Crawler-type shot blasting machine key component state monitoring and collaborative maintenance method

Abstract

The invention belongs to the technical field of shot blasting machine equipment operation and maintenance and intelligent monitoring, and provides a state monitoring and collaborative maintenance method for key components of a crawler-type shot blasting machine. The method comprises the following steps: firstly, initializing a system, and configuring a key component basic parameter and shot blasting gap duration database; acquiring the real-time state of the component through multi-dimensional data acquisition; a collaborative early warning model is constructed and operated, and an early warning level is output according to the abrasion-tensioning-blocking logic; generating a standardized maintenance process; and the maintenance instruction is matched with the shot blasting gap duration, and maintenance time sequence adaptation is achieved. In addition, the method also has a model self-updating function. According to the invention, fragmentation detection limitation is broken through, multi-component cooperative early warning is realized, and cascading failure risk is reduced; a maintenance time sequence and a shot blasting gap are accurately adapted, and the comprehensive efficiency of equipment is improved; the maintenance process is standardized, and manual dependence is reduced; the method adapts to complex working conditions, continuously optimizes the early warning capability, and effectively solves many problems in the aspect of maintenance of key parts of the shot blasting machine in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of shot blasting machine equipment operation and maintenance and intelligent monitoring technology. Specifically, it relates to a method for status monitoring, fault early warning and maintenance process optimization of key components of a tracked shot blasting machine. It is particularly suitable for integrating multi-dimensional status data of core components such as directional sleeves, elevator belts and tracks, and achieving intelligent management scenarios with accurate early warning and maintenance sequence adaptation through collaborative analysis. Background Technology

[0002] As a key piece of equipment in metal surface treatment, the tracked shot blasting machine's directional sleeve (controlling the shot blasting angle), elevator belt (transporting shot), and track (transporting workpieces) are core components that ensure the machine's efficiency and stability. The operating conditions of these components directly affect the shot blasting quality (such as surface roughness uniformity), equipment lifespan (such as cascading failures caused by excessive wear), and production continuity (such as sudden shutdowns).

[0003] The existing technology for managing the state of the aforementioned components has the following drawbacks: 1. Fragmented detection, lack of coordinated early warning Wear of the directional bushing relies on periodic manual measurement (e.g., disassembly and inspection every 100 hours), while the tension of the hoist belt is assessed visually or through single-point tensile testing. Track blockage relies on current overload protection (which has a strong hysteresis). The status data of each component are isolated and cannot be analyzed in a correlated manner. For example, excessive wear of the directional bushing can cause the steel shot to deviate, which will aggravate local friction on the track. However, current technology cannot predict abnormal track loads based on directional bushing wear data, often resulting in the problem of "a single component warning but a chain reaction of failures has already occurred."

[0004] 2. Maintenance schedules are out of sync with production, resulting in high downtime costs. The maintenance process lacks a standardized adaptation mechanism: replacing the directional sleeve requires a 2-hour shutdown, adjusting the hoist belt tension requires a 30-minute shutdown, and cleaning the track requires a 1-hour shutdown. Traditional maintenance is often performed separately (for example, if the directional sleeve is found to be worn, the machine is stopped to replace it without taking into account the subsequent 4-hour production gap), resulting in an average of 3-5 unplanned shutdowns per day, and the overall equipment efficiency (OEE) is less than 60%.

[0005] 3. The early warning model is simplistic and lacks adaptability. Existing early warning systems are mostly based on fixed thresholds (such as alarms for directional sleeve wear > 5mm), without considering working condition variables (such as a 30% increase in directional sleeve wear rate when the hardness of the workpiece material increases), resulting in "false alarms (threshold redundancy at low loads) or missed alarms (threshold lag at high loads)", with an actual early warning accuracy of less than 50%.

[0006] Therefore, there is an urgent need for a standardized maintenance method that can integrate the status data of multiple components, establish a collaborative early warning model, and adapt to production gaps, in order to solve the problems of "delayed early warning, inefficient maintenance, and frequent cascading failures" in existing technologies. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for monitoring the status and collaborative maintenance of key components of a tracked shot blasting machine, solving the problems of fragmented detection of key components, lack of collaborative early warning, and poor adaptation between maintenance and shot blasting gaps in existing shot blasting machines.

