Method, system, medium and equipment for evaluating running state of main ventilator

Abstract

The invention discloses a method, a system, a medium and equipment for evaluating the running state of a main ventilator, relates to the technical field of safety engineering, and aims to select important parameters from monitoring parameters of a mining main ventilator to construct a running state evaluation index system and divide the running state of the main ventilator. The initial weight of the index is determined through a combined weighting method, the weight of the degradation index is improved by introducing a variable weight theory, and the identification capability of the model on the degradation state is enhanced. A cloud model is adopted to calculate the membership degree of the index parameters to the operation state grade, and the operation state of the main ventilator is determined according to the evaluation criterion of the worst grade item with the membership degree larger than 0.1. Verification is carried out by using data before the main ventilator breaks down, and the result shows that the model can evaluate the degradation state of the ventilator in time.

Description

Technical Field

[0001] The invention relates to the technical field of safety engineering, and in particular to a method, system, medium and equipment for evaluating the operating state of a main fan. Background Art

[0002] In current mining production, the main ventilation fan for mines is a key safety equipment, and the stability of its operating state is directly related to the safe production and personnel safety of the mine. However, the existing state parameter early warning model for the main ventilation fan for mines often only focuses on the monitoring and early warning of a single state parameter, which has obvious limitations. Since it only relies on the early warning of a single state parameter, it is impossible to fully reflect the overall operating status of the main ventilation fan. This one-sided monitoring method often makes it difficult to capture potential failures of the equipment in the early stage.

[0003] Traditional early warning models usually implement alarms by setting a fixed threshold for a single parameter. Once the parameter value exceeds this threshold, the system will sound an alarm. However, this approach can only detect those faults that are already serious, and the system cannot effectively identify those potential faults that are subtle and difficult to detect in the early stage. This results in the system being unable to take any early warning measures in the early stage of the fault, and can only allow the fault to gradually develop until the parameter value exceeds the set threshold, and then an alarm is sounded. This delayed early warning mechanism not only delays the best time to handle the fault, but may also lead to further expansion of the fault, increasing maintenance costs and safety production risks. Summary of the invention

[0004] In order to overcome the shortcoming of the above-mentioned prior art that only relying on the early warning of a single state parameter cannot fully reflect the overall operating status of the mine main ventilation fan, the main purpose of the present invention is to provide a method, system, medium and equipment for evaluating the operating status of the main ventilation fan.

[0005] To achieve the above object, the present invention adopts the following technical solution, a method for evaluating the operating status of a main fan, comprising the following steps:

[0006] Acquire monitoring parameters of the main fan operating state, and select monitoring parameters with a correlation with the main fan operating state greater than a set value as indicator parameters;

[0007] According to the index parameters combined with fuzzy comprehensive evaluation, a main fan operation status evaluation model is constructed, and the monitoring parameters are used as evaluation indicators of the main fan operation status evaluation model;

[0008] Based on the monitoring parameters, the weights are combined through the analytic hierarchy process and the entropy weight method, and the variable weight theory is introduced to further revise the weights to obtain the revised weights of the monitoring parameters.

[0009] Determining the degradation degree of the indicator parameter according to the attribute of the indicator parameter, replacing the membership function in the main fan operation status evaluation model with the cloud model, and obtaining an updated main fan operation status evaluation model;

[0010] The index parameters are input into the updated main fan operation status evaluation model to obtain the main fan operation status result.

[0011] The combined weighting is determined by the minimum identification information to determine the combined weight, and the calculation formula is:

[0012]

[0013] Among them, wj is the combined weight; wj' is the weight of the hierarchical analysis method; and wj* is the weight of the entropy weight method.

[0014] The introduction of variable weight theory further revises the weights, and the revised formula is:

[0015]

[0016] Among them, wj and wj 0 are the variable weight and initial combination weight of the j-th indicator respectively; dj is the degradation degree of the j-th indicator; σ is the variable weight coefficient, which is 2.

[0017] The degradation degree ranges from [0, 1], and the lower the degradation degree, the healthier the indicator parameter.

