Equipment maintenance and management method based on industrial Internet

Abstract

The present invention discloses an equipment maintenance and management method based on the industrial Internet, and relates to the technical field of industrial equipment maintenance and management. The present invention performs comprehensive evaluation by regularly extracting operating parameters within a set time interval. In terms of operating parameter evaluation, the fault temperature index and the fault abnormal sound index are calculated, and the corresponding weight coefficient is set to calculate the fault evaluation index. The degree of equipment failure risk can be quantified more accurately, and possible equipment failures can be predicted in advance, providing a basis for timely maintenance measures, and reducing the losses caused by sudden equipment failures. At the same time, for predictive evaluation equipment that has not triggered fault maintenance signaling, its performance parameters are deeply analyzed to calculate the predictive evaluation index, and compared with the reference threshold index to trigger preventive maintenance signaling, avoiding excessive or insufficient maintenance, and being able to intervene in time when equipment performance begins to decline but has not yet caused serious failures, thereby reducing maintenance costs and extending the service life of equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment maintenance and management, and specifically to an equipment maintenance and management method based on the Industrial Internet. Background Art

[0002] In modern industrial production, the efficient and stable operation of various industrial equipment is the key for enterprises to achieve high production efficiency, excellent product quality and good economic benefits.

[0003] However, traditional equipment maintenance and management methods have gradually exposed many problems in actual application, including:

[0004] Most of the existing technologies rely on manual periodic maintenance and are unable to analyze the operating status of industrial equipment to determine whether the industrial equipment needs maintenance. In addition, when there is no abnormality in the operating status of the industrial equipment, it is impossible to further analyze the performance of the equipment and determine whether preventive maintenance is needed based on the analysis results. The level of intelligence of equipment maintenance is low and one-sided.

[0005] To this end, an equipment maintenance and management method based on the Industrial Internet is introduced. Summary of the invention

[0006] The purpose of the present invention is to solve the problems raised in the background technology and to propose an equipment maintenance and management method based on the industrial Internet.

[0007] The purpose of the present invention can be achieved by the following technical solution: an equipment maintenance and management method based on industrial Internet, characterized in that it includes:

[0008] Equipment data collection: The initial data of each working equipment is collected in real time by the collection device pre-deployed on each industrial equipment, and the initial data of each industrial equipment collected in real time by the collection device is pre-processed, and the pre-processed initial data of each industrial equipment is transmitted through wireless communication;

[0009] Equipment data processing: extract the operating parameters and performance parameters from the initial data of each industrial equipment after preprocessing, and then establish a distributed storage database, number each industrial equipment, and classify and store the operating parameters and performance parameters of each industrial equipment according to the number in the established distributed storage database; the operating parameters include operating temperature and sound signals; the performance parameters include production efficiency and energy consumption indicators;

[0010] Equipment failure analysis: preset the evaluation interval duration corresponding to each industrial equipment. After reaching the preset evaluation interval duration, extract the operating parameters of each industrial equipment within the set time interval and conduct a comprehensive evaluation to obtain the fault evaluation index qwc of each industrial equipment within the set time interval; and execute corresponding steps according to the fault evaluation index qwc of each industrial equipment within the set time interval to determine whether to trigger the fault maintenance signaling;

[0011] Equipment prediction analysis: If the fault evaluation index qwc of a certain industrial equipment within the set time interval does not trigger the fault maintenance signaling, the industrial equipment that has not triggered the fault maintenance signaling is marked as a prediction evaluation device, and the performance parameters of the prediction evaluation device within the set time interval are extracted and comprehensively evaluated to obtain the prediction evaluation index qre of the prediction evaluation device within the set time window; and according to the prediction evaluation index qre of the prediction evaluation device within the set time window, corresponding steps are executed to determine whether to trigger the preventive maintenance signaling;

[0012] Task allocation and execution: Execute corresponding steps based on the fault maintenance signaling or preventive maintenance signaling triggered by each industrial equipment.

[0013] As a preferred embodiment of the present invention, the operating parameters of each industrial equipment within a set time interval are extracted and comprehensively evaluated, specifically:

[0014] Read the operating temperature values ​​of each group of pre-arranged points of each industrial equipment within a set time interval; the pre-arranged points of each industrial equipment are represented by a number t, where t=1, 2, ..., x; x is the total number of pre-arranged points of each industrial equipment;

[0015] Extract the operating temperature values ​​of each group of pre-arranged points of each industrial equipment at different time points within the set time interval, and calculate the average value to obtain the operating average temperature hwt of each group of pre-arranged points corresponding to each industrial equipment; preset the maximum allowable operating temperature of each group of pre-arranged points corresponding to each industrial equipment during actual use, and mark it as hyt;

[0016] According to the formula , weighted calculation is performed on the average operating temperature hwt of each group of pre-arranged points corresponding to each industrial equipment within the set time interval, so as to obtain the fault temperature index map of each industrial equipment within the set time interval; wherein at represents the influence weight factor of the average operating temperature hwt of each group of pre-arranged points corresponding to each industrial equipment;

[0017] Read the sound signal of each industrial device within a set time interval, perform time domain analysis on the sound signal of each industrial device within the set time interval, and calculate the root mean square value of each industrial device within the set time interval; based on the calculated root mean square value, further calculate the peak factor of each industrial device;

[0018] The normal peak factor of each industrial equipment during actual use is preset, and the ratio of the peak factor of each industrial equipment to the corresponding preset normal peak factor is calculated to obtain the fault abnormal sound index mbp of each industrial equipment within a set time interval.