[0008] A method for monitoring the condition and collaborative maintenance of key components of a tracked shot blasting machine, characterized by the following steps: (1) System initialization: Configure the basic parameters of the directional sleeve, the elevator belt and the track, including the initial wall thickness of the directional sleeve, the standard tension range of the elevator belt, the no-load running current threshold of the track, and preset the shot blasting gap duration database; (2) Multi-dimensional data acquisition: The wear of the directional sleeve is detected in real time by laser displacement sensor, the real-time tension of the elevator belt is detected by tension sensor, and the blockage degree and running stability of the track are detected by current sensor combined with image recognition. The acquisition frequency is synchronized with the working rhythm of shot blasting machine. (3) Construction and operation of collaborative early warning model: Based on the data collected in step (2), a collaborative early warning model of "wear-tension-blockage" is established. The model outputs the early warning level through the following logic: ① Calculate the correlation coefficient between the wear rate of the directional sleeve and the attenuation rate of the belt tension of the hoist. When the correlation coefficient is greater than 0.7, it is judged as "linkage wear risk". ② Based on the coupling relationship between the track blockage degree and the wear of the directional sleeve, when the blockage degree is greater than 30% and the wear of the directional sleeve is greater than 20% of the initial wall thickness, the "load abnormality warning" is triggered; ③ Based on the deviation between real-time data and basic parameters, the warning level is divided into minor (deviation 10-20%), moderate (deviation 20-40%), and emergency (deviation > 40%). (4) Standardized maintenance process generation: Based on the warning level in step (3), a preset standardized maintenance module is invoked, the module comprising: ① Targeted sleeve maintenance sub-process: Record the wear amount when there is a minor warning, mark it as needing replacement when there is a moderate warning, and generate an immediate replacement instruction when there is an emergency warning; ② Hoist belt maintenance sub-process: Automatically calculate the adjustment amount based on the tension deviation and output the stepper motor adjustment steps; ③ Track maintenance sub-process: When the blockage level is <30%, the automatic unblocking program is started; when the blockage level is ≥30%, the machine is marked to stop for cleaning. (5) Maintenance timing adaptation: Match the maintenance instructions in step (4) with the shot blasting gap duration database. When the maintenance required duration is less than or equal to the shot blasting gap duration, execute within the gap. When the maintenance required duration is greater than the shot blasting gap duration, split the maintenance steps and prioritize the execution of emergency items. The remaining steps are included in the next gap.

[0009] Preferably, in step (2): The wear detection points of the directional sleeve are three evenly distributed areas on its inner wall that are in contact with the steel shot, and the average value is taken as the real-time wear amount. The tension detection of the elevator belt adopts a dual-sensor arrangement, which is installed at the drive pulley and driven pulley of the belt respectively, and the difference between the two is taken as the tension uniformity index; The degree of track blockage is determined by the image recognition module, which identifies the occlusion rate of the track mesh. The result is calculated by combining the difference rate between the operating current and the no-load current. The formula is: Blockage degree = (0.6 × occlusion rate + 0.4 × current difference rate) × 100%.

[0010] Preferably, the collaborative early warning model in step (3) also includes working condition adaptive correction logic: when the hardness of the workpiece material is detected to be >250HB, the wear threshold of the directional sleeve is automatically reduced by 15%; when the workpiece load is >80% of the rated value, the warning threshold of the track blockage is automatically reduced by 20%.

[0011] Preferably, in the standardized maintenance module of step (4): The standardized steps for replacing the directional sleeve include: positioning marking → removing the fixing bolts → measuring and recording the old part → setting the preload of the new part (35-40 N·m) → trial run verification; The stepper motor for adjusting the belt tension of the hoist has an adjustment accuracy of 0.5mm / step, and a single adjustment should not exceed 5 steps. After adjustment, the tension should be kept stable for more than 30 seconds.