[0018] A system for fuzzy comprehensive evaluation of the operating status of a main fan, comprising:

[0019] A data acquisition module is used to obtain monitoring parameters of the main fan operation status, and select monitoring parameters with a correlation with the main fan operation status greater than a set value as indicator parameters;

[0020] The data model construction module is used to construct a main fan operation status evaluation model based on the indicator parameters combined with fuzzy comprehensive evaluation, and use the monitoring parameters as the evaluation indicators of the main fan operation status evaluation model; based on the monitoring parameters, the hierarchical analysis method and the entropy weight method are used for combined weighting, and the variable weight theory is introduced to further revise the weights to obtain the revised weights of the monitoring parameters; according to the attributes of the indicator parameters, the degradation degree of the indicator parameters is determined, and the membership function in the main fan operation status evaluation model is replaced by the cloud model to obtain the updated main fan operation status evaluation model;

[0021] The data processing module is used to input the index parameters into the updated main fan operation status evaluation model to obtain the main fan operation status result.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: constructing a health evaluation model for the main ventilation fan in a mine, selecting multiple monitoring parameters that can reflect the operating status of the main ventilation fan as model evaluation indicators, using the analytic hierarchy process and the entropy weight method for combined weighting, and introducing the variable weight theory to further modify the weights. Determine the degree of degradation of the indicator parameters, use the cloud model to replace the traditional membership function, and enhance the fuzziness of the model. By comprehensively considering multiple evaluation indicators and setting a reasonable health status level, the model can more objectively reflect the actual operation of the main ventilation fan in a mine, and quickly identify abnormal conditions of the main ventilation fan, which helps to respond and handle potential fault problems of the main ventilation fan in a timely manner, ensuring that the main ventilation fan continues to operate efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0024] Figure 1 It is a schematic diagram of the process structure of the present invention;

[0025] Figure 2 It is a schematic diagram of the health status evaluation index system of the main ventilation fan for mining according to the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0027] Relying on the early warning of a single state parameter cannot fully reflect the overall operating status of the mine main ventilation fan. Therefore, it is particularly important to establish a systematic, objective and effective mine main ventilation fan operating status evaluation system. In view of the problem that the existing mine main ventilation fan state parameter early warning model is mainly limited to the single parameter state evaluation, this application proposes an evaluation method that comprehensively considers the overall state of the main ventilation fan. Based on the fuzzy comprehensive evaluation theory, a mine main ventilation fan operating status evaluation model is constructed. By comprehensively considering multiple evaluation indicators, the model can more objectively reflect the actual operating conditions of the mine main ventilation fan and help to promptly discover potential fault problems. This comprehensive evaluation model plays a vital role in predicting and identifying the fault state of the main ventilation fan.

[0028] Specific steps are as follows Figure 1 As shown: The specific steps include:

[0029] Acquire monitoring parameters of the main fan operating state, and select monitoring parameters with a correlation with the main fan operating state greater than a set value as indicator parameters;

[0030] The index parameters are combined with fuzzy comprehensive evaluation to construct a main fan operation status evaluation model, and the monitoring parameters are used as evaluation indicators of the main fan operation status evaluation model;

[0031] Based on the monitoring parameters, the weights are combined through the analytic hierarchy process and the entropy weight method, and the variable weight theory is introduced to further revise the weights to obtain the revised weights of the monitoring parameters.

[0032] Determine the degradation degree of the indicator parameter according to the attribute of the indicator parameter and the revised weight of the monitoring parameter, replace the membership function in the main fan operation status evaluation model with the cloud model, and obtain an updated main fan operation status evaluation model;

[0033] The index parameters are input into the updated main fan operation status evaluation model to obtain the main fan operation status result.

[0034] The fuzzy comprehensive evaluation method is based on the principle of fuzzy mathematics and is a method used to comprehensively evaluate objects affected by multiple factors. Fuzzy comprehensive evaluation is generally divided into a target layer and an indicator layer. By constructing a membership function, the membership vector of the target layer to the evaluation set can be determined, and the comprehensive evaluation result can be obtained according to the evaluation criteria. Fuzzy comprehensive evaluation is divided into two forms: primary fuzzy comprehensive evaluation and multi-level fuzzy comprehensive evaluation. The former is suitable for evaluation systems with simple structures and a small number of indicators; the latter is suitable for evaluation systems with complex structures and a large number of indicators, which helps to solve the problems of small weight distribution and information loss.