[0019] As a preferred implementation of the present invention, the failure evaluation index qwc of each industrial equipment within a set time interval is obtained, specifically:

[0020] The weight coefficients of the fault temperature index map and the fault abnormal sound index mbp corresponding to each industrial equipment are set, and the fault temperature index map and the fault abnormal sound index mbp of each industrial equipment in the set time interval are multiplied by the corresponding set weight coefficients, and then the sum is obtained to obtain the fault assessment index qwc of each industrial equipment.

[0021] As a preferred implementation of the present invention, corresponding steps are performed according to the fault evaluation index qwc of each industrial equipment within a set time interval to determine whether to trigger a fault maintenance signaling, specifically:

[0022] The fault assessment index qwc of each industrial equipment is input into a pre-built fault database and compared with the lowest fault threshold index in the fault database. If it is higher than the lowest fault threshold index, a fault maintenance signaling is triggered;

[0023] Obtain the fault temperature index map and the fault abnormal sound index mbp from the fault assessment index qwc of the industrial equipment corresponding to the triggering fault maintenance signaling, and use the fault temperature index map as the coordinate point of the horizontal axis and the fault abnormal sound index mbp as the coordinate point of the vertical axis to obtain the triggering coordinates of the industrial equipment corresponding to the triggering fault maintenance signaling;

[0024] A set of fault threshold indexes of industrial equipment corresponding to the fault maintenance signaling triggered in the fault database is extracted, and the fault type coordinates corresponding to each group of fault threshold indexes are constructed in the plane rectangular coordinate system; the fault type coordinates are constructed using the fault temperature index and the fault abnormal sound index in the fault threshold index as the horizontal axis coordinate point and the vertical axis coordinate point respectively; after the construction is completed, the coordinate point corresponding to the trigger coordinate in the plane rectangular coordinate system is drawn as the starting point, and a straight line segment is constructed from the starting point to each group of fault type coordinates, the fault type coordinate with the shortest distance to the straight line segment is selected, and compared with the preset reference distance, if it is less than or equal to the reference distance, the fault type corresponding to the fault type coordinate is used as a similar fault type of the industrial equipment corresponding to the trigger maintenance signaling.

[0025] As a preferred embodiment of the present invention, the performance parameters of the prediction and evaluation equipment within a set time interval are extracted and comprehensively evaluated, specifically:

[0026] Obtaining the production efficiency of the prediction and evaluation equipment within the set time window; extracting the production efficiency of the prediction and evaluation equipment at each time point within the set time window, and setting the qualified production efficiency of the prediction and evaluation equipment;

[0027] The production efficiency of each time point corresponding to the prediction and evaluation equipment is compared with the set qualified production efficiency. If the production efficiency at a certain time point is lower than the set qualified production efficiency, the production efficiency at this time point is marked as an inefficient yield. At the same time, the difference between the inefficient yield and the qualified production efficiency is calculated as the inefficient difference. The number of inefficient yields is counted as the inefficient number. The average of each group of inefficient differences is calculated to obtain the inefficient mean.

[0028] The weight coefficients corresponding to the number of inefficiencies and the mean value of inefficiencies are set respectively, and the number of inefficiencies and the mean value of inefficiencies of the prediction and evaluation equipment are multiplied by the corresponding set weight coefficients respectively, and then the sum is obtained to obtain the production evaluation index cq of the prediction and evaluation equipment;

[0029] Obtaining energy consumption data of the prediction and evaluation equipment within a set time window; extracting the energy consumption data of the prediction and evaluation equipment at each time point within the set time window, and arranging the energy consumption data of the prediction and evaluation equipment at each time point in chronological order;

[0030] The moving average cycle is preset, starting from the first set of energy consumption data, and the calculation is performed according to the preset moving average cycle until the last set of energy consumption data is calculated, and each set of new energy consumption data is obtained as evaluation data;

[0031] Arrange each group of new energy consumption data in the evaluation data in chronological order, and then calculate the difference between two adjacent groups of new energy consumption data, that is, subtract the data on the left from the data on the right in the two adjacent groups; if the calculated difference is a negative value, take the absolute value as the energy consumption decrease value; if the calculated difference is a positive value, take it as the energy consumption increase value; sum up all the energy consumption increase values ​​and energy consumption decrease values ​​respectively to obtain the total increase value and the total decrease value, and calculate the ratio between the total increase value and the total decrease value to obtain the energy consumption trend index ch of the predicted evaluation equipment, that is, it is calculated by the total increase value / total decrease value.

[0032] As a preferred embodiment of the present invention, the predictive evaluation index qre of the predictive evaluation device within a set time window is obtained, specifically:

[0033] Set the allowable reference indexes of the production evaluation index cq and energy consumption trend index ch corresponding to the forecast evaluation equipment, and mark them as ct and cu respectively;

[0034] According to the formula The production evaluation index cq and the energy consumption trend index ch of the predictive evaluation equipment are weightedly calculated to obtain the predictive evaluation index qre of the predictive evaluation equipment within the set time window; wherein r1 and r2 correspond to the influence weight factors of the production evaluation index cq and the energy consumption trend index ch respectively.

[0035] As a preferred embodiment of the present invention, corresponding steps are executed according to the predictive evaluation index qre of the predictive evaluation device within a set time window to determine whether to trigger the preventive maintenance signaling, specifically:

[0036] A reference threshold index of the predictive evaluation index qre corresponding to each industrial equipment is set, and the predictive evaluation index qre marked as the predictive evaluation equipment is compared with the set reference threshold index. If it is greater than the set reference threshold index, the preventive maintenance signaling is triggered.

