, voltage, current, power, resistance and capacitance.
3. The intelligent equipment fault early warning method based on the multi-parameter logic relation according to claim 2, wherein the measured data of the characteristic parameters of the industrial equipment refer to measured data obtained by measuring the corresponding characteristic parameters by an instrument and meter equipment fixedly installed on the intelligent equipment, the measured data comprise equipment numbers of the industrial equipment, and the equipment numbers of the industrial equipment are character strings formed by 16-bit digits.
4. The intelligent equipment fault early warning method based on the multiparameter logic relation according to claim 1, wherein fault data record data of all industrial equipment faults occurring in the past of the petrochemical plant are stored in the fault data record unit, the fault record data comprise fault types and corresponding fault treatment strategies, fault-causing characteristic parameters, measured data of the fault-causing characteristic parameters, maintenance processing time and response time in P2 time before the fault occurs, and the P2 is a preset fault backtracking time threshold.
5. The intelligent equipment fault early warning method based on the multiparameter logic relation according to claim 1, wherein the specific analysis steps of the data analysis module for all fault types of the petrochemical plant, which have occurred at least P4 times in the past, based on the maintenance processing time and the response time of each fault type in the past each fault are as follows:
s21: for one fault type that has occurred at least P4 times in the past in petrochemical plants, the formula is usedCalculating and acquiring maintenance response characteristics C1 of the fault type, wherein n refers to the total number of times that the fault type appears in the past, A1, A2, aa refer to corresponding maintenance processing time of the fault type when each occurrence happens in the past, B1, B2, ba refer to corresponding response time of the fault type when each occurrence happens in the past, namely the interval time from the fault equipment discovery by maintenance personnel of the petrochemical factory to the fault equipment departure is equal to the preset value, and alpha 1 and alpha 2 are respectively;
s22: and (3) respectively calculating and acquiring maintenance response characteristics C1, C2, cc and C more than or equal to 1 of all fault types of the petrochemical factory which at least occur P4 times in the past according to S21, wherein P4 is a preset diagnostic analysis frequency threshold value.
6. The intelligent equipment fault early warning method based on the multi-parameter logic relation according to claim 1, wherein the picture configuration module sets a model state of industrial equipment which does not have faults to a static state.
7. The intelligent early warning method for equipment faults based on the multiparameter logic relation according to claim 1, wherein the predicted result data of the current equipment diagnosis period further comprises a predicted fault equipment number, a corresponding fault treatment strategy, characteristic parameters causing faults and fault priorities.
8. The intelligent equipment fault early warning method based on the multi-parameter logic relation according to claim 1, wherein the joint prediction unit obtains the joint priority corresponding to each prediction fault type, and the method is specifically as follows:
s11: based on all fault types stored in the joint prediction unit of one prediction fault type carried in the current equipment diagnosis period prediction result data, whether matching exists or not is carried out;
s12: if the predicted fault type exists, a maintenance response characteristic D1 corresponding to the matched fault type is obtained, and a joint priority F1 corresponding to the predicted fault type is obtained through calculation by using a formula F1=E1×β1+D1×β2, wherein E1 is a fault priority corresponding to the predicted fault type, and β1 and β2 are preset first joint fault priority adjusting factors;
s13: if not, then use the formulaCalculating and obtaining a joint priority F1 corresponding to the predicted fault type, wherein P6 is a preset normal fault constant, and lambda 1 is a preset second joint fault priority adjustment factor;
s14: calculating and obtaining the joint priorities corresponding to all the predicted fault types carried in the predicted result data of the current equipment diagnosis period according to S11 to S13, and sequentially and re-marking the joint priorities as G1, G2, gg according to the values from small to large, wherein G is more than or equal to 1;
and the joint prediction unit predicts the joint priorities G1, G2, gg corresponding to all the predicted fault types carried in the result data according to the current equipment diagnosis period.
9. The intelligent early warning method for equipment faults based on the multiparameter logic relation according to claim 1, wherein the higher the combination priority is, the shorter the maintenance time of the industrial equipment corresponding to the predicted fault type is.
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| CN116279700A (en) * | 2023-03-27 | 2023-06-23 | 卡斯柯信号有限公司 | Method for realizing fault early warning and management of electronic related equipment of railway signal system |
| CN116720324A (en) * | 2023-05-15 | 2023-09-08 | 中铁第四勘察设计院集团有限公司 | Traction substation key equipment fault early warning method and system based on prediction model |
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