[0035] There are many parameters that affect the operating status of the main ventilation fan in mines, and each parameter reflects the operating status of the main ventilation fan to varying degrees. From the monitoring parameters of the main ventilation fan in mines, representative index parameters are selected to construct an evaluation index system for the operating status of the main ventilation fan in mines. The index system is divided into three layers, such as Figure 2 shown.

[0036] The first layer of the mine main ventilation fan operation status evaluation index system is the target layer X. The second layer is the object layer that decomposes the target layer into three main bodies, recorded as X = {X1, X2, X3} = {1-level motor, 2-level motor, air duct}. The third layer is the indicator layer, each object contains n indicators, specifically X1 = {X 11 ,X 12 ,X 13 ,X 14 ,X 15 ,X 16 ,X 17 ,X 18 ,X 19}={front axle temperature, rear axle temperature, stator temperature, motor voltage, A phase current, C phase current, active power, horizontal vibration, vertical vibration}; X2={X 21,X 22 ,X 23 ,X 24 ,X 25 ,X 26 ,X 27 ,X 28 ,X 29}={front axle temperature, rear axle temperature, stator temperature, motor voltage, A phase current, C phase current, active power, horizontal vibration, vertical vibration}; X3={X 31 ,X 32 ,X 33}={inlet static pressure, ventilation flow, static pressure efficiency}.

[0037] The operating status of the main ventilation fan in a mine presents a continuous and fuzzy transition process from operating status to failure, which is difficult to quantify accurately. Generally, the operating status of the main ventilation fan in a mine is evaluated according to the operating status level. The operating status of the main ventilation fan in a mine is divided into "operating status" and "good"

[0038] There are four levels: “Caution” and “Fault”, and the comment set V = {V1, V2, V3, V4} = {operating status, good, caution, fault}. The status description of each operating status level is shown in Table 1.

[0039] Table 1 Operation status level

[0040] Operation status level Status description <![CDATA[Health (V1)]]> The operating parameters are in ideal condition, with no abnormal signals or signs of failure. <![CDATA[Good (V2)]]> The operating parameters deviate from the ideal state, but it does not affect normal operation <![CDATA[Attention (V3)]]> The operating parameters deviate from the normal operating range and tend to deteriorate. <![CDATA[Fault (V4)]]> The operating parameters deviate from the normal values ​​and deteriorate significantly

[0041] For the determination of weights, the Analytic Hierarchy Process (AHP) is a method for solving complex decision-making problems, which was proposed by American operations researcher Thomas L. Saaty in the early 1970s. The AHP breaks down complex decision-making problems into smaller parts for analysis and evaluation, and then combines these parts to obtain a solution to the original problem. The core of the AHP is to build a hierarchical model, perform pairwise comparisons, calculate weights, and perform consistency tests.

[0042] As a common subjective weighting method, analytic hierarchy process is widely used in various fields. Its basic steps are:

[0043] (1) Establish a hierarchical model and decompose the evaluation object into multiple levels, which can usually be divided into the target level, object level and indicator level.

[0044] (2) Construct a pairwise comparison judgment matrix, compare each pair of elements in the object layer and indicator layer, and determine the relative importance of the elements with the help of expert experience. The 1-9 scale method proposed by Saaty is usually used to quantify the results of pairwise comparison. The judgment matrix scale and definition are shown in Table 2.

[0045] Table 2 Scale

[0046] Scale definition 1 Both elements are equally important 3 The former is slightly more important than the latter 5 The former is obviously more important than the latter 7 The former is more important than the latter 9 The former is extremely more important than the latter 2,4,6,8 The importance is the middle value of the above adjacent judgments Countdown from 1 to 9 The inverse relationship between the relative importance of the two

[0047] Construct the judgment matrix A according to the 1-9 scaling method:

[0048]

[0049] (3) Solve the maximum eigenvalue and eigenvector of the judgment matrix A, and normalize the eigenvector to obtain the weight of each indicator.