[0037] As a preferred implementation of the present invention, corresponding steps are performed based on the fault maintenance signaling or preventive maintenance signaling triggered by each industrial device:

[0038] Fault maintenance signaling: Taking the location of the industrial equipment that triggers the fault maintenance signaling as the starting point, calculate the distance between each maintenance personnel and the starting point;

[0039] At the same time, the historical processing times of each maintenance personnel are obtained, and the processing time spent on each processing is extracted;

[0040] Calculate the average of each processing time to obtain the average processing time of each maintenance personnel; preset the distance, historical processing times and average processing time corresponding to each group of distance value ranges, each group of number value ranges and each group of time value ranges;

[0041] Set each distance value range to correspond to a timely score; each number value range to correspond to an experience score; each duration value range to correspond to an efficiency score;

[0042] The distance travelled, historical processing times and average processing time of each maintenance personnel are converted into timely scores, experience scores and efficiency scores;

[0043] After the conversion is completed, the timely score, experience score and efficiency score of each maintenance personnel are comprehensively analyzed to obtain the preferred evaluation index of each maintenance personnel; and the maintenance personnel with the largest preferred evaluation index are selected as the processing personnel of the industrial equipment that currently triggers the fault maintenance signaling, and the triggered fault maintenance signaling is sent to the processing personnel;

[0044] Preventive maintenance signaling: extract the predictive evaluation index qre of the industrial equipment corresponding to the triggering preventive maintenance signaling, and calculate the difference between the predictive evaluation index qre and the corresponding set reference threshold index, recorded as the maintenance difference, preset the difference value ranges corresponding to the maintenance difference, set each group of difference value ranges to correspond to a preventive maintenance day, match the maintenance difference of the industrial equipment corresponding to the triggering preventive maintenance signaling with the preset difference value ranges, so as to determine the preventive maintenance days of the industrial equipment corresponding to the triggering preventive maintenance signaling, and send the determined preventive maintenance days to the management personnel, reminding the management personnel to complete the inspection and maintenance of the industrial equipment within the preventive maintenance days.

[0045] As a preferred implementation of the present invention, the preferred evaluation index of each maintenance personnel is obtained, specifically:

[0046] After the conversion is completed, similar fault types of industrial equipment that trigger fault maintenance signaling are extracted, and different fault types of different industrial equipment are set to correspond to a set of weight coefficients; wherein the weight coefficient set includes the weight coefficients of timeliness score, experience score and efficiency score corresponding to different fault types of different industrial equipment;

[0047] Based on similar fault types of industrial equipment that trigger fault maintenance signaling, the timeliness score, experience score, and efficiency score of each maintenance personnel are multiplied by the corresponding weight coefficients, and then the sum is obtained to obtain the preferred evaluation index of each maintenance personnel.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention presets the evaluation interval of each industrial equipment, regularly extracts the operating parameters within the set time interval for comprehensive evaluation, calculates the fault temperature index and the fault abnormal sound index in the operating parameter evaluation, and sets the corresponding weight coefficient to calculate the fault evaluation index, which can more accurately quantify the risk of equipment failure, predict the possible failure of the equipment in advance, provide a basis for timely maintenance measures, and reduce the losses caused by sudden equipment failures. At the same time, for the predictive evaluation equipment that has not triggered the fault maintenance signaling, its performance parameters are deeply analyzed to calculate the predictive evaluation index, and compared with the reference threshold index to trigger the preventive maintenance signaling, avoiding excessive or insufficient maintenance, and being able to intervene in time when the equipment performance begins to decline but has not yet caused a serious failure, thereby reducing maintenance costs and extending the service life of the equipment.

[0050] After the fault maintenance signal is triggered, the present invention outputs the fault evaluation index of the equipment to a pre-built fault database for comparison. If the index is higher than the minimum threshold value, the maintenance signal is triggered; at the same time, the trigger coordinates are built and compared with the fault type coordinates built based on historical fault data in the fault database; by calculating the straight line segment distance between the trigger coordinates and each fault type coordinate, the fault type coordinates with the shortest distance and meeting the preset reference distance are screened out, and similar fault types are determined, which changes the situation of blindly troubleshooting problems during traditional fault maintenance. Maintenance personnel can quickly locate possible fault causes and carry out maintenance work in a targeted manner, thereby improving maintenance efficiency, shortening equipment downtime, and reducing production losses caused by faults.

[0051] The present invention calculates the difference between the predictive evaluation index and the reference threshold index, matches the preventive maintenance days according to a preset difference value range, thereby accurately judging the effective period for which preventive maintenance is required, and the management personnel reasonably arrange the maintenance personnel to perform maintenance within the effective period according to the preventive maintenance days, thereby improving the intelligence and orderliness of management and maintenance;

[0052] After triggering the fault maintenance signaling, the present invention takes the location of the faulty equipment as the starting point and comprehensively considers three key factors: the distance between the maintenance personnel and the equipment, the number of historical processing times and the average processing time; presets a value range for each factor and corresponds to a corresponding score, converts the actual data of the maintenance personnel into a score, and then combines similar fault types of the faulty equipment to set weight coefficients of each scoring factor for different fault types, calculates the preferred evaluation index, and selects the maintenance personnel with the largest index to handle the fault, thereby avoiding the irrationality caused by traditional maintenance personnel deployment based on only a single factor, and realizing multi-dimensional comprehensive evaluation, ensuring that the deployed maintenance personnel can better meet the fault handling needs in terms of experience, efficiency and timeliness of arriving at the scene, and improving the success rate and efficiency of fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0054] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1As shown, the equipment maintenance and management method based on the Industrial Internet includes:

