Factory area abnormal data monitoring system based on integrated energy operation and maintenance

Abstract

A factory area abnormal data monitoring system based on integrated energy operation and maintenance. The present invention relates to the technical field of abnormality monitoring. Disclosed is a factory area abnormal data monitoring system based on integrated energy operation and maintenance, the system comprising a plurality of production devices. The monitoring system further comprises a monitoring module, a maintenance information module, and an analysis module, wherein the monitoring module monitors each production device, and acquires and stores actual production data; the maintenance information module stores maintenance data of each production device; and then the analysis module performs analysis on the basis of the actual production data and the maintenance data, so as to determine whether the production device is faulty, and when a determination result indicates that the production device is faulty, the analysis module acquires a fault state index of each faulty production device according to a preset rule, and performs maintenance sorting on the faulty device on the basis of the fault state index, such that when a plurality of production devices are abnormal, an administrator can perform maintenance on the basis of fault evaluation values and according to a maintenance sequence, and preferentially arrange for the repair of a production device having smaller faults.

Description

A plant area abnormal data monitoring system for comprehensive energy operation and maintenance TECHNICAL FIELD

[0001] The present application relates to the technical field of abnormal monitoring, in particular to a plant area abnormal data monitoring system for comprehensive energy operation and maintenance. BACKGROUND

[0002] With the improvement of factory automation level, many large factories produce continuously for 24 hours, and a large amount of electric energy is needed in the production process. In order to improve energy utilization, low-cost comprehensive energy is usually used to provide stable electric energy for the factory.

[0003] There are many production devices in a large factory. Management personnel monitor the production situation and device situation of the production devices by setting sensors on the production devices. When the production situation and device situation are abnormal, a warning is sent to let the staff repair.

[0004] However, since there are many production devices in a large factory and the number of maintenance personnel is limited, when multiple production devices appear abnormal at the same time, the staff cannot directly see the fault degree, and the abnormal production devices need to be repaired one by one. Moreover, each repair is randomly selected, and if the fault to be repaired first is complex and takes more time, the production progress will be delayed. SUMMARY

[0005] The purpose of the present application is to provide a plant area abnormal data monitoring system for comprehensive energy operation and maintenance, which solves the following technical problems:

[0006] How to make the repair order of the production devices that appear abnormal at the same time more reasonable.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] A plant area abnormal data monitoring system for comprehensive energy operation and maintenance, comprising a plurality of production devices, the monitoring system further comprising:

[0009] A monitoring module for monitoring each production device, acquiring and storing actual production data;

[0010] A repair information module for storing repair data of each production device;

[0011] An analysis module connected with the monitoring module and the repair information module, for analyzing the actual production data and the repair data, judging whether the production device has a fault, and when the result of the judgment is that there is a fault, obtaining a fault state index of each fault production device according to a preset rule, and performing repair sorting on the fault device according to the fault state index.

[0012] As a further scheme of the present application, the actual production data comprises actual temperature variation curve of the production equipment on the day, actual power consumption on the day, actual working time on the day and actual production quantity on the day.

[0013] As a further scheme of the present application, the maintenance data comprises maintenance frequency and failure index of each maintenance.

[0014] As a further scheme of the present application, the preset rule for obtaining the failure state index is:

[0015] S1: obtaining actual state value of the production equipment by analyzing actual temperature variation curve, actual power consumption, actual working time and actual production quantity of the past preset days;

[0016] S2: obtaining actual cumulative time by the monitoring module;

[0017] S3: obtaining failure probability index according to actual cumulative time, maintenance frequency and failure index of each maintenance of the production equipment;

[0018] S4: judging whether the production equipment has failure according to actual state value and failure probability index of the production equipment; when the result of the judgment is failure, obtaining failure state index of the failure equipment.