[0050] (4) Calculate the consistency index CI value using the following formula:

[0051]

[0052] In the formula, λ max is the maximum characteristic root to be solved, and n is the order.

[0053] Determine the random consistency index RI, which provides a standard consistency benchmark for judgment matrices of different orders, so that the consistency of judgment matrices of different orders can be compared fairly. The commonly used values ​​of RI are shown in Table 3:

[0054] Table 3 RI value

[0055] n 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49

[0056] The consistency ratio CR is calculated using the formula:

[0057]

[0058] The entropy weight method is an objective weighting method based on the principle of information entropy, which is widely used in multi-index comprehensive evaluation problems. Its core idea is to use the data distribution of the indicator itself to reveal the amount of information it carries, and then determine the relative importance of the indicator in the overall evaluation. The concept of information entropy is used as an indicator to measure the degree of disorder of indicator data. The lower the information entropy of an indicator, the greater the variability of its data, which can provide richer information, and the corresponding weight should be higher.

[0059] By accurately calculating the information entropy of each indicator, the entropy weight method can quantify the differences and information contribution between indicators, and further determine the weight coefficient of each indicator. The advantage of the entropy weight method lies in its objectivity. It is completely based on the actual data of the evaluation object, avoiding the deviation that may be caused by subjective evaluation methods, but relying entirely on data may also cause the weight solution to deviate from reality.

[0060] Assume that the original data X has m evaluation indicators, n samples, x ij represents the jth evaluation index in the i-th sample. The evaluation indexes need to be normalized to eliminate the dimension effect and convert the index values ​​to the [0,1] interval. Use the normalized data to calculate the weight p of each sample under each index. ij , the specific formula is:

[0061]

[0062] Calculate the information entropy e of each indicator using the weight j , the formula is:

[0063]

[0064] Through information entropy, the information utility value dj=1-ej of each indicator is calculated. The smaller the information entropy, the larger the information utility value, indicating that the indicator has a large variability and high difference. The entropy weight wj is calculated according to the information utility value of each indicator:

[0065]

[0066] Thus, the objective weights of each evaluation index based on the entropy weight method are obtained.

[0067] When faced with complex decision-making problems, a single subjective or objective weighting method is often difficult to fully reflect the true situation of the evaluation system. There are many indicator parameters for mine main ventilation fans, and different weights directly affect the final evaluation results of the main ventilation fans. Therefore, this application adopts a comprehensive weighting strategy, that is, by combining the subjective weights of the hierarchical analysis method with the objective weights of the entropy weight method, it not only retains the information contribution of each indicator in the overall evaluation, but also takes into account the judgment obtained from expert experience, so as to achieve a scientific and reasonable distribution of weights. The comprehensive weighting strategy uses the minimum discriminant information principle to calculate the combined weights. The weight calculation formula is:

[0068] Among them, w j is the combined weight; wj' is the weight of the hierarchical analysis method; wj* is the weight of the entropy weight method.

[0069] The weight calculated by the combined weighting method is always fixed. If the weight of the indicator is too small, even if the indicator is seriously deteriorated, it will have little effect on the final evaluation result, and information annihilation is very likely to occur. Therefore, during the operation of the main ventilation fan in the mine, the weight value corresponding to each evaluation indicator should be dynamically adjusted according to the change of the state parameter. When a certain evaluation indicator deteriorates significantly, the weight of the indicator should be increased to more accurately reflect the health status of the main ventilation fan. The variable weight formula selected is:

[0070]

[0071] Among them, w j , w j 0 is the variable weight and initial combined weight of the jth indicator; d j is the degradation degree of the jth indicator; σ is the variable weight coefficient, which takes a value of 2.

[0072] The degradation degree reflects the degradation degree of the current state of the main fan index parameter, and to a certain extent, it can reflect the health status of the main fan. The degradation degree ranges from [0,1]. The lower the degradation degree, the healthier the index parameter. According to the attributes of the index parameter, the monitoring parameters collected from the mine main fan SCADA system are divided into smaller and better indicators, intermediate optimal indicators, and larger and better indicators. The specific division is shown in Table 4.

