A method and system for evaluating the residual life of a lining plate of a transfer device in the field of coal conveying

Abstract

The present application belongs to the technical field of liner life evaluation, and relates to a method and system for evaluating the residual life of a liner of a transfer equipment in the field of coal conveying. The method comprises determining the degradation mechanism of the liner and its key parameters; using a method combining orthogonal test and grey correlation analysis to quantify the influence weight of each degradation mechanism on the life of the liner; constructing a degradation model corresponding to different degradation mechanisms to obtain the degradation amount under different degradation mechanisms respectively; combining the degradation amount under different degradation mechanisms with the influence weight to construct a multi-factor coupled degradation model of the liner and obtain the total degradation amount of the liner at present; based on the maximum allowed degradation thickness of the liner, the total degradation amount of the liner at present and the current cumulative coal passing amount, the residual allowed cumulative coal passing amount is calculated, which represents the residual life of the liner. The present application can significantly improve the prediction accuracy of the residual life of the liner, thereby reducing the operation and maintenance cost of the liner and avoiding production loss caused by unplanned shutdown.

Description

Technical Field

[0001] This invention belongs to the technical field of liner life assessment, specifically relating to a method and system for assessing the remaining life of liners in coal conveying transfer equipment. Background Technology

[0002] Transfer equipment is a key hub in the coal transportation chain, undertaking the core functions of material transfer, drop buffering, and direction adjustment. As the core protective component of transfer equipment, the liner directly bears multiple loads such as impact, wear, and corrosion from coal. Its service condition determines the operation and maintenance cost of transfer equipment and the reliability of the transportation system.

[0003] Compared to general bulk materials such as ore and sand, coal transportation has significant unique characteristics, including strong adhesion, high moisture content, and susceptibility to spontaneous combustion. This leads to more complex liner failure mechanisms and greater lifespan fluctuations, a problem particularly pronounced in high-moisture, high-viscosity coal transportation scenarios. Currently, the coal industry generally adopts a crude "replace when it fails" approach to managing liners on transfer equipment, which suffers from three major pain points: First, there is a lack of targeted research on the characteristics of bulk coal transportation, and no customized management model that is clearly different from that for bulk materials such as grain and ore. Second, the impact of various coal characteristics on liner lifespan is not clearly quantified, making accurate lifespan prediction impossible. Third, a lifespan management system is lacking, failing to form a closed-loop control system of "selection and commissioning - operation monitoring - maintenance and replacement - scrapping and recycling." Consequently, the existing model not only results in passive liner replacement and high maintenance costs, but also easily leads to a chain of failures such as material spillage, belt misalignment, and equipment jamming due to sudden liner failure, which can cause unplanned downtime and affect production operations in severe cases.

[0004] In the existing technology, most studies on liner life assessment focus on equipment such as ball mills. Their baseline degradation models mostly only consider operating time and do not take into account the core operating condition parameter of bulk material conveying. However, the basic wear of the liner is essentially determined by the total amount of material passing through it. Parameters such as belt conveyor speed, bulk coal flow rate, and throughput per second directly reflect the basic load strength borne by the liner. Ignoring these parameters will cause the baseline degradation calculation to deviate from reality, thus affecting the accuracy of liner life prediction. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for assessing the remaining lifespan of lining plates in coal conveying equipment, aiming to solve the problem of high maintenance costs caused by inaccurate prediction of lining plate lifespan in existing technologies.

[0006] This invention provides a method for assessing the remaining service life of liners in coal conveying equipment, comprising the following steps: S1. Determine the degradation mechanism and key parameters of the liner; the degradation mechanism includes the baseline physical degradation mechanism, impact degradation mechanism, corrosion degradation mechanism, and adhesion degradation mechanism; the key parameters of the baseline physical degradation mechanism include the throughput of loose coal per second; the key parameters of the impact degradation mechanism include the content of large pieces of material and the impact load; the key parameters of the corrosion degradation mechanism include the moisture content of coal and the concentration of heavy metal ions; the key parameters of the adhesion degradation mechanism include the amount of coal slag adhered and the belt speed of the conveyor. S2. Using a combination of orthogonal experiments and grey relational analysis, the influence weights of baseline physical degradation mechanism, impact degradation mechanism, corrosion degradation mechanism, and adhesion degradation mechanism on the liner life are quantified. , , and ; S3. Construct a baseline physical degradation model, an impact degradation model, a corrosion degradation model, and an adhesion degradation model to obtain the current baseline physical degradation amount of the liner. Impact degradation Corrosion and degradation and the amount of adhesion degradation ; S4. Construct a multi-factor coupled degradation model for the liner to calculate the current total degradation of the liner. , expressed as equation (1); (1); S5. Calculate the remaining allowable cumulative amount of loose coal passing through according to formula (2). and with Characterizes the remaining life of the liner; among which, For the maximum allowable degradation thickness of the liner, This represents the current cumulative volume of loose coal passing through. (2).