[0019] As a further scheme of the present application, the state value of the production equipment is obtained by the formula:

[0020]

[0021] calculating actual state value R of the production equipment S ;

[0022] wherein, N is the past preset days, n∈N; t n is working time of the past n day; R0 is standard state value of the production equipment; t0 is standard working time of the production equipment; T0(t)) is standard temperature curve of the standard working time of the production equipment in the standard state value; T(t)) is real-time temperature curve of the production equipment; n is power consumption of the past n day; S0 is standard power consumption of the standard working time of the production equipment in the standard state value; Q n is production quantity of the past n day; Q0 is standard production quantity of the standard working time of the production equipment in the standard state value; ε is unit coefficient; C is preset constant; γ1 is first weight coefficient; γ2 is second weight coefficient; γ3 is third weight coefficient.

[0023] As a further scheme of the present application, the failure probability index is obtained by the formula:

[0024]

[0025] Wherein, A S is the failure probability index; L A is the expected cumulative length of use of the production equipment; L S is the actual cumulative length of the production equipment; L0 is the preset maintenance cycle length of the production equipment; M is the actual maintenance times of the production equipment, m∈M; B m is the failure severity index of the production equipment in the mth maintenance; α is the first adjustment coefficient; β is the second adjustment coefficient; δ1 is the fourth weight coefficient; δ2 is the fifth weight coefficient; A0 is the preset failure index.

[0026] As a further scheme of the present application: the actual state value R S is compared with the first preset value R1 and the second preset value R2; the failure probability index A S is compared with the third preset value A1.

[0027] When R S <R1, the production equipment is normal.

[0028] When R1<R S <R2, A S <A1, the production equipment is normal.

[0029] When R1<R S <R2, A S ≥A1, the production equipment has a slight failure.

[0030] When R2<R S , A S <A1, the production equipment is scrapped.

[0031] When R2<R S , A S ≥A1, the production equipment has a serious failure.

[0032] As a further scheme of the present application: the failure state index is obtained by the formula to obtain the failure state index W.

[0033] The present application has the following beneficial effects:

[0034] The application stores the maintenance data of each production equipment through the maintenance information module; then the analysis module analyzes according to the actual production data and the maintenance data to judge whether the production equipment has a fault, and when the result of the judgment is that there is a fault, the fault state index of each fault production equipment is obtained according to the preset rule, and the fault equipment is sorted according to the fault state index; so that the administrator can repair according to the fault evaluation value and the repair order when multiple production equipments have an exception, and the production equipment with less fault is repaired preferentially. BRIEF DESCRIPTION OF DRAWINGS

[0035] The application will be further described below in combination with the drawings.

[0036] Fig. 1 is a system module framework diagram of an embodiment of the application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the application will be apparently and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0038] Please refer to Fig. 1, in an embodiment, a plant area abnormal data monitoring system for comprehensive energy operation and maintenance is provided, comprising multiple production equipments;

[0039] A monitoring module is used to monitor each production equipment, acquire and store actual production data;

[0040] A maintenance information module is used to store the maintenance data of each production equipment;

[0041] An analysis module is connected with the monitoring module and the maintenance information module, and is used to analyze according to the actual production data and the maintenance data to judge whether the production equipment has a fault, and when the result of the judgment is that there is a fault, the fault state index of each fault production equipment is obtained according to the preset rule, and the fault equipment is sorted according to the fault state index;

[0042] Through the above technical solution, in the embodiment, each production equipment is monitored by the monitoring module to acquire and store actual production data; the maintenance data of each production equipment is stored by the maintenance information module; then the analysis module analyzes according to the actual production data and the maintenance data to judge whether the production equipment has a fault, and when the result of the judgment is that there is a fault, the fault state index of each fault production equipment is obtained according to the preset rule, and the fault equipment is sorted according to the fault state index; so that the administrator can repair according to the fault evaluation value and the repair order when multiple production equipments have an exception, and the production equipment with less fault is repaired preferentially.