[0073] Table 4 Index degradation function parameters

[0074]

[0075] The calculation formulas for the degradation degree of the three indicators are as follows:

[0076] The smaller the better indicator (6)

[0077] Intermediate optimal index (7)

[0078] The bigger the better indicator (8)

[0079] Among them, d(x) is the degradation degree of the evaluation index; x is the current value of the evaluation index; xmax, xmin are the maximum and minimum values ​​of the evaluation index threshold; xa, xb are the minimum and maximum values ​​of the optimal interval of the evaluation index.

[0080] The cloud model is a mathematical model used to deal with the relationship between fuzziness and randomness, proposed by Chinese scientist Li Deyi in 1995. It is based on the concept of membership theory and normal distribution cloud droplets, combining and improving fuzzy sets and probability statistics theory, aiming to describe the uncertainty in fuzzy phenomena.

[0081] The health status level of the main ventilation fan in the mine is divided into the corresponding level of degradation interval. Except for the two ends, the mathematical expectation is the middle value of the interval. The selection of entropy is usually determined by the formula, and the super entropy needs to be obtained with the help of expert experience and repeated experiments to obtain the formula.

[0082]

[0083] Where mmax and mmin are the upper and lower limits of the interval.

[0084] According to the above division method, the degradation degree interval of the health status level is divided, and the super entropy value H is determined by experience and repeated experiments. e =0.05, and finally the numerical characteristics corresponding to each health level are shown in Table 5.

[0085] Table 5 Numerical characteristics of cloud models

[0086]

[0087] In the drawing of the cloud model, the degradation value is used as the horizontal axis and the membership degree is used as the vertical axis. A graph is constructed to intuitively display the probability distribution of the mine main ventilation fan at different health levels. By specifying a specific degradation value, the membership degree corresponding to each health level of the point can be obtained.

[0088] In the cloud model, the membership corresponding to the degradation value is not fixed, but follows a specific probability distribution. The generation of each cloud droplet in the cloud model is random and follows the current distribution law of the data. Therefore, the cloud model can comprehensively show the randomness of the sample value and the uncertainty of its membership, effectively revealing the inherent connection between randomness and fuzziness.

[0089] Example

[0090] In order to verify the validity of the application submitted by the municipality, the data for 7 time periods between 9:00 (T1) on August 20, 2022 and 9:00 (T7) on August 21, 2022 were selected from the SCADA data of the main ventilation fan of Coal Mine 2. The interval between each set of data is 4 hours. The monitoring system did not issue an alarm at these 7 time periods. The specific data are shown in Table 6.

[0091] Table 6 SCADA data from T1 to T7

[0092] Indicator parameters <![CDATA[T1]]> <![CDATA[T2]]> <![CDATA[T3]]> <![CDATA[T4]]> <![CDATA[T5]]> <![CDATA[T6]]> <![CDATA[T7]]> <![CDATA[X 11 / ℃]]> 99.72 99.83 100.08 100.23 103.79 104.48 104.98 <![CDATA[X 12 / ℃]]> 45.32 45.29 45.47 46.33 48.03 48.84 49.52 <![CDATA[X 13 / ℃]]> 60.44 59.81 59.41 59.47 61.23 61.57 62.49 <![CDATA[X 14 / A]]> 18.9 18.86 18.83 19.42 19.88 21.02 20.68 <![CDATA[X 15 / A]]> 18.87 18.87 18.8 19.54 19.96 20.99 20.41 <![CDATA[X 16 / V]]> 10333.8 10205.4 10207.3 10119.7 10196.2 10288.9 10296.4 <![CDATA[X 17 / mm / s]]> 0.62 0.59 0.66 0.75 0.84 0.91 1.13 <![CDATA[X 18 / mm / s]]> 1.02 1.15 1.05 1.2 1.25 1.27 1.35 <![CDATA[X 19 / kW]]> 226.56 223.27 223.02 227.97 235.15 250.87 247.01 <![CDATA[X 21 / ℃]]> 78.58 79.87 83.74 88.81 91.38 95.39 101.39 <![CDATA[X 22 / ℃]]> 48.93 49.96 52.99 54.09 56.38 57.71 59.02 <![CDATA[X 23 / ℃]]> 59.49 61.98 64.9 67.25 70.48 75.82 79.67 <![CDATA[X 24 / A]]> 16.02 15.97 16.25 16.84 17.09 18.11 18.93 <![CDATA[X 25 / A]]> 16.05 15.95 16.53 17.07 17.08 18.18 19.11 <![CDATA[X 26 / V]]> 10321.8 10188.9 10184.2 10121.9 10202.9 10297.1 10293.2 <![CDATA[X 27 / mm / s]]> 0.89 0.85 1.08 1.12 1.49 1.69 1.82 <![CDATA[X 28 / mm / s]]> 1.05 1.13 1.45 1.55 1.72 1.89 2.02 <![CDATA[X 29 / kW]]> 164.14 162.46 165.69 170.43 174.37 186.48 184.55 <![CDATA[X 31 / KPa]]> 1.03 1.07 1.08 1.08 1.1 1.11 1.12 <![CDATA[X 32 / Km 3 / min]]> 10.34 10.31 10.28 10.35 10.38 10.46 10.53 <![CDATA[X 33 / %]]> 54.44 54.87 51.26 49.11 48.07 46.29 44.31