[0007] In some embodiments, the baseline physical degradation model in step S3 is expressed as equation (3); wherein, The reference coefficient for the lining material is... This refers to the throughput of loose coal per second. (3).

[0008] In some embodiments, the impact degradation model in step S3 is expressed as equation (4); wherein, The impact degradation coefficient, For bulk material content, Impact load; (4).

[0009] In some embodiments, the corrosion degradation model in step S3 is expressed as equation (5); wherein, The corrosion degradation coefficient is . The moisture content of coal, This represents the concentration of heavy metal ions. (5).

[0010] In some embodiments, the attachment degradation model in step S3 is expressed as equation (6); wherein, This is the wear ratio coefficient. The amount of coal slag adhering to the surface. For the belt speed of the conveyor belt; (6).

[0011] In some embodiments, in step S5, the liner is allowed a maximum degradation thickness. It is 30% of the initial thickness of the liner.

[0012] In some embodiments, in step S2, the orthogonal experiment sets three levels for the key parameters corresponding to the four degradation mechanisms, and adopts... Eighteen test combinations were planned using a standard orthogonal array. The orthogonal experiment used a fixed cumulative coal throughput as the control benchmark to fully reproduce the real working conditions of coal conveying, impact, corrosion, and adhesion on the liner wear simulation test bench. The test parameters were adjusted group by group, and the wear rate per unit throughput of the liner was measured using a rangefinder. As evaluation indicators, among them... For the experimental group number, .

[0013] In some embodiments, in step S2, the grey relational analysis is performed based on orthogonal experimental data, including the following steps: Construct a data sequence, which includes a reference sequence. and comparison sequences ;in, , ~ The test parameters correspond to the baseline physical degradation, impact degradation, corrosion degradation, and adhesion degradation, respectively; The data is dimensionless by means of the mean, as expressed in equation (7), where, ; (7); Calculate the correlation coefficient according to equation (8). ,in, The resolution coefficient, Then, the correlation degree is calculated according to equation (9). ; (8); (9); The influence weights of each degradation mechanism are calculated according to equation (10). , (10).

[0014] This invention also provides a system for assessing the remaining service life of linings for transfer equipment in the coal conveying industry, used to perform the above-described method for assessing the remaining service life of linings for transfer equipment in the coal conveying industry. The system for assessing the remaining service life of linings for transfer equipment in the coal conveying industry includes: The data acquisition module is used to collect key parameters during bulk coal transportation, including the content of large material, the impact load under the preset transfer drop, the moisture content of coal, the concentration of heavy metal ions, and the amount of coal slag adhering; it is also used to collect key parameters during belt conveyor operation, including the belt conveyor's drum diameter, drum speed, belt speed, and the amount of bulk coal passing through per second. The experimental analysis module is used to conduct multi-parameter, multi-level orthogonal experiments and grey relational analysis to quantify the influence weights of different degradation mechanisms on the liner life. The model building module is used to build degradation models corresponding to different degradation mechanisms to obtain the degradation amount under different degradation mechanisms; it is also used to combine the degradation amount under different degradation mechanisms with the influence weights to build a multi-factor coupled degradation model of the liner to obtain the current total degradation of the liner. The remaining life prediction module is used to calculate the remaining allowable cumulative coal throughput based on the maximum allowable degradation thickness of the liner, the current total degradation of the liner, and the current cumulative coal throughput, and thus characterize the remaining life of the liner.