[0057] Equipment data collection: The initial data of each working equipment is collected in real time through the collection device pre-deployed on each industrial equipment, and the initial data of each industrial equipment collected in real time by the collection device is pre-processed; the pre-processing includes filtering, amplification, analog-to-digital conversion, etc., and the initial data of each industrial equipment after pre-processing is transmitted through wireless communication, which is industrial Ethernet;

[0058] It should be noted that the collection devices such as temperature sensors, vibration sensors and current sensors, etc.; select appropriate sensors according to different types of industrial equipment and install them in key parts of the equipment. At the same time, set and calibrate the sensors to ensure the accuracy of the collected data.

[0059] Equipment data processing: extract the operating parameters and performance parameters from the initial data of each industrial equipment after preprocessing, and then establish a distributed storage database to number each industrial equipment; the number is represented by k, where k=1,2,...,s; s is the total number of industrial equipment; in the established distributed storage database, the operating parameters and performance parameters of each industrial equipment are classified and stored according to the number; the operating parameters include operating temperature and sound signals; the performance parameters include production efficiency and energy consumption indicators;

[0060] Equipment failure analysis: preset the evaluation interval duration corresponding to each industrial equipment. After reaching the preset evaluation interval duration, extract the operating parameters of each industrial equipment within the set time interval and conduct a comprehensive evaluation to obtain the fault evaluation index qwc of each industrial equipment within the set time interval; and execute corresponding steps according to the fault evaluation index qwc of each industrial equipment within the set time interval to determine whether to trigger the fault maintenance signaling;

[0061] Specifically:

[0062] Read the operating temperature values ​​of each group of pre-arranged points of each industrial equipment within a set time interval; each group of pre-arranged points is pre-arranged according to the type of industrial equipment, for example, the temperature of different key parts of the industrial equipment, including bearings and windings, etc., can be collected; the pre-arranged points of each industrial equipment are represented by a number t, where t=1, 2, ..., x; x is the total number of pre-arranged points of each industrial equipment;

[0063] Extract the operating temperature values ​​of each group of pre-arranged points of each industrial equipment at different time points within the set time interval, and calculate the average value to obtain the operating average temperature hwt of each group of pre-arranged points corresponding to each industrial equipment; preset the maximum allowable operating temperature of each group of pre-arranged points corresponding to each industrial equipment during actual use, and mark it as hyt;

[0064] According to the formula , weighted calculation is performed on the average operating temperature hwt of each group of pre-arranged points corresponding to each industrial equipment within the set time interval, so as to obtain the fault temperature index map of each industrial equipment within the set time interval; wherein at represents the influence weight factor of the average operating temperature hwt of each group of pre-arranged points corresponding to each industrial equipment;

[0065] It should be noted that the fault temperature index is obtained by weighted calculation of the average operating temperature of each group of pre-deployed points corresponding to each industrial equipment within the set time interval, thereby realizing a quantitative assessment of the overall failure risk of the equipment; the fault temperature index can comprehensively consider the temperature conditions of different key parts and their impact on equipment failure.

[0066] Read the sound signals of each industrial equipment within the set time interval, perform time domain analysis on the sound signals of each industrial equipment within the set time interval, and calculate the root mean square value of each industrial equipment within the set time interval; the root mean square value is calculated by the formula Calculate and get It represents the collected discrete sound signal samples, which are collected at the set sampling frequency, and N represents the total number of sampling points;

[0067] Based on the calculated RMS value, the peak factor of each industrial equipment is further calculated; that is, it is calculated by comparing the peak value of the industrial equipment within a set time interval with the corresponding RMS value;

[0068] The normal peak factor of each industrial equipment in actual use is preset, and the ratio between the peak factor of each industrial equipment and the corresponding preset normal peak factor is calculated to obtain the fault abnormal sound index mbp of each industrial equipment within the set time interval; it is calculated by peak factor / normal peak factor;

[0069] It should be noted that the sensitivity and accuracy of fault detection are improved by using the fault abnormal sound index obtained above.

[0070] The weight coefficients of the fault temperature index map and the fault abnormal sound index mbp corresponding to each industrial equipment are set, and the fault temperature index map and the fault abnormal sound index mbp of each industrial equipment in the set time interval are multiplied by the corresponding set weight coefficients, and then the sum is obtained to obtain the fault assessment index qwc of each industrial equipment;

[0071] According to the fault evaluation index qwc of each industrial equipment within the set time interval, corresponding steps are executed to determine whether to trigger the fault maintenance signaling, specifically:

[0072] The fault assessment index qwc of each industrial equipment is input into a pre-built fault database and compared with the lowest fault threshold index in the fault database. If it is higher than the lowest fault threshold index, a fault maintenance signaling is triggered;

[0073] Obtain the fault temperature index map and the fault abnormal sound index mbp from the fault assessment index qwc of the industrial equipment corresponding to the triggering fault maintenance signaling, and use the fault temperature index map as the coordinate point of the horizontal axis and the fault abnormal sound index mbp as the coordinate point of the vertical axis to obtain the triggering coordinates of the industrial equipment corresponding to the triggering fault maintenance signaling;