[0093] Regarding the weight processing, taking time T3 as an example, the variable weights are obtained for the indicator layer based on the combined weighting of the hierarchical analysis method and the entropy weight method. The results are shown in Table 7.

[0094] Table 7 Index parameter variable weight

[0095] Indicator parameters <![CDATA[Data at time T3]]> Degradation Initial portfolio weight Variable weight <![CDATA[X 11 / ℃]]> 100.08 0.004 0.09 0.0862 <![CDATA[X 12 / ℃]]> 45.47 0.028 0.1244 0.1248 <![CDATA[X 13 / ℃]]> 59.41 0 0.1769 0.168 <![CDATA[X 14 / A]]> 18.83 0 0.0418 0.0397 <![CDATA[X 15 / A]]> 18.8 0 0.022 0.0209 <![CDATA[X 16 / V]]> 10207.3 0.031 0.0634 0.064 <![CDATA[X 17 / mm / s]]> 0.66 0 0.2445 0.2322 <![CDATA[X 18 / mm / s]]> 1.05 0 0.1138 0.1081 <![CDATA[X 19 / kW]]> 223.02 0.155 0.1232 0.1561 <![CDATA[X 21 / ℃]]> 83.74 0.15 0.0758 0.0697 <![CDATA[X 22 / ℃]]> 52.99 0.199 0.1215 0.1217 <![CDATA[X 23 / ℃]]> 64.9 0.288 0.2063 0.2384 <![CDATA[X 24 / A]]> 16.25 0.061 0.0259 0.0203 <![CDATA[X 25 / A]]> 16.53 0.072 0.0205 0.0164 <![CDATA[X 26 / V]]> 10184.2 0.019 0.0436 0.0315 <![CDATA[X 27 / mm / s]]> 1.08 0.164 0.278 0.2621

[0096] The object layer cannot use the entropy weight method to calculate the objective weight, and only the hierarchical analysis method is used to determine the weight. The object layer weight is:

[0097] W=[0.420.330.25] (1)

[0098] Substitute the index degradation degree at time T3 into the membership cloud model and calculate the membership matrix of the three evaluation objects.

[0099] Table 8 Evaluation results from time T1 to T7

[0100] Indicator parameters <![CDATA[Data at time T3]]> Degradation Initial portfolio weight Variable weight <![CDATA[X 28 / mm / s]]> 1.45 0.302 0.1202 0.1418 <![CDATA[X 29 / kW]]> 165.69 0.108 0.1148 0.0981 <![CDATA[X 31 / KPa]]> 1.08 0.067 0.1641 0.1742 <![CDATA[X 32 / Km 3 / min]]> 10.28 0 0.2824 0.2635 <![CDATA[X 33 / %]]> 51.26 0.044 0.5535 0.5623

[0101] The specific health status assessment includes the following steps:

[0102] (1) Calculate the health status assessment vector of the evaluation object layer

[0103] Taking the 2-level motor X2 as an example, the health assessment formula for X2 is as follows:

[0104] B2=W2·R2=[0.18410.59410.01440]

[0105] Among them, B2 is the health status assessment vector of object X2; W2 is the variable weight of the evaluation index contained in object X2; R2 is the membership matrix of object X2.