[0012] Preferably, the maintenance timing adaptation in step (5) also includes priority rules: when multiple maintenance instructions exist at the same time, they are sorted in the order of "emergency warning items > items affecting shot blasting quality > pure preventive maintenance items", where "items affecting shot blasting quality" include directional sleeve wear > 25% of initial wall thickness and elevator belt tension deviation > 30%.

[0013] Preferably, it also includes a model self-updating step: after each maintenance, the actual fault data is compared with the early warning results, and the correlation coefficient weight of the collaborative early warning model is adjusted to dynamically optimize the early warning accuracy, with each update not exceeding 10% of the initial value.

[0014] Preferably, the shot blasting interval time database in step (1) includes: pre-shift warm-up interval (5-8 minutes), batch change interval (15-20 minutes), and daily maintenance interval (30-45 minutes), and the actual duration distribution of each interval can be automatically updated through historical data statistics.

[0015] The beneficial effects of this invention are as follows: 1. Overcoming the limitations of fragmented detection, achieving multi-component collaborative early warning, and significantly reducing the risk of cascading failures. By integrating multi-dimensional status data (wear, tension, and blockage) from the directional sleeve, hoist belt, and track, a collaborative early warning model for "wear-tension-blockage" is constructed. This addresses the shortcomings of traditional technologies where data from individual components are isolated and cannot be correlated for analysis. The model calculates correlation coefficients (e.g., a linkage warning is triggered when the correlation between directional sleeve wear and belt tension reduction is >0.7) and coupling relationships (an abnormal load warning is issued when blockage >30% + directional sleeve wear >20% of the initial wall thickness). This increases the early warning accuracy from less than 50% in existing technologies to over 90%, effectively avoiding the problem of "a single component issuing a warning but a chain reaction of failures has already occurred." This reduces the overall machine chain reaction failure rate by 60%, minimizing production interruption losses due to sudden failures.

[0016] 2. Precise matching of maintenance timing with shot blasting intervals significantly improves overall equipment efficiency (OEE). Based on a pre-set database of shot blasting interval durations (5-8 minutes for pre-shift warm-up, 15-20 minutes for batch transition, etc.), maintenance instructions are intelligently matched with production intervals. Tasks requiring short-term maintenance (such as belt tension adjustment within 30 minutes) are completed during batch transition intervals, while tasks requiring long-term maintenance (such as directional sleeve replacement within 2 hours) are split and prioritized for urgent tasks, avoiding unplanned downtime caused by traditional "individual downtime maintenance." After implementation, the average number of unplanned downtimes per day has decreased from 3-5 times to less than 1 time, the downtime for a single maintenance session has been reduced by 40%, the overall equipment efficiency (OEE) has increased from below 60% to over 85%, and the annual increase in effective production time exceeds 1200 hours.

[0017] 3. Standardized maintenance processes reduce costs and improve efficiency, decrease reliance on manual labor, and ensure stable maintenance quality. The design incorporates standardized maintenance sub-processes for directional sleeves, elevator belts, and tracks (e.g., directional sleeve replacement includes steps such as "positioning marking → pre-tensioning force 35-40 N·m → trial run verification," with belt tension adjustment accuracy reaching 0.5 mm / step), replacing traditional maintenance methods that rely on manual experience. On one hand, novices can operate according to the process after just one hour of training, reducing labor costs by 50%. On the other hand, maintenance quality stability is significantly improved—after directional sleeve replacement, the deviation of the shot blasting angle is reduced from ±5° to ±1°, the recurrence cycle of blockages after track cleaning is extended from 24 hours to 72 hours, and the surface roughness uniformity compliance rate of shot-blasted workpieces increases from 80% to 98%, reducing rework costs caused by improper maintenance.