[0015] Based on the above technical solutions, the method and system for assessing the remaining life of coal conveying transfer equipment liners in this embodiment of the invention clarifies four types of degradation mechanisms of transfer equipment liners and their key parameters. Through orthogonal experiments and grey relational analysis, the influence weight of each degradation mechanism is quantified, thereby constructing a multi-factor coupled degradation model for the liner. Finally, the remaining allowable cumulative bulk coal throughput is calculated by combining the maximum allowable degradation thickness of the liner, and the remaining life of the liner is characterized by this method. This achieves accurate assessment of the remaining life of the liner, providing reliable support for the precise management and optimized design of coal conveying transfer equipment liners. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the method for assessing the remaining life of lining plates in coal conveying equipment according to the present invention. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that the terms "center", "lateral", "longitudinal", "upper", "lower", "top", "bottom", "inner", "outer", "left", "right", "front", "rear", "vertical", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] The terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] refer to Figure 1 As shown, the present invention provides a method for assessing the remaining service life of lining plates in coal conveying equipment, comprising the following steps S0 to S5.

[0022] Step S0: Analyze the specific impact characteristics of coal conveying equipment liners. Specifically, poor ventilation and susceptibility to spontaneous combustion after coal accumulation necessitate long-term protective measures such as spraying during storage and transportation, leading to increased moisture content in loose coal and triggering a series of specific problems. These problems include: 1) Coal agglomeration forming large lumps: When the moisture content of loose coal exceeds 8%, a continuous water film forms on the particle surface. Combined with its own clay minerals, organic matter, and other components, it agglomerates and adheres to fine particles through capillary adhesion, gradually forming large lumps with a particle size of 50mm to 200mm. These large lumps exert concentrated impact loads on the liner, with impact pressures reaching 0.8MPa to 1.2MPa, far exceeding the impact load of uniform loose material. This easily leads to localized plastic deformation and crack initiation in the liner, and may also cause blockages and exacerbate compression wear; 2) Corrosiveness and adhesion of water-containing coal: High moisture content causes sulfur-containing coal to form on the liner surface. Electrolyte solutions containing compounds and chloride ions trigger electrochemical corrosion. Sulfate ions generated from the oxidation of pyrite in coal further accelerate the corrosion process. At the same time, the strong adhesion caused by high water content will leave a large amount of coal slag on the surface of the liner, forming an adhesion layer with a thickness of 5mm to 15mm, which hinders heat dissipation of the liner, accelerates material aging, and also forms a "material-on-material" wear mechanism, which greatly increases the wear rate of the liner. 3) Uneven impact of bulk coal transportation: Coal density is lower than that of bulk materials such as ore. During the transfer process, it is affected by drop, belt speed, and belt offset. The throwing angle can reach 28° to 35°, and the throwing distance is 3.2m to 4.0m. The impact area is dispersed and uneven, resulting in the wear of the liner in some areas being 2.3 times to 3.1 times that of the uniform wear area. This leads to local excessive wear, stress concentration, and crack propagation.

[0023] Step S1: Based on the analysis of the specific influence characteristics of the lining plates of transfer equipment in the coal conveying field in Step S0, determine the degradation mechanism and its key parameters of the lining plates. The degradation mechanism includes the baseline physical degradation mechanism, the impact degradation mechanism, the corrosion degradation mechanism, and the adhesion degradation mechanism. The key parameters of the baseline physical degradation mechanism include the throughput of loose coal per second; the key parameters of the impact degradation mechanism include the content of large material and the impact load; the key parameters of the corrosion degradation mechanism include the moisture content of coal and the concentration of heavy metal ions; and the key parameters of the adhesion degradation mechanism include the amount of coal slag adhesion and the belt speed of the conveyor belt.

[0024] Step S2: Using a combination of orthogonal experiments and grey relational analysis, the influence weights of the baseline physical degradation mechanism, impact degradation mechanism, corrosion degradation mechanism, and adhesion degradation mechanism on the liner life are quantified. , , and .