[0074] Extract the set of fault threshold indexes of industrial equipment corresponding to the fault maintenance signaling triggered in the fault database, and construct the fault type coordinates corresponding to each group of fault threshold indexes in the plane rectangular coordinate system; use the fault temperature index and the fault abnormal sound index in the fault threshold index as the horizontal axis coordinate point and the vertical axis coordinate point to construct the fault type coordinates; after the construction is completed, draw the coordinate point corresponding to the trigger coordinate in the plane rectangular coordinate system as the starting point, and construct the straight line segment between each group of fault type coordinates from the starting point, select the fault type coordinate with the shortest distance to the straight line segment, and compare it with the preset reference distance. If it is less than or equal to the reference distance, the fault type corresponding to the fault type coordinate is used as a similar fault type of the industrial equipment corresponding to the trigger maintenance signaling;

[0075] The specific steps to pre-build the fault database are:

[0076] Record the historical failures of each industrial equipment, and analyze the failure evaluation index of each industrial equipment in the set time zone before each failure occurs, as a set of failure threshold indexes corresponding to each industrial equipment;

[0077] Equipment prediction analysis: If the fault evaluation index qwc of a certain industrial equipment within the set time interval does not trigger the fault maintenance signaling, the industrial equipment that has not triggered the fault maintenance signaling is marked as a prediction evaluation device, and the performance parameters of the prediction evaluation device within the set time interval are extracted and comprehensively evaluated to obtain the prediction evaluation index qre of the prediction evaluation device within the set time window; and according to the prediction evaluation index qre of the prediction evaluation device within the set time window, corresponding steps are executed to determine whether to trigger the preventive maintenance signaling;

[0078] Specifically:

[0079] Obtain the production efficiency of the prediction and evaluation equipment within the set time window; that is, the output per unit time; extract the production efficiency of the prediction and evaluation equipment at each time point within the set time window, and set the qualified production efficiency of the prediction and evaluation equipment;

[0080] The production efficiency of each time point corresponding to the prediction and evaluation equipment is compared with the set qualified production efficiency. If the production efficiency at a certain time point is lower than the set qualified production efficiency, the production efficiency at this time point is marked as an inefficient yield. At the same time, the difference between the inefficient yield and the qualified production efficiency is calculated as the inefficient difference. The number of inefficient yields is counted as the inefficient number. The average of each group of inefficient differences is calculated to obtain the inefficient mean.

[0081] The weight coefficients corresponding to the number of inefficiencies and the mean value of inefficiencies are set respectively, and the number of inefficiencies and the mean value of inefficiencies of the prediction and evaluation equipment are multiplied by the corresponding set weight coefficients respectively, and then the sum is obtained to obtain the production evaluation index cq of the prediction and evaluation equipment;

[0082] It should be noted that by obtaining the predicted and evaluated production efficiency of the equipment at each time point within the set time window and comparing it with the qualified production efficiency, the production efficiency performance of the equipment at different times can be accurately captured. The higher the production evaluation index cq, the more likely it is that the performance of key parts of the equipment has declined and maintenance is required.

[0083] Obtain energy consumption data of the prediction and evaluation equipment within a set time window; that is, electric energy consumption, and energy consumption data under the same type of production tasks; extract the energy consumption data of the prediction and evaluation equipment at each time point within the set time window, and arrange the energy consumption data of the prediction and evaluation equipment at each time point in chronological order;

[0084] The moving average cycle is preset, starting from the first set of energy consumption data, and the calculation is performed according to the preset moving average cycle until the last set of energy consumption data is calculated, and each set of new energy consumption data is obtained as evaluation data;

[0085] Arrange each group of new energy consumption data in the evaluation data in chronological order, and then calculate the difference between two adjacent groups of new energy consumption data, that is, the data on the right minus the data on the left in the two adjacent groups; if the calculated difference is a negative value, take the absolute value as the energy consumption decrease value; if the calculated difference is a positive value, take it as the energy consumption increase value; sum up all the energy consumption increase values ​​and energy consumption decrease values ​​respectively to obtain the total increase value and the total decrease value, and calculate the ratio between the total increase value and the total decrease value to obtain the energy consumption trend index ch of the predicted evaluation equipment, that is, the total increase value / total decrease value will be calculated;

[0086] It should be noted that the difference between two adjacent sets of new energy consumption data is calculated, and the energy consumption increase and decrease are further distinguished. The total increase and decrease are summed up to obtain the energy consumption trend index, which realizes the quantitative analysis of the changes in equipment energy consumption. If the energy consumption trend index ch is higher, it means that the equipment's power loss increases and preventive maintenance is required.

[0087] Set the allowable reference indexes of the production evaluation index cq and energy consumption trend index ch corresponding to the forecast evaluation equipment, and mark them as ct and cu respectively;

[0088] It should be noted that the allowable reference index represents the highest index allowed to be reached by the forecast evaluation equipment production evaluation index cq and the highest index allowed to be reached by the energy consumption trend index ch.