[0018] 4. Adaptive operating conditions and self-updating models enable continuous optimization of early warning capabilities to adapt to complex production scenarios. The model features adaptive correction logic based on operating conditions (e.g., reducing the wear threshold of the directional sleeve by 15% when the workpiece hardness is >250HB, and reducing the track blockage threshold by 20% when the load is >80% of the rated value), avoiding false alarms and missed alarms caused by traditional fixed thresholds, reducing the false alarm rate from 30% to below 5%. Simultaneously, it dynamically adjusts model weights through post-maintenance data comparison (matching analysis of actual faults and early warning results), ensuring continuous optimization of early warning capabilities as equipment operates. Furthermore, accurate early warnings and timely maintenance extend the lifespan of core components—the lifespan of the hoist belt is extended by 20% due to dynamic tension adjustment, the lifespan of the track is extended by 15% due to timely clearing of blockages, and the directional sleeve, by being replaced as needed, avoids excessive wear and waste, reducing annual component replacement costs by 30%. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is the overall control flowchart.

[0021] Figure 2 This is a logic diagram of the collaborative early warning model. Detailed Implementation

[0022] The technical solutions of 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The following detailed explanation of the technical solution of this invention is based on a practical scenario involving the processing of aluminum alloy die-cast parts (single batch loading capacity 120kg, workpiece hardness 180-220HB). This embodiment is based on a KiC-200 crawler shot blasting machine (equipped with a laser displacement sensor, tension sensor, current sensor, and Siemens S7-1200 PLC), focusing on the entire process of "data acquisition - collaborative early warning - standardized maintenance - timing adaptation" to ensure the reproducibility of the technical solution.

[0024] I. Basic Configuration Before Implementation (a) Setting basic parameters for key components The PLC pre-stores the reference parameters for the directional sleeve, elevator belt, and track to adapt to the processing conditions of aluminum alloy die-casting parts.

[0025] (II) Configuration of the shot blasting interval duration database Enter the production interval data for this shot blasting machine (based on historical operating statistics for the past 3 months): Pre-shift warm-up interval: fixed at 7 minutes (the time for the equipment to warm up to normal operating conditions after starting up each day); Batch transition interval: 15-20 minutes (the interval between the completion of the current batch of workpieces and the loading of the next batch of workpieces; in this embodiment, it is taken as 18 minutes). Daily maintenance interval: 40 minutes (the fixed interval for routine cleaning and inspection after the end of the day's shift).

[0026] II. Multi-dimensional Data Collection (Real-time Monitoring Phase) (a) Sensor deployment and data acquisition logic 1. Wear detection of directional sleeve Sensor: Laser displacement sensor (model KEYENCEIL-600), installed at the observation window of the shot blasting machine outer cover, focusing on three evenly distributed detection points (120° interval) on the inner wall of the directional sleeve, with a sampling frequency of 5Hz, and outputting a 0-10V analog signal (corresponding to a wall thickness of 0-30mm). Data processing: The average value of 3 detection points is taken every 10 seconds as the real-time wear amount. For example, the detection values ​​in the second hour are 13.8mm, 13.6mm, and 13.7mm, and the real-time wear amount is calculated to be 13.7mm (initial 15mm, cumulative wear 1.3mm).

[0027] 2. Hoist belt tension test Sensors: Tension sensors (model HBMU9C) are installed at the bearing seats of the drive wheel (upper part) and driven wheel (lower part) of the hoist, respectively, with a sampling frequency of 2Hz; Data processing: Real-time calculation of the tension difference between the two wheels (drive wheel tension - driven wheel tension). For example, in the second hour, the drive wheel tension is measured at 10.5kN and the driven wheel tension at 9.8kN, resulting in a tension difference of 0.7kN (meeting the uniformity requirement of ≤1kN). The real-time tension is 10.1kN (within the standard range of 8-12kN).

[0028] 3. Track blockage and operational status detection Sensor: A current sensor (model SCT013) is connected in series in the track drive motor circuit, and is simultaneously used with an industrial camera (model Hikvision MV-CA050-10GM) to capture images of the track mesh; Data processing: The grid occlusion rate is calculated by image recognition (12% occlusion rate in the second hour of this embodiment), combined with the current difference rate (real-time current 9.2A, no-load current 9A, difference rate 2.2%), and the occlusion rate is calculated to be 8.1% (no risk of occlusion) according to the formula "occlusion degree = (0.6×12%+0.4×2.2%)×100%".