[0025] To further explain, the orthogonal experiment selected the following parameters corresponding to each degradation mechanism: coal throughput per second, content of large materials and impact load, coal moisture content and heavy metal ion concentration, coal slag adhesion and conveyor belt speed. The wear rate per unit throughput of the liner was used as the evaluation index. Specifically, the orthogonal experimental design was employed to conduct multi-parameter, multi-level experiments on the four degradation mechanisms of liner plates in coal conveying equipment. During the orthogonal experiment, based on the actual industrial service conditions, three levels (low, medium, and high) were set for the key parameters corresponding to each degradation mechanism. The standard orthogonal array was used to plan 18 test combinations, with each test combination allowed for three parallel repetitions to reduce random errors. The orthogonal experiment used a fixed cumulative coal throughput as the control benchmark, and fully reproduced the real working conditions of coal conveying, impact, corrosion, and adhesion on the liner wear simulation test bench. The test parameters were adjusted group by group, and a rangefinder was used to measure the wear rate of the liner per unit throughput. As evaluation indicators, among them... For the experimental group number, The specific operation of orthogonal experiments is well known to those skilled in the art and will not be described in detail here. Through this standardized test, the wear rate data of the unit throughput of the liner is obtained, and the interference of the running time variable on the wear results is eliminated throughout the process. This provides real, reliable, and unbiased raw data support for subsequent grey relational analysis, and lays the foundation for the accurate quantification of the influence weights of various degradation mechanisms.

[0026] To further explain, the grey relational analysis is based on orthogonal experimental data. The data undergoes in-depth processing, using the wear rate per unit throughput of the liner as the reference sequence and the experimental parameters corresponding to the four major degradation mechanisms as the comparison sequence. The data is dimensionless through mean normalization to eliminate differences in dimensions and orders of magnitude. Then, the correlation coefficient and correlation degree are calculated, and normalization is performed to obtain the influence weights of each degradation mechanism on the liner's lifespan. This analysis process is entirely based on objective calculations of the experimental data, without subjective assignment or manual correction, ensuring the scientific validity and engineering applicability of the weighting results.

[0027] The specific operation of grey relational analysis is well known to those skilled in the art. The steps are briefly described below: 1) Construct a data sequence, which includes a reference sequence. and comparison sequences ;in, , ~ The test parameters correspond to the baseline physical degradation, impact degradation, corrosion degradation, and adhesion degradation, respectively; 2) The data is dimensionless by means of the mean, in order to eliminate dimensional differences, as expressed in equation (7), where, ; (7); 3) Calculate the correlation coefficient according to equation (8). ,in, The resolution coefficient is used for control. Discrimination, usually ; (8); 4) Calculate the correlation degree according to formula (9) Relevance The larger the value, the greater the impact of the key parameters corresponding to its degradation mechanism on the liner life. (9); 5) Based on the concept of weight normalization, the influence weight of each degradation mechanism is calculated according to equation (10). Thus, the influence weights of the baseline physical degradation mechanism, impact degradation mechanism, corrosion degradation mechanism, and adhesion degradation mechanism on the liner life are obtained. , , and ; (10).

[0028] In some embodiments, the influence weights of each degradation mechanism of the liner obtained by orthogonal experiment and grey relational analysis are specifically the influence weights of the baseline physical degradation mechanism. Impact weight of the degradation mechanism The influence weight of corrosion degradation mechanism Weight of the impact of attachment degradation mechanism .

[0029] Step S3: Construct a baseline physical degradation model, an impact degradation model, a corrosion degradation model, and an adhesion degradation model to obtain the current baseline physical degradation amount of the liner. Impact degradation Corrosion and degradation and the amount of adhesion degradation All degradation measurements are in mm.

[0030] Specifically, the baseline physical degradation model characterizes the natural wear of the liner caused by friction and impact between the material and the liner base during bulk material conveying under normal operating conditions. Its degradation rate is directly related to the bulk material conveying volume (the larger the conveying volume, the more severe the base wear), and is also related to the liner material properties and operating time. The baseline physical degradation model is constructed based on the bulk material conveying volume and is expressed as Equation (3); where, The reference coefficient for the lining material (unit: ), The throughput of loose coal per second (unit: ), The current cumulative volume of loose coal passing through (unit: ); (3).

[0031] It should be noted that the lining material reference coefficient Pre-calibration was performed through experiments; specifically, when the liner is a stainless steel-ceramic composite plate, When the liner is made of ordinary wear-resistant steel plate, Loose coal throughput per second Based on the instantaneous flow rate and historical cumulative flow rate of the belt scale at the site, the average throughput per second was calculated. Through long-term tracking and recording at the test site, the instantaneous flow rate is usually within the range of 0.8t to 1.2t. Therefore, by using the bulk material conveying capacity as the core parameter of the benchmark physical degradation model, the throughput per second of bulk coal was calculated and then converted into the benchmark physical degradation value. This yielded a benchmark physical degradation model based on the liner material and conveying capacity, solving the problem of bias in basic wear assessment caused by the traditional model relying solely on operating time. This makes the benchmark physical degradation model more realistic and the calculation of the benchmark physical degradation value more scientific and practical in engineering.