[0043] As an embodiment of the present application, the actual production data includes actual temperature change curve of the production equipment on the day, actual power consumption on the day, actual working time on the day and actual production quantity on the day;

[0044] Through the above technical solution, the actual temperature change curve of the production equipment on the day, the actual power consumption on the day, the actual working time on the day and the actual production quantity on the day are obtained by setting temperature sensors, electric meters and counters on each production equipment.

[0045] As an embodiment of the present application, the maintenance data includes the number of maintenance and the failure index of each maintenance;

[0046] The preset rule for obtaining the failure state index is:

[0047] S1: obtaining the actual state value of the production equipment by analyzing the actual temperature change curve, the actual power consumption, the actual working time and the actual production quantity of the past preset number of days;

[0048] S2: obtaining the actual cumulative time by the monitoring module;

[0049] S3: obtaining the failure probability index according to the actual cumulative time, the number of maintenance and the failure index of each maintenance of the production equipment;

[0050] S4: judging whether the production equipment has failure according to the actual state value and the failure probability index of the production equipment; when the result of the judgment is failure, obtaining the failure state index of the failure equipment;

[0051] Through the above technical solution, the actual state value of the production equipment is obtained by analyzing the actual temperature change curve, the actual power consumption, the actual working time and the actual production quantity of the past preset number of days; the actual cumulative time is obtained by the monitoring module; the failure probability index is obtained according to the actual cumulative time, the number of maintenance and the failure index of each maintenance of the production equipment; whether the production equipment has failure is judged according to the actual state value and the failure probability index of the production equipment; when the result of the judgment is failure, the failure state index of the failure equipment is obtained; the production equipment is judged by the actual state value and the failure probability index, the reason of the abnormal production equipment can be accurately judged; the production equipment which does not need to be repaired is screened out.

[0052] As an embodiment of the present application, the state value of the production equipment is obtained by the formula:

[0053]

[0054] Calculate the actual state value R of the production equipment S ;

[0055] Wherein, N is the past preset days, n∈N; t n is the working time of the past n days; R0 is the standard state value of the production equipment; t0 is the standard working time of the production equipment; T0(t)) is the standard temperature curve of the standard working time of the production equipment in the standard state value; T(t)) is the real-time temperature curve of the production equipment; S n is the power consumption of the past n days; S0 is the standard power consumption of the standard working time of the production equipment in the standard state value; Q n is the production of the past n days; Q0 is the standard production of the standard working time of the production equipment in the standard state value; ε is the unit coefficient; C is the preset constant; γ1 is the first weight coefficient; γ2 is the second weight coefficient; γ3 is the third weight coefficient;

[0056] Through the above technical scheme, the embodiment calculates the actual state value R of the production equipment S ; is the actual cumulative temperature of the production equipment of the past n days; is the actual cumulative temperature of the past preset days N; is the cumulative working time of the past preset days N; is the actual average temperature per unit time of the past preset days N; is the preset average temperature per unit time; is the average temperature difference between the actual average temperature and the preset average temperature; when , it indicates that the actual average temperature is higher than the preset average temperature, the greater the absolute value of the average temperature difference, the greater the actual state value R S ; when , it indicates that the actual average temperature is lower than the preset average temperature, the greater the absolute value of the average temperature difference, the smaller the actual state value R S ; therefore, the greater the actual state value R S indicates that the state of the production equipment is worse, and the smaller the actual state value R S indicates that the state of the production equipment is better; is the actual average power consumption per unit time of the past preset days N; is the preset average power consumption per unit time of the past preset days N; is the average power consumption difference between the actual average power consumption and the preset average power consumption; when , it indicates that the actual average power consumption is higher than the preset average power consumption, the greater the absolute value of the average power consumption difference, the greater the actual state value R S ; when ​When the actual average power consumption is lower than the preset average power consumption, the larger the absolute value of the difference in average power consumption, the higher the actual state value R. S The smaller; The actual average production volume per unit time for a preset number of days N in the past; The average production volume per unit time is preset for a predetermined number of days N in the past; The difference between the preset average production quantity and the actual average production quantity; when When the actual average production is higher than the preset average production, the larger the absolute value of the difference between the two average production values, the higher the actual state value R. S The larger; when When the preset average production is lower than the actual average production, the larger the absolute value of the difference in average production, the higher the actual state value R. S The smaller;