[0029] III. Operation of the Collaborative Early Warning Model (Early Warning Judgment Phase) (I) Data Correlation Analysis and Early Warning Level Output Taking the real-time data from the 8th hour as an example, execute the collaborative early warning logic: 1. Basic Data Input Orientation sleeve: cumulative wear 2.8mm (wear rate 0.35mm / hour), projectile angle deviation 2°; Hoist belt: Real-time tension 8.5kN (decreases by 1.5kN from the initial 10kN, at a rate of 0.1875kN / hour), tension difference 0.9kN; Track: Blockage level 28% (blockage rate 40%, real-time current 10.5A, differential rate 16.7%), running smoothly without deviation.

[0030] 2. Cooperative logic computation Correlation coefficient calculation: The correlation coefficient between the wear rate of the directional sleeve (0.35 mm / h) and the belt tension decay rate (0.1875 kN / h) is 0.65 (<0.7, no risk of linked wear). Coupling relationship determination: Track blockage degree 28% (<30%), directional sleeve wear 2.8mm (<20% of initial wall thickness, i.e., 3mm), no abnormal load warning; Warning level determination: 2.8mm wear of the directional sleeve (within the 1.5-3mm range) → minor warning; 28% track blockage (within the 10-30% range) → minor warning; normal belt tension → no warning.

[0031] 3. Adaptive correction for operating conditions (simulating special operating conditions) When switching to process high-hardness cast iron parts (hardness 260HB) in the 10th hour, the PLC automatically triggers a working condition correction: The wear threshold for the directional sleeve has been reduced by 15% from "1.5-3mm / 3-6mm" to "1.275-2.55mm / 2.55-5.1mm"; The wear of the directional sleeve was detected in real time, and after correction, it was judged as a medium warning (originally judged as a slight warning), thus avoiding the warning lag under high hardness conditions.

[0032] IV. Execution of Standardized Maintenance Procedures (Maintenance Operation Phase) For the "Targeted Alternating Moderate Warning + Minor Track Warning" in the 10th hour, the standardized maintenance module is invoked: (a) Targeted early warning maintenance (marking for replacement + parameter recording) 1. Execution steps: Step 1: The PLC displays a warning message on the touchscreen: "Orientation sleeve is in a middle warning, marked as to be replaced after batch conversion", and simultaneously records the current wear amount of 3.1mm and the projectile angle deviation of 3°. Step 2: Generate a pre-replacement list (including the FS5.3 directional sleeve model and the preload force parameter of 35-40 N·m) and push it to the maintenance personnel's terminal.

[0033] Effect verification: After marking, it can still be shot blasted normally, the surface roughness Ra of the workpiece is ≤6.3μm (meets the requirements), and there is no shot blasting deviation caused by excessive use.

[0034] (ii) Minor track warning maintenance (automatic blockage clearing) 1. Execution steps: Step 1: The PLC controls the track drive motor to reverse (speed 2m / min, lower than forward rotation 5.1m / min), and at the same time, the compressed air at the bottom of the shot blasting chamber is turned on for blowing (pressure 0.4MPa). Step 2: After 3 minutes of clearing blockage, the industrial camera's recognition occlusion rate drops to 15%, the current difference rate drops to 8%, and the blockage degree is calculated as "(0.6×15%+0.4×8%)×100%=12.2%", which meets the requirement of <30%, so the clearing process automatically stops.

[0035] 2. Effect verification: After clearing the blockage, the track running current returned to 9.3A, with no jamming, and the next batch of workpieces was transported smoothly.

[0036] V. Maintenance timing adaptation (production gap matching) (a) Adaptation of gaps between directional sleeve replacement and batch conversion 1. Duration assessment: The standard procedure for replacing the directional sleeve takes 45 minutes (including 20 minutes for disassembly, 15 minutes for installation, and 10 minutes for trial operation). The current batch conversion interval is 18 minutes (<45 minutes), which cannot be completed in one go.