[0032] To further explain, 0.5 in equation (3) is the instantaneous throughput exponent, and its value is determined by fixing... Set up simulation group Calculate the corresponding Double log-linear fitting was used. ,draw The slope of the curve is obtained through linear regression. Rounded down to 0.5, its goodness of fit is... In equation (3), 0.8 is the cumulative throughput index, which is determined by fixing... Set up simulation group ,calculate Power function fitting Nonlinear fitting yields the exponential Rounded down to 0.8.

[0033] Specifically, the impact degradation model characterizes the local plastic deformation and crack propagation of the liner caused by concentrated impact from large materials. Its degradation rate is positively correlated with the content of large materials and the impact load. The impact degradation model is expressed as equation (4); where, Impact degradation coefficient (unit: ), This represents the content of bulk materials (in %, using the percentage value before the percentage). Impact load (unit: N); (4).

[0034] To further explain, the impact degradation coefficient Through experimental calculations, specifically, measurements were obtained from actual operating conditions. Record the content of bulk materials, impact load, and cumulative throughput. Calculate the average impact degradation coefficient from multiple sets of experiments to obtain the impact degradation coefficient. In equation (4), 0.7 represents the impact load index. This value is determined by fixing the experimental conditions and performing tests and calculations. Using double logarithmic fitting, plotting The curve yielded a linear regression slope of 0.697, rounded to 0.7, indicating a goodness of fit. =0.981.

[0035] Specifically, the corrosion degradation model characterizes sulfides and heavy metal ions (such as Fe) in coal. 3+ Mn 2+ The corrosive liquid formed by the combination of water and molten metal accelerates the electrochemical corrosion of the lining; the corrosion degradation model is expressed as equation (5); where, Corrosion degradation coefficient (unit: ), This refers to the moisture content of coal (in %, taking the value before the percentage). Heavy metal ion concentration (unit: ); (5).

[0036] To further explain, the corrosion degradation coefficient Through experimental calculations, specifically, simulations were performed based on actual working conditions and measurement results, with the measurement results then fixed. Record the moisture content of coal With heavy metal ion concentration Calculate the mean corrosion degradation coefficient to obtain the corrosion degradation coefficient. In equation (5), 0.08 represents the moisture content index and 0.03 represents the ion index. The method for determining these values ​​is to fix the experimental conditions and calculate... Exponential fitting was used. and The exponential regression coefficients were obtained. and The values ​​are rounded down to 0.08 and 0.03 respectively.

[0037] Specifically, the adhesion degradation model characterizes how the coal slag adhesion layer alters the stress state of the liner, creating additional wear loads. The degradation rate is positively correlated with the adhesion amount and belt speed. The adhesion degradation model is expressed as equation (6); where, Wear ratio coefficient (unit: ), Coal slag adhesion amount (unit: ), Belt speed of the conveyor belt (unit: ); (6).

[0038] To further explain, the wear ratio coefficient Through experimental calculations, specifically, coefficient simulation calculations were performed based on actual working condition measurement results. With records , , The wear ratio coefficient was calculated by multi-group experiments to obtain the average wear ratio coefficient. In equation (6), 0.8 represents the coal slag adhesion index, which is determined by fixing... , Set up multiple control groups ,calculate Power function fitting The exponent is obtained through nonlinear fitting. Rounded to 0.8, the goodness of fit is... =0.968.

[0039] Step S4: Combine the degradation amount under different degradation mechanisms with the influence weights to construct a multi-factor coupled degradation model for the liner, in order to calculate the current total degradation of the liner. , expressed as equation (1); (1); By weighted calculation of degradation amount under each degradation mechanism, the baseline physical degradation amount, impact degradation amount, corrosion degradation amount and adhesion degradation amount are superimposed and coupled to obtain the current total degradation amount of the liner.