[0057] It should be noted that the preset number of days N, standard state value of production equipment R0, standard working time of production equipment t0, standard temperature curve T0(t) of production equipment in standard state value working standard working time, standard power consumption S0 of production equipment in standard state value working standard working time, standard production quantity Q0 of production equipment in standard state value working standard working time, de-unit coefficient ε, preset constant C, first weight coefficient γ1, second weight coefficient γ2 and third weight coefficient γ3 are preset values, set based on experience, and will not be described in detail here.

[0058] In one embodiment of the present invention, the failure probability index is obtained by formula:

[0059]

[0060] Among them, A S L is the failure probability index. A L represents the estimated cumulative usage time of the production equipment. S L0 represents the actual cumulative maintenance time of the production equipment; M represents the preset maintenance cycle time of the production equipment; and m = M represents the actual number of maintenance operations of the production equipment. m Let be the severity index of the production equipment during the m-th maintenance; α be the first adjustment coefficient; β be the second adjustment coefficient; δ1 be the fourth weighting coefficient; δ2 be the fifth weighting coefficient; and A0 be the preset fault index.

[0061] Through the above technical solution, this embodiment achieves... Calculate the failure probability index A S L A -L S This is the difference between the estimated cumulative usage time and the actual cumulative usage time. The cumulative length ratio of the cumulative length difference and the predicted cumulative length of use; The greater the value is, the shorter the time of putting the production equipment into use is, and thus the greater the possibility of failure when the equipment has a problem is; The standard maintenance frequency; The difference between the actual maintenance frequency and the standard maintenance frequency; The maintenance frequency ratio of the difference between the actual maintenance frequency and the standard maintenance frequency and the standard maintenance frequency; The greater the value is, the more frequent the production equipment has a failure, and thus the greater the possibility of failure when the equipment has a problem is; The cumulative failure index; The smaller the value is, the better the running state of the equipment is, and thus the greater the possibility of failure when the equipment has a problem is;

[0062] It should be noted that the predicted cumulative length of use L A , the preset maintenance period length L0, the first adjustment coefficient a, the second adjustment coefficient β, the fourth weight coefficient δ1, the fifth weight coefficient δ2 and the preset failure index A0 are preset values, the failure severity index B m of the production equipment in the mth maintenance is set according to the inspection experience of the staff, which is not described here.

[0063] As an embodiment of the present application, the actual state value R S is compared with the first preset value R1 and the second preset value R2; the failure probability index A S is compared with the third preset value A1;

[0064] When R S <R1, the production equipment is normal;

[0065] When R1<R S <R2, A S <A1, the production equipment is normal;

[0066] When R1<R S <R2, A S ≥A1, the production equipment has a slight failure;

[0067] When R2<R S , A S <A1, the production equipment is scrapped;

[0068] When R2<R S , A S ≥A1, the production equipment has a serious failure;

[0069] Through the above technical solution, the actual state value R SCompared with the first preset value R1 and the second preset value R2; the failure probability index A is... S Compared with the third preset value; when R S When R1 < R1, it indicates that the production equipment is in good condition and is operating normally; when R1 < R S <R2,A S When R1 < R, it indicates that the poor operating condition of the production equipment is unrelated to a fault; when R1 < R S <R2,A S When R1 is greater than or equal to A1, it indicates a minor malfunction in the production equipment; when R2 < R... S A S When R1 ≥ A1, the production equipment has a serious malfunction; when R2 < R S A S When the value is <A1, the production equipment is scrapped. The scrapped and normal production equipment are screened out, and the faulty production equipment is divided into minor faults and serious faults. The management personnel can prioritize the repair of minor faults so that the production equipment can be put into production as soon as possible.