[0089] According to the formula The production evaluation index cq and the energy consumption trend index ch of the prediction and evaluation equipment are weighted and calculated to obtain the predictive evaluation index qre of the prediction and evaluation equipment within the set time window; wherein r1 and r2 correspond to the influence weight factors of the production evaluation index cq and the energy consumption trend index ch respectively;

[0090] According to the predictive evaluation index qre of the predictive evaluation device within the set time window, corresponding steps are executed to determine whether to trigger the preventive maintenance signaling, specifically:

[0091] A reference threshold index of the predictive evaluation index qre corresponding to each industrial equipment is set, and the predictive evaluation index qre marked as the predictive evaluation equipment is compared with the set reference threshold index. If it is greater than the set reference threshold index, a preventive maintenance signaling is triggered;

[0092] Task allocation and execution: Execute corresponding steps based on the fault maintenance signaling or preventive maintenance signaling triggered by each industrial equipment;

[0093] Specifically:

[0094] Fault maintenance signaling: Taking the location of the industrial equipment that triggers the fault maintenance signaling as the starting point, calculate the distance between each maintenance personnel and the starting point;

[0095] At the same time, the historical processing times of each maintenance personnel are obtained, and the processing time spent on each processing is extracted; the timing starts from the arrival at the industrial equipment location and ends when the processing is completed;

[0096] Calculate the average of each processing time to obtain the average processing time of each maintenance personnel; preset the distance, historical processing times and average processing time corresponding to each group of distance value ranges, each group of number value ranges and each group of time value ranges;

[0097] Set each distance value range to correspond to a timely score; each number value range to correspond to an experience score; each duration value range to correspond to an efficiency score;

[0098] The distance travelled, historical processing times and average processing time of each maintenance personnel are converted into timely scores, experience scores and efficiency scores;

[0099] After the conversion is completed, similar fault types of industrial equipment that trigger fault maintenance signaling are extracted, and different fault types of different industrial equipment are set to correspond to a set of weight coefficients; wherein the weight coefficient set includes the weight coefficients of timeliness score, experience score and efficiency score corresponding to different fault types of different industrial equipment;

[0100] Based on similar fault types of industrial equipment that triggers fault maintenance signaling, the timely score, experience score, and efficiency score of each maintenance personnel are multiplied by the corresponding weight coefficients, and then the sum is obtained to obtain the preferred evaluation index of each maintenance personnel; and the maintenance personnel with the largest preferred evaluation index are selected as the processing personnel of the current industrial equipment that triggers the fault maintenance signaling, and the triggered fault maintenance signaling is sent to the processing personnel;

[0101] It should be noted that the timeliness score, experience score and experience score range are all set at 1-10 and are positive integers; the shorter the distance, the higher the timeliness score obtained by matching; the more historical processing times, the higher the experience score obtained by matching; the shorter the average processing time, the higher the efficiency score obtained by matching.

[0102] It should be noted that the evaluation is conducted by comprehensively considering the three key factors of the distance between the maintenance personnel and the faulty equipment, the number of historical processing times, and the average processing time, which avoids the one-sidedness of selecting maintenance personnel based on a single factor; the distance reflects whether the maintenance personnel can quickly reach the fault site, the number of historical processing times reflects their past experience in handling similar problems, and the average processing time can intuitively show the efficiency of their fault handling. This multi-dimensional comprehensive evaluation makes personnel deployment more comprehensive, scientific, and more in line with actual fault handling needs; further combined with similar fault types of industrial equipment that trigger fault maintenance signaling, and setting corresponding weight coefficient sets for different fault types, it can highlight the importance of different scoring factors in a targeted manner according to the characteristics of specific faults. For example, for some complex and rare fault types, a higher weight may be given to the experience score, while for common faults that need to be solved quickly, more emphasis is placed on the timely score and efficiency score; in this way, the selected maintenance personnel are more adaptable to the specific fault scenario, which helps to improve the success rate and efficiency of fault handling.

[0103] Preventive maintenance signaling: extract the predictive evaluation index qre of the industrial equipment corresponding to the triggering preventive maintenance signaling, and calculate the difference between the predictive evaluation index qre and the corresponding set reference threshold index, recorded as the maintenance difference, preset the difference value ranges corresponding to the maintenance difference, set each group of difference value ranges to correspond to a preventive maintenance day, match the maintenance difference of the industrial equipment corresponding to the triggering preventive maintenance signaling with the preset difference value ranges, so as to determine the preventive maintenance days of the industrial equipment corresponding to the triggering preventive maintenance signaling, and send the determined preventive maintenance days to the management personnel, reminding the management personnel to complete the inspection and maintenance of the industrial equipment within the preventive maintenance days;

[0104] It should be noted that by calculating the difference between the predictive evaluation index and the set reference threshold index, and matching the corresponding preventive maintenance days according to the preset difference value range, accurate control of the timing of preventive maintenance of industrial equipment can be achieved.