[0040] Step S5: Based on the maximum allowable degradation thickness of the liner, the current total degradation of the liner, and the current cumulative loose coal throughput, calculate the remaining allowable cumulative loose coal throughput according to formula (2). and with Characterizes the remaining life of the liner; among which, For the maximum allowable degradation thickness of the liner, This represents the current cumulative volume of loose coal passing through. The cumulative average degradation rate; specifically, the maximum allowable degradation thickness of the liner. It can be set as the initial thickness of the liner. 30%; (2).

[0041] The above illustrative embodiments clarify the four types of degradation mechanisms of the liner and their key parameters, and quantify the influence weight of different degradation mechanisms on the liner life. This clarifies the contribution ratio of each degradation mechanism to the liner life attenuation. Based on this, the degradation amount under each degradation mechanism is weighted to construct a multi-factor coupled degradation model for the liner, obtain the current total degradation of the liner, and calculate the remaining liner life. Therefore, the multi-factor coupled degradation model can better fit the actual working conditions of coal transportation, significantly improving the prediction accuracy of the liner life. Moreover, this embodiment uses the remaining allowable cumulative loose coal throughput to characterize the remaining liner life, breaking through the conventional approach of using the remaining operating time to characterize the remaining liner life in existing technologies. This makes the prediction results of the remaining liner life more intuitive and effective, providing a quantitative basis for liner protection optimization and operation and maintenance cycle formulation, and realizing the effective implementation of the life prediction model from theoretical calculation to engineering application.

[0042] This invention also provides a system for assessing the remaining service life of lining plates for transfer equipment in the field of coal transportation, used to perform the aforementioned method for assessing the remaining service life of lining plates for transfer equipment in the field of coal transportation; the system includes a data acquisition module, an experimental analysis module, a model building module, and a remaining service life prediction module.

[0043] The data acquisition module collects key parameters during bulk coal transportation, including the content of large materials, impact load under a preset transfer drop, coal moisture content, heavy metal ion concentration, and coal slag adhesion. It also collects key parameters during belt conveyor operation, including drum diameter, drum speed, belt speed, and bulk coal throughput per second. The experimental analysis module conducts multi-parameter, multi-level orthogonal experiments and grey relational analysis to quantify the influence weights of different degradation mechanisms on liner life. The model building module constructs degradation models corresponding to different degradation mechanisms to obtain the degradation amount under each mechanism. It also combines the degradation amounts under different mechanisms with influence weights to construct a multi-factor coupled degradation model for the liner to obtain the current total degradation of the liner. The remaining life prediction module calculates the remaining allowable cumulative bulk coal throughput based on the maximum allowable degradation thickness of the liner, the current total degradation of the liner, and the current cumulative bulk coal throughput, thus characterizing the remaining life of the liner.

[0044] To illustrate the technical effects of the present invention, two embodiments are presented and described below.

[0045] Example 1: This embodiment uses a stainless steel-ceramic composite liner at a transfer point of a belt conveyor as the evaluation object. The initial thickness of the liner is... Maximum allowable degradation thickness (i.e., 30% of the initial thickness of the lining), based on the current cumulative throughput of loose coal. In the case of lining plate remaining service life assessment, the following steps are included: Step S1: Collect key parameters of the belt conveyor and bulk coal during bulk coal transportation, including coal characteristics: moisture content. Large material content Impact load corresponding to the drop height heavy metal ion concentration Coal slag adhesion amount Belt conveyor parameters: Drum diameter Drum speed belt speed Loose coal throughput per second ; Step S2: Determine the influence weight of each degradation mechanism through orthogonal experiments and grey relational analysis: influence weight of the baseline physical degradation mechanism. Impact weight of the degradation mechanism The influence weight of corrosion degradation mechanism Weight of the impact of attachment degradation mechanism ; Step S3: Calculate the degradation amount under each degradation mechanism (the lining plate is a stainless steel-ceramic composite plate, and the material reference coefficient is used). ): 1) Baseline physical degradation: ; 2) Impact degradation: ; 3) Corrosion degradation amount: ; 4) Additional degradation due to adhesion: .

[0046] Step S4: Calculate the current total degradation of the liner: ; Step S5: Calculate the remaining life of the liner: ; Based on actual on-site monitoring, the lining plate of Example 1 passed the cumulative test... After reaching the preset wear limit, the prediction error was 1.7%, proving the accuracy of the method of the present invention.