[0106] (2) Calculation of the health assessment membership matrix of the mine main ventilation fan

[0107] The health status assessment vectors of objects X1 and X3 are calculated using the same method to obtain the health assessment membership matrix of the mine main ventilation fan. The result is shown in formula (4-19).

[0108]

[0109] (3) Calculation of the health status assessment vector of the main ventilation fan in the mine

[0110] Combined with the object layer weight W, the membership vector of the main ventilation fan in the mine at each health level at time T3 is obtained as follows:

[0111] B=W·R=[0.67860.21390.00490]

[0112] In the multi-level fuzzy evaluation, due to the layer-by-layer transmission of weights, some degradation parameters of the mine main ventilation fan are obscured at the final target level. This phenomenon of parameter degradation information annihilation is not conducive to the evaluation of the operating status of the mine main ventilation fan. According to the traditional evaluation criteria based on the "maximum membership principle", the comprehensive evaluation of the health status of the fan at time T3 is "healthy", but from the monitoring parameters of the main ventilation fan, it is found that the rear shaft temperature of the second-level motor at time T3 is higher than that of the previous time, and the evaluation result is obviously unreasonable. The reason is that the degradation degree of other indicator parameters is very low at this moment, which makes the degradation degree of the "healthy" level dominant, and dilutes the fact that the rear shaft temperature of the second-level motor is in an upward state. By consulting the literature and combining the characteristics of the indicator parameters of the mine main ventilation fan, it is finally determined that the comprehensive evaluation of the health status of the main ventilation fan adopts the "worst level item with a membership greater than 0.1". According to the selected evaluation criteria, the health status of the fan at time T3 is finally evaluated as "good". The fan status has declined to a certain extent, but it has not affected normal operation, which is consistent with the actual situation. The evaluation results from time T1 to T7 are determined using the evaluation criteria of "the worst grade item with a membership greater than 0.1", as shown in Table 9 below;

[0113] Table 9 Evaluation results from time T1 to T7

[0114]

[0115] The evaluation results show that the health status of the mine main ventilation fan has declined at T3, and from this moment on, the fan status continues to deteriorate. At T6, the fan status level initially changed to fault, and the fault level intensified at T7. After T7, the rear shaft temperature of the 2nd-level motor issued an over-limit temperature alarm. After inspection by the on-site guard, the cause was insufficient lubrication at the rear bearing end of the 2nd-level motor. After replenishment, the temperature gradually returned to normal. This shows that the proposed mine main ventilation fan health assessment model can deduce the trend of the main ventilation fan's impending failure in advance compared to the traditional fixed threshold alarm, and realize early warning during potential failures.

[0116] In order to further verify the evaluation model in this application and the advantages of the proposed mine main ventilation fan health assessment model, the evaluation model proposed in the existing public literature for the daily operation status assessment of wind turbines based on the improved degradation degree model is compared. In the daily operation status assessment of wind turbines based on the improved degradation degree model, only the analytic hierarchy process and the entropy weight method are used to fuse fixed weights without variable weight processing. The evaluation results of the normal cloud with fixed weights are shown in Table 10.

[0117] Table 10 Comparison of model evaluation results from time T1 to T7

[0118]

[0119] Combined with the above table, it can be seen that the evaluation results of the model used in the daily operation status evaluation of wind turbines based on the improved degradation model are lagging, and the health status of the ventilator is evaluated as "good" only at T4. Although the health status of the ventilator is also evaluated as "faulty" at T7, the membership corresponding to the fault at T7 of the present application model is 0.157, while the membership corresponding to the fault of the comparison model is only 0.118, slightly exceeding the evaluation criterion of 0.1. This is because the comparison model does not redistribute weights according to the degree of degradation of the indicator parameters, resulting in a small proportion of the indicator weight of the model degradation, and the membership corresponding to the low health level is small, which cannot detect the degradation of the mine main ventilation fan in time.