[0037] 2. Adaptation solution: Step 1: During the current batch conversion interval (18 minutes), prioritize the execution of "removing the old directional sleeve + cleaning the mounting surface" (15 minutes). The remaining "installing new parts + trial run" tasks are marked as "to be completed during the daily maintenance interval". Step 2: During the daily maintenance break (40 minutes), complete the installation of the new directional sleeve (preload force 38 N·m) and trial run (projection angle deviation 0.8°) according to the procedure. The total time is 35 minutes, and the remaining 5 minutes are used for gap inspection.

[0038] (ii) Priority adaptation for multiple maintenance tasks (simulated concurrency warning) If both "Medium warning for hoist belt (tension deviation 3kN)" and "Slight warning for track" are triggered simultaneously in the 12th hour: Priority ranking: Based on "items affecting shot blasting quality > purely preventive maintenance items", "belt tension adjustment (affecting steel shot delivery)" is ranked higher than "track unblocking". Gap matching: Belt tension adjustment takes 25 minutes. Select the batch conversion gap (18 minutes) + pre-shift warm-up gap (7 minutes) combination to complete the process. During the batch conversion gap, perform "stepper motor adjustment (accuracy 0.5mm / step, 4 steps in total, taking 12 minutes)". During the pre-shift warm-up gap, perform "tension stability monitoring (30 seconds, confirm tension 10.2kN, difference 0.8kN)" to avoid individual machine shutdowns.

[0039] VI. Model Self-Update and Implementation Effect Verification (a) Model self-updating After completing 10 maintenance sessions on day 30, the PLC automatically compares the "early warning results with the actual faults": The accuracy rate of the directional sleeve warning is 92% (with one false alarm due to a sudden increase in workpiece hardness that was not corrected in time). The "wear threshold reduction ratio when workpiece hardness > 250HB" has been revised from 15% to 18%. The track blockage warning accuracy rate is 95% (0 missed reports). The original threshold remains unchanged, and the optimization of the model correlation coefficient weight is controlled within 8% (<10%).

[0040] (II) Implementation Results The results of this embodiment after processing 100 batches of aluminum alloy die-cast parts (cumulative operation time of 450 hours) are as follows: Early warning and faults: The accuracy rate of collaborative early warning is 91%, and the number of chain faults of the whole machine (such as the wear of the directional sleeve leading to uneven track wear) has been reduced from 8 to 2. Equipment efficiency: Unplanned downtime decreased from 120 hours to 35 hours, and OEE improved from 58% to 86%; Maintenance and Costs: Core Component Lifespan – Hoist belt lifespan increased by 22%, track lifespan increased by 16%, annual component replacement costs reduced by 32%; manual maintenance costs decreased by 55%, and workpiece rework rate decreased from 12% to 2%.

[0041] The above embodiments can be adapted to other workpiece types such as cast iron and stainless steel parts. Only the "working condition adaptive threshold" needs to be adjusted in the PLC (e.g., the track blockage threshold is lowered by 20% when handling stainless steel parts). The control logic remains consistent, and the same maintenance effect can be achieved.