[0105] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The equipment maintenance and management method based on the industrial Internet is characterized by: include: Equipment data collection: The initial data of each working equipment is collected in real time by the collection device pre-deployed on each industrial equipment, and the initial data of each industrial equipment collected in real time by the collection device is pre-processed, and the pre-processed initial data of each industrial equipment is transmitted through wireless communication; Equipment data processing: extract the operating parameters and performance parameters from the initial data of each industrial equipment after preprocessing, and then establish a distributed storage database, number each industrial equipment, and classify and store the operating parameters and performance parameters of each industrial equipment according to the number in the established distributed storage database; the operating parameters include operating temperature and sound signals; the performance parameters include production efficiency and energy consumption indicators; Equipment failure analysis: preset the evaluation interval duration corresponding to each industrial equipment. After reaching the preset evaluation interval duration, extract the operating parameters of each industrial equipment within the set time interval and conduct a comprehensive evaluation to obtain the fault evaluation index qwc of each industrial equipment within the set time interval; and execute corresponding steps according to the fault evaluation index qwc of each industrial equipment within the set time interval to determine whether to trigger the fault maintenance signaling; According to the fault evaluation index qwc of each industrial equipment within the set time interval, corresponding steps are executed to determine whether to trigger the fault maintenance signaling, specifically: The fault assessment index qwc of each industrial equipment is input into a pre-built fault database and compared with the lowest fault threshold index in the fault database. If it is higher than the lowest fault threshold index, a fault maintenance signaling is triggered; Obtain the fault temperature index map and the fault abnormal sound index mbp from the fault assessment index qwc of the industrial equipment corresponding to the triggering fault maintenance signaling, and use the fault temperature index map as the coordinate point of the horizontal axis and the fault abnormal sound index mbp as the coordinate point of the vertical axis to obtain the triggering coordinates of the industrial equipment corresponding to the triggering fault maintenance signaling; Extract the set of fault threshold indexes of industrial equipment corresponding to the triggering fault maintenance signaling in the fault database, and construct the fault type coordinates corresponding to each set of fault threshold indexes in the plane rectangular coordinate system; The fault temperature index and the fault abnormal sound index in the fault threshold index are used as the horizontal axis coordinate point and the vertical axis coordinate point to construct the fault type coordinate; After the construction is completed, the coordinate point corresponding to the trigger coordinate in the plane rectangular coordinate system is drawn as the starting point, and a straight line segment between each group of fault type coordinates is constructed from the starting point. The fault type coordinate with the shortest distance to the straight line segment is selected and compared with the preset reference distance. If it is less than or equal to the reference distance, the fault type corresponding to the fault type coordinate is used as a similar fault type of the industrial equipment corresponding to the trigger fault maintenance signaling; Equipment prediction analysis: If the fault evaluation index qwc of a certain industrial equipment within the set time interval does not trigger the fault maintenance signaling, the industrial equipment that has not triggered the fault maintenance signaling is marked as a prediction evaluation device, and the performance parameters of the prediction evaluation device within the set time interval are extracted and comprehensively evaluated to obtain the prediction evaluation index qre of the prediction evaluation device within the set time window; and according to the prediction evaluation index qre of the prediction evaluation device within the set time window, corresponding steps are executed to determine whether to trigger the preventive maintenance signaling; According to the predictive evaluation index qre of the predictive evaluation device within the set time window, corresponding steps are executed to determine whether to trigger the preventive maintenance signaling, specifically: A reference threshold index of the predictive evaluation index qre corresponding to each industrial equipment is set, and the predictive evaluation index qre marked as the predictive evaluation equipment is compared with the set reference threshold index. If it is greater than the set reference threshold index, a preventive maintenance signaling is triggered; Task allocation and execution: Execute corresponding steps based on the fault maintenance signaling or preventive maintenance signaling triggered by each industrial equipment.

2. The equipment maintenance and management method based on the industrial Internet according to claim 1 is characterized in that: Extract the operating parameters of each industrial equipment within the set time interval and conduct a comprehensive evaluation, specifically: Read the operating temperature values ​​of each group of pre-arranged points of each industrial equipment within a set time interval; the pre-arranged points of each industrial equipment are represented by a number t, where t=1, 2, ..., x; x is the total number of pre-arranged points of each industrial equipment; Extract the operating temperature values ​​of each group of pre-arranged points of each industrial equipment at different time points within the set time interval, and calculate the average value to obtain the operating average temperature hwt of each group of pre-arranged points corresponding to each industrial equipment; preset the maximum allowable operating temperature of each group of pre-arranged points corresponding to each industrial equipment during actual use, and mark it as hyt; According to the formula , weighted calculation is performed on the operating average temperature hwt of each group of pre-arranged points corresponding to each industrial equipment within the set time interval, so as to obtain the fault temperature index map of each industrial equipment within the set time interval; Where at represents the influence weight factor of the average operating temperature hwt of each group of pre-arranged points corresponding to each industrial equipment; Read the sound signal of each industrial device within a set time interval, perform time domain analysis on the sound signal of each industrial device within the set time interval, and calculate the root mean square value of each industrial device within the set time interval; based on the calculated root mean square value, further calculate the peak factor of each industrial device; The normal peak factor of each industrial equipment during actual use is preset, and the ratio of the peak factor of each industrial equipment to the corresponding preset normal peak factor is calculated to obtain the fault abnormal sound index mbp of each industrial equipment within a set time interval.

3. The equipment maintenance and management method based on the industrial Internet according to claim 2 is characterized in that: The failure evaluation index qwc of each industrial equipment within the set time interval is obtained, specifically: The weight coefficients of the fault temperature index map and the fault abnormal sound index mbp corresponding to each industrial equipment are set, and the fault temperature index map and the fault abnormal sound index mbp of each industrial equipment in the set time interval are multiplied by the corresponding set weight coefficients, and then the sum is obtained to obtain the fault assessment index qwc of each industrial equipment.