[0120] In summary, the model proposed in this application can timely detect the deterioration of the main ventilation fan in the mine, timely evaluate the health status of the main ventilation fan, and then issue early warning of the fault and take timely and effective defensive measures.

[0121] It should be noted that, in the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0122] The above embodiments are merely examples of the present invention and do not limit the protection scope of the present invention. All designs that are the same or similar to the present invention fall within the protection scope of the present invention.

Claims

1. A method for evaluating the operating status of a main fan, characterized in that: The following steps are involved: Acquire monitoring parameters of the main fan operating state, and select monitoring parameters with a correlation with the main fan operating state greater than a set value as indicator parameters; According to the index parameters combined with fuzzy comprehensive evaluation method, a main fan operation status evaluation model is constructed, and the index parameters are used as evaluation indicators of the main fan operation status evaluation model; Based on the indicator parameters, the initial weights of the indicators are determined by combining the analytic hierarchy process and the entropy weight method, and the weights are further revised by introducing the variable weight theory to obtain the revised weights of the indicator parameters; Determining the degradation degree of the indicator parameter according to the attribute of the indicator parameter and the revised weight, replacing the membership function in the main fan operation status evaluation model with the cloud model, and obtaining an updated main fan operation status evaluation model; The index parameters are input into the updated main fan operation status evaluation model to obtain the main fan operation status result.

2. The method for evaluating the operating status of the main fan according to claim 1, characterized in that: The combined weighting is determined by the minimum identification information to determine the combined weight, and the calculation formula is: Among them, wj is the combined weight; wj' is the weight of the hierarchical analysis method; wj* is the weight of the entropy weight method, and m is the number of evaluation indicators.

3. The method for evaluating the operating status of the main fan according to claim 1, characterized in that: The introduction of variable weight theory further revises the weights, and the revised formula is: Among them, wj and are the variable weight and initial combination weight of the j-th indicator respectively; dj is the degradation degree of the j-th indicator; σ is the variable weight coefficient, which is 2, and m is the number of evaluation indicators.

4. The method for evaluating the operating status of the main fan according to claim 1, characterized in that: The membership function in the main fan operation status evaluation model is replaced by a cloud model to obtain an updated main fan operation status evaluation model, including the following steps: Determine the three parameters of the cloud model: expected Ex, entropy En and excess entropy He; The membership of each indicator parameter is calculated using the cloud model; The membership value of the main fan operation status evaluation model is replaced by the membership calculated by the cloud model to obtain an updated main fan operation status evaluation model.

5. The method for evaluating the operating status of the main fan according to claim 1, characterized in that: The degradation degree ranges from [0, 1], and the lower the degradation degree, the healthier the indicator parameter.

6. The method for evaluating the operating status of the main fan according to claim 1, characterized in that: The main fan operation status is divided into four levels, namely healthy state, good state, caution state and fault state; The healthy state means that all operating parameters are in an ideal state, with no abnormal signals or signs of failure; The good state is when the operating parameters deviate from the ideal state but do not affect normal operation; The caution state means that the operating parameters deviate from the normal operating range and have a tendency to deteriorate; The operating parameters in the fault state deviate from normal values ​​and deteriorate significantly.

7. A fuzzy comprehensive evaluation system for the operating status of the main fan, characterized in that: include: A data acquisition module is used to obtain monitoring parameters of the main fan operation status, and select monitoring parameters with a correlation with the main fan operation status greater than a set value as indicator parameters; A data model construction module is used to construct a main fan operation status evaluation model according to the indicator parameters combined with fuzzy comprehensive evaluation, and use the monitoring parameters as evaluation indicators of the main fan operation status evaluation model; based on the monitoring parameters, the hierarchical analysis method and the entropy weight method are used for combined weighting, and the variable weight theory is introduced to further revise the weights to obtain the revised weights of the monitoring parameters; according to the attributes of the indicator parameters, the degradation degree of the indicator parameters is determined, and the membership function in the main fan operation status evaluation model is replaced by the cloud model to obtain the updated main fan operation status evaluation model; The data processing module is used to input the index parameters into the updated main fan operation status evaluation model to obtain the main fan operation status result.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of claims 1 to 5 is implemented.

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