[0047] Example 2: This embodiment uses a common wear-resistant steel liner at a transfer point of a belt conveyor as the evaluation object, with an initial liner thickness of... Maximum allowable degradation thickness (i.e., 30% of the initial thickness of the lining), based on the current cumulative throughput of loose coal. In the case of lining plate remaining service life assessment, the following steps are included: Step S1: Collect key parameters of the belt conveyor and bulk coal during bulk coal transportation, including coal characteristics: moisture content. Large material content Impact load corresponding to the drop height heavy metal ion concentration Coal slag adhesion amount Belt conveyor parameters: Drum diameter Drum speed belt speed Loose coal throughput per second ; Step S2: Determine the influence weight of each degradation mechanism through orthogonal experiments and grey relational analysis: influence weight of the baseline physical degradation mechanism. Impact weight of the degradation mechanism The influence weight of corrosion degradation mechanism Weight of the impact of attachment degradation mechanism ; Step S3: Calculate the degradation amount under each degradation mechanism (the liner is ordinary wear-resistant steel plate, material reference coefficient). ): 1) Baseline physical degradation: ; 2) Impact degradation: ; 3) Corrosion degradation amount: ; 4) Additional degradation due to adhesion: .

[0048] Step S4: Calculate the current total degradation of the liner: ; Step S5: Calculate the remaining life of the liner: ; Based on actual on-site monitoring, the lining plate of Example 2 passed the cumulative test... The device eventually reached the preset wear limit with a prediction error of 2.6%, verifying the accuracy of the method of the present invention.

[0049] Through the description of several embodiments of the method and system for assessing the remaining service life of lining plates in coal conveying equipment according to the present invention, it can be seen that the present invention has at least one or more of the following advantages: 1) Based on fully considering the specific characteristics of coal transportation, this invention adapts to the working conditions of strong adhesion, high moisture content, and uneven impact in coal transportation, clarifies the four types of degradation mechanisms of the liner and their key parameters, and solves the problem of poor applicability of existing liner life assessment methods. 2) This invention accurately quantifies the influence weight of each degradation mechanism on the liner life through orthogonal experiments and grey relational analysis, and constructs a multi-factor coupled degradation model of the liner to better fit the actual working conditions of coal transportation, thereby enabling accurate prediction of the remaining life of the liner; experimental verification shows that the prediction error is less than 6%, the mean absolute percentage error (MAPE) is 3.7%, and the prediction accuracy is over 92.3%. 3) Because this invention can accurately predict the remaining lifespan of the liner, it can formulate maintenance and replacement plans in advance, achieve precise replacement of the liner, avoid material waste caused by premature replacement of the liner, optimize the liner replacement cycle by more than 20%, reduce operation and maintenance costs by more than 25%, and has significant economic benefits; it also avoids the failure of the liner caused by sudden failure when it is replaced too late, which may lead to failures such as material spillage, belt misalignment, and equipment jamming, thus ensuring the safe and stable operation of the coal conveying system and reducing production losses caused by unplanned downtime; 4) This invention solves the technical problems of extensive management of liner plates in existing coal conveying and transshipment equipment, failure to consider bulk material conveying volume, inaccurate life prediction, and high operation and maintenance costs. It realizes accurate assessment of the remaining life of the liner plates, providing reliable support for the precise management and optimized design of liner plates in coal conveying and transshipment equipment.