4. The equipment maintenance and management method based on the industrial Internet according to claim 3 is characterized in that: Extract the performance parameters of the prediction and evaluation equipment within the set time interval and conduct a comprehensive evaluation, specifically: Obtaining the production efficiency of the prediction and evaluation equipment within the set time window; extracting the production efficiency of the prediction and evaluation equipment at each time point within the set time window, and setting the qualified production efficiency of the prediction and evaluation equipment; The production efficiency of each time point corresponding to the prediction and evaluation equipment is compared with the set qualified production efficiency. If the production efficiency at a certain time point is lower than the set qualified production efficiency, the production efficiency at this time point is marked as an inefficient yield. At the same time, the difference between the inefficient yield and the qualified production efficiency is calculated as the inefficient difference. The number of inefficient yields is counted as the inefficient number. The average of each group of inefficient differences is calculated to obtain the inefficient mean. The weight coefficients corresponding to the inefficiency number and the inefficiency mean are set respectively, and the inefficiency number and the inefficiency mean of the prediction and evaluation equipment are multiplied by the corresponding set weight coefficients respectively, and then the sum is obtained to obtain the production evaluation index cq of the prediction and evaluation equipment; Obtaining energy consumption data of the prediction and evaluation equipment within a set time window; extracting the energy consumption data of the prediction and evaluation equipment at each time point within the set time window, and arranging the energy consumption data of the prediction and evaluation equipment at each time point in chronological order; The moving average cycle is preset, starting from the first set of energy consumption data, and the calculation is performed according to the preset moving average cycle until the last set of energy consumption data is calculated, and each set of new energy consumption data is obtained as evaluation data; Arrange each group of new energy consumption data in the evaluation data in chronological order, and then calculate the difference between two adjacent groups of new energy consumption data, that is, subtract the data on the left from the data on the right of the two adjacent groups; if the calculated difference is a negative value, take the absolute value as the energy consumption reduction value; If the calculated difference is a positive value, it is used as the energy consumption increase value; all energy consumption increase values ​​and energy consumption decrease values ​​are summed up respectively to obtain the total increase value and the total decrease value, and the ratio between the total increase value and the total decrease value is calculated to obtain the energy consumption trend index ch of the predicted evaluation equipment, that is, it is calculated by the total increase value / total decrease value.

5. The equipment maintenance and management method based on the industrial Internet according to claim 4 is characterized in that: The predictive evaluation index qre of the predictive evaluation device within the set time window is obtained, specifically: Set the allowable reference indexes of the production evaluation index cq and energy consumption trend index ch corresponding to the forecast evaluation equipment, and mark them as ct and cu respectively; According to the formula The production evaluation index cq and the energy consumption trend index ch of the predictive evaluation equipment are weightedly calculated to obtain the predictive evaluation index qre of the predictive evaluation equipment within the set time window; wherein r1 and r2 correspond to the influence weight factors of the production evaluation index cq and the energy consumption trend index ch respectively.

6. The equipment maintenance and management method based on the industrial Internet according to claim 5 is characterized in that: Based on the fault maintenance signaling or preventive maintenance signaling triggered by each industrial device, the corresponding steps are executed: Fault maintenance signaling: Taking the location of the industrial equipment that triggers the fault maintenance signaling as the starting point, calculate the distance between each maintenance personnel and the starting point; At the same time, the historical processing times of each maintenance personnel are obtained, and the processing time spent on each processing is extracted; Calculate the average of each processing time to obtain the average processing time of each maintenance personnel; preset the distance, historical processing times and average processing time corresponding to each group of distance value ranges, each group of number value ranges and each group of time value ranges; Set each distance value range to correspond to a timely score; each number value range to correspond to an experience score; each duration value range to correspond to an efficiency score; The distance travelled, historical processing times and average processing time of each maintenance personnel are converted into timely scores, experience scores and efficiency scores; After the conversion is completed, the timely score, experience score and efficiency score of each maintenance personnel are comprehensively analyzed to obtain the preferred evaluation index of each maintenance personnel; and the maintenance personnel with the largest preferred evaluation index are selected as the processing personnel of the industrial equipment that currently triggers the fault maintenance signaling, and the triggered fault maintenance signaling is sent to the processing personnel; Preventive maintenance signaling: extract the predictive evaluation index qre of the industrial equipment corresponding to the triggering preventive maintenance signaling, and calculate the difference between the predictive evaluation index qre and the corresponding set reference threshold index, recorded as the maintenance difference, preset the difference value ranges corresponding to the maintenance difference, set each group of difference value ranges to correspond to a preventive maintenance day, match the maintenance difference of the industrial equipment corresponding to the triggering preventive maintenance signaling with the preset difference value ranges, so as to determine the preventive maintenance days of the industrial equipment corresponding to the triggering preventive maintenance signaling, and send the determined preventive maintenance days to the management personnel, reminding the management personnel to complete the inspection and maintenance of the industrial equipment within the preventive maintenance days.

7. The equipment maintenance and management method based on the industrial Internet according to claim 6 is characterized in that: The optimal evaluation index of each maintenance personnel is obtained, specifically: After the conversion is completed, similar fault types of industrial equipment that trigger fault maintenance signaling are extracted, and different fault types of different industrial equipment are set to correspond to a set of weight coefficients; wherein the weight coefficient set includes the weight coefficients of timeliness score, experience score and efficiency score corresponding to different fault types of different industrial equipment; Based on similar fault types of industrial equipment that trigger fault maintenance signaling, the timeliness score, experience score, and efficiency score of each maintenance personnel are multiplied by the corresponding weight coefficients, and then the sum is obtained to obtain the preferred evaluation index of each maintenance personnel.

8. The equipment maintenance and management method based on the industrial Internet according to claim 7 is characterized in that: The specific steps to pre-build the fault database are: The historical failures of each industrial equipment are recorded, and the failure evaluation index of each industrial equipment in the set time zone before each failure occurs is analyzed as the failure threshold index set corresponding to each industrial equipment.

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