[0070] It should be noted that the first preset value R1, the second preset value R2, and the third preset value A1 are preset values ​​and satisfy R0 < R1 < R2 and A0 < A1. The first preset value R1, the second preset value R2, and the third preset value A1 are obtained based on experience and will not be described in detail here.

[0071] As one embodiment of the present invention, the fault state index is obtained by formula Obtain the fault state index W;

[0072] Through the above technical solution, in this embodiment R S -R1 is the difference between the actual state value and the first preset value; the larger the difference in state value, the larger the fault state index W; each faulty production equipment is repaired in order of increasing fault state index W.

[0073] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A plant area abnormal data monitoring system for integrated energy operation and maintenance, comprising a plurality of production devices, characterized in that, The monitoring system further comprises: a monitoring module for monitoring each production device, acquiring and storing actual production data; a maintenance information module for storing maintenance data of each production device; an analysis module connected with the monitoring module and the maintenance information module, for analyzing according to the actual production data and the maintenance data, judging whether the production device has a fault, and when the result of the judgment is that there is a fault, obtaining a fault state index of each faulty production device according to a preset rule, and performing maintenance sorting on the faulty device according to the fault state index; the preset rule for obtaining the fault state index is: S1: obtaining an actual state value of the production device by analyzing the actual temperature change curve, the actual power consumption, the actual working time and the actual production quantity of the past preset number of days; S2: obtaining the actual cumulative time by the monitoring module; S3: obtaining a fault probability index according to the actual cumulative time, the number of maintenance and the fault index of each maintenance of the production device; S4: judging whether the production device has a fault according to the actual state value and the fault probability index of the production device; when the result of the judgment is that there is a fault, obtaining the fault state index of the faulty device; The state value of the production equipment is by the formula: calculating the actual state value R of the production plant S ; Wherein, N is the past preset days, n∈N; t n is the working time of the past n days; R0 is the standard state value of the production equipment; t0 is the standard working time of the production equipment; T0(t) is the standard temperature curve of the standard working time of the production equipment in the standard state value; T(t) is the real-time temperature curve of the production equipment; S n is the power consumption of the past n days; S0 is the standard power consumption of the standard working time of the production equipment in the standard state value; Q n is the production quantity of the past n days; Q0 is the standard production quantity of the standard working time of the production equipment in the standard state value; ε is a unit coefficient; C is a preset constant; γ1 is a first weight coefficient; γ2 is a second weight coefficient; γ3 is a third weight coefficient; The failure probability index is by the formula: Wherein, A S is the failure probability index; L A is the expected cumulative length of use of the production equipment; L S is the actual cumulative length of use of the production equipment; L0 is the preset maintenance cycle length of the production equipment; M is the actual number of times of maintenance of the production equipment, m ∈ M; B m is the failure severity index of the production equipment in the mth maintenance; α is the first adjustment coefficient; β is the second adjustment coefficient; δ1 is the fourth weight coefficient; δ2 is the fifth weight coefficient; A0 is the preset failure index; The failure state index is by the formula obtaining the fault state index W. 2.The plant abnormal data monitoring system for integrated energy operation according to claim 1, wherein, The actual production data includes the actual temperature change curve of the production device on the day, the actual power consumption on the day, the actual working time on the day and the actual production quantity on the day. 3.The plant abnormal data monitoring system for integrated energy operation according to claim 2, wherein, The maintenance data includes the number of maintenance and the fault index of each maintenance. 4.The plant abnormal data monitoring system for integrated energy operation of claim 3, wherein, comparing the actual state value R S with the first preset value R1 and the second preset value R2; comparing the failure probability index A S with the third preset value A1. When R S When R1, the production equipment is normal; When R1 < R S < R2, A S < A1, the production equipment is normal; When R1 < R S < R2, A S A1, the production equipment has a minor malfunction; When R2 < R S , A S < A1, the production equipment is scrapped; When R2 < R S , A S > A1, the production equipment has a serious malfunction.

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