[0050] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0051] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for assessing the remaining service life of liners in transshipment equipment in the field of coal conveying, characterized in that, Includes the following steps: S1. Determine the degradation mechanism and key parameters of the liner; the degradation mechanism includes the baseline physical degradation mechanism, impact degradation mechanism, corrosion degradation mechanism, and adhesion degradation mechanism; the key parameters of the baseline physical degradation mechanism include the throughput of loose coal per second; the key parameters of the impact degradation mechanism include the content of large material and the impact load; the key parameters of the corrosion degradation mechanism include the moisture content of coal and the concentration of heavy metal ions; the key parameters of the adhesion degradation mechanism include the amount of coal slag adhered and the belt speed of the conveyor. S2. Using a combination of orthogonal experiments and grey relational analysis, the influence weights of baseline physical degradation mechanism, impact degradation mechanism, corrosion degradation mechanism, and adhesion degradation mechanism on the liner life are quantified. , , and The orthogonal experiment selects key parameters corresponding to each degradation mechanism as experimental parameters, and uses the wear rate per unit throughput of the liner as the evaluation index. The grey relational analysis is based on the orthogonal experimental data, using the wear rate per unit throughput of the liner as the reference sequence and the experimental parameters corresponding to each degradation mechanism as the comparison sequence. The data is dimensionless by means of the mean value method, and then the correlation coefficient and correlation degree are calculated. After normalization, the influence weight of each degradation mechanism on the liner life is obtained. S3. Construct a baseline physical degradation model, an impact degradation model, a corrosion degradation model, and an adhesion degradation model to obtain the current baseline physical degradation amount of the liner. Impact degradation Corrosion and degradation and the amount of adhesion degradation The baseline physical degradation model is expressed as equation (3), the impact degradation model as equation (4), the corrosion degradation model as equation (5), and the adhesion degradation model as equation (6), wherein, The reference coefficient for the lining material is... This refers to the throughput of loose coal per second. The impact degradation coefficient, For bulk material content, For impact load, The corrosion degradation coefficient is . For the moisture content of coal, This represents the concentration of heavy metal ions. This is the wear ratio coefficient. The amount of coal slag adhering to the surface. For the belt speed of the conveyor belt, This represents the current cumulative volume of loose coal passing through. (3)； (4)； (5)； (6)； S4. Construct a multi-factor coupled degradation model for the liner to calculate the current total degradation of the liner. , expressed as equation (1); (1)； S5. Calculate the remaining allowable cumulative amount of loose coal passing through according to formula (2). and with Characterizes the remaining life of the liner; among which, For the maximum allowable degradation thickness of the liner, It is 30% of the initial thickness of the liner; This represents the current cumulative volume of loose coal passing through. (2)。 2. The method for assessing the remaining service life of lining plates in coal conveying equipment according to claim 1, characterized in that, In step S2, the orthogonal experiment sets three levels for the key parameters corresponding to the four degradation mechanisms, and adopts... Eighteen test combinations were planned using a standard orthogonal array. These orthogonal experiments used a fixed cumulative coal throughput as the control benchmark, fully replicating the real-world conditions of coal conveying, impact, corrosion, and adhesion on a liner wear simulation test bench. Test parameters were adjusted group by group, and a rangefinder was used to measure the wear rate per unit throughput of the liner. As evaluation indicators, among them... For the experimental group number, .

3. The method for assessing the remaining service life of lining plates in coal conveying equipment according to claim 2, characterized in that, In step S2, the grey relational analysis is performed based on orthogonal experimental data, and includes the following steps: Construct a data sequence, the data sequence including a reference sequence. and comparison sequences ;in, , ~ The test parameters correspond to the baseline physical degradation, impact degradation, corrosion degradation, and adhesion degradation, respectively; The data is dimensionless by means of the mean, as expressed in equation (7), where, ; (7)； Calculate the correlation coefficient according to equation (8). ,in, The resolution coefficient, Then, the correlation degree is calculated according to equation (9). ; (8)； (9)； The influence weights of each degradation mechanism are calculated according to equation (10). , (10)。 4. A system for assessing the remaining service life of liners in coal conveying equipment, characterized in that, For performing the method for assessing the remaining service life of linings of transshipment equipment in the coal conveying field as described in any one of claims 1 to 3, the system for assessing the remaining service life of linings of transshipment equipment in the coal conveying field comprises: The data acquisition module is used to collect key parameters during bulk coal transportation, including the content of large material, the impact load under the preset transfer drop, the moisture content of coal, the concentration of heavy metal ions, and the amount of coal slag adhering; it is also used to collect key parameters during belt conveyor operation, including the belt conveyor's drum diameter, drum speed, belt speed, and the amount of bulk coal passing through per second. The experimental analysis module is used to conduct multi-parameter, multi-level orthogonal experiments and grey relational analysis to quantify the influence weights of different degradation mechanisms on the liner life. The model building module is used to build degradation models corresponding to different degradation mechanisms to obtain the degradation amount under different degradation mechanisms; it is also used to combine the degradation amount under different degradation mechanisms with the influence weights to build a multi-factor coupled degradation model of the liner to obtain the current total degradation of the liner. The remaining life prediction module is used to calculate the remaining allowable cumulative coal throughput based on the maximum allowable degradation thickness of the liner, the current total degradation of the liner, and the current cumulative coal throughput, and thus characterize the remaining life of the liner.

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