[0042] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for monitoring and collaborative maintenance of key components of a track-type shot blasting machine, characterized in that, The method comprises the following steps: (1) System initialization: configure the basic parameters of the directional sleeve, the elevator belt, and the track, including the initial wall thickness of the directional sleeve, the standard tension range of the elevator belt, and the no-load running current threshold of the track, and preset the shot clearance time length database; (2) Multi-dimensional data acquisition: the wear amount of the directional sleeve is detected in real time by a laser displacement sensor, the real-time tension of the elevator belt is detected by a tension sensor, and the blockage degree and running stability of the track are detected by a current sensor combined with image recognition, and the acquisition frequency is synchronized with the working rhythm of the shot blasting machine; (3) Construction and operation of the collaborative early warning model: based on the data collected in step (2), a "wear-tension-blockage" collaborative early warning model is established, and the model outputs the early warning level through the following logic: ① Calculate the correlation coefficient of the wear rate of the directional sleeve and the tension attenuation rate of the elevator belt. When the correlation coefficient is greater than 0.7, it is determined that there is a "linkage wear risk"; ② Combined with the coupling relationship between the blockage degree of the track and the wear amount of the directional sleeve, when the blockage degree is greater than 30% and the wear amount of the directional sleeve is greater than 20% of the initial wall thickness, the "load abnormality early warning" is triggered; ③ According to the deviation value of the real-time data and the basic parameters, the early warning level is divided into slight (deviation 10-20%), medium (deviation 20-40%), and urgent (deviation > 40%); (4) Generation of standardized maintenance process: according to the early warning level in step (3), the preset standardized maintenance module is called, which includes: ① Directional sleeve maintenance sub-process: record the wear amount when the early warning is slight, mark for replacement when the early warning is medium, and generate immediate replacement instructions when the early warning is urgent; ② Elevator belt maintenance sub-process: automatically calculate the adjustment amount according to the tension deviation, and output the step motor adjustment steps; ③ Track maintenance sub-process: when the blockage degree is less than 30%, start the automatic unblocking program, and when it is greater than or equal to 30%, mark for cleaning; (5) Maintenance timing adaptation: match the maintenance instructions in step (4) with the shot clearance time length database. When the required maintenance time is less than or equal to the shot clearance time, perform the maintenance within the clearance; when the required maintenance time is greater than the shot clearance time, split the maintenance steps and prioritize the urgent items, and the remaining steps are included in the next clearance.

2. The method of claim 1, wherein, In step (2): The wear amount detection points of the directional sleeve are three evenly distributed regions on the inner wall in contact with the steel shot, and the average value is taken as the real-time wear amount; The tension detection of the elevator belt adopts double sensor arrangement, which is installed on the driving wheel and the driven wheel of the belt respectively, and the difference between the two is taken as the tension uniformity index; The blockage degree of the track is identified by the image recognition module to identify the blockage rate of the track grid, and the difference rate of the running current and the no-load current is calculated, the formula is: blockage degree = (0.6 x blockage rate + 0.4 x current difference rate) x 100%.

3. The method of claim 1, wherein, The collaborative early warning model in step (3) also includes a working condition self-adaptive correction logic: when the hardness of the workpiece material is greater than 250HB, the wear amount threshold of the directional sleeve is automatically reduced by 15%; when the workpiece load is greater than 80% of the rated value, the blockage degree early warning threshold of the track is automatically reduced by 20%.

4. The method of claim 1, wherein, In the standardized maintenance module in step (4): The standardization steps of directional sleeve replacement include: positioning mark → disassembly of fixing bolts → old part measurement record → new part pre-tightening force setting (35-40 N·m) → trial operation verification; The adjustment precision of the stepping motor of the belt tensioning adjustment of the elevator is 0.5 mm / step, and the single adjustment does not exceed 5 steps, and the tensioning force needs to be kept stable for more than 30 seconds after adjustment.

5. The method of claim 1, wherein, The maintenance timing adaptation of step (5) also includes priority rules: when there are multiple maintenance instructions at the same time, sort them in the order of "emergency warning items > impact on shot blasting quality items > pure preventive maintenance items", wherein "impact on shot blasting quality items" include directional sleeve wear > 25% of the initial wall thickness, and elevator belt tensioning force deviation > 30%.

6. The method of claim 1, wherein, It also includes a model self-updating step: after completing 1 maintenance, compare the actual fault data with the warning results, correct the correlation coefficient weight of the collaborative warning model, and dynamically optimize the warning accuracy, with an update amplitude of no more than 10% of the initial value each time.

7. The method of claim 1, wherein, The shot blasting gap length database of step (1) includes: pre-shift warm-up gap (5-8 minutes), batch conversion gap (15-20 minutes), and daily maintenance gap (30-45 minutes), and the actual gap length distribution can be automatically updated through historical data statistics.

Images