Blower quality inspection method and system based on multi-dimensional factors

Abstract

The invention relates to the field of air blower quality control, and particularly discloses an air blower quality inspection method and system based on multi-dimensional factors, and the method comprises the steps: carrying out the continuous dynamic load scanning from no-load to full-load on an air blower, and synchronously collecting the instantaneous data of flow, pressure and temperature at high frequency; generating a dynamic flow-pressure relation atlas and a dynamic flow-temperature relation atlas based on the dynamic flow-pressure relation atlas and the dynamic flow-temperature relation atlas, and extracting a unit flow temperature rise rate; the dynamic atlas is compared with a standard tolerance band, and cross validation of the unit flow temperature rise rate and a standard threshold value is combined, so that judgment of the comprehensive performance of the air blower and intelligent defect diagnosis are achieved; the system comprises a central control module, a load regulation and control module, a multi-parameter synchronous acquisition module, a performance atlas construction module and an intelligent diagnosis module. According to the method, isolated single-point testing is improved into coherent systematic evaluation, the quality inspection process is spanned from passive measurement to active diagnosis, and the detection efficiency and depth are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of blower quality control technology, and in particular to a blower quality inspection method and system based on multi-dimensional factors. Background Technology

[0002] As a core fluid transport device, the flow rate, outlet pressure, and operating temperature of a blower are key performance parameters that directly determine its efficiency, reliability, and lifespan. Currently, although various technical solutions have been adopted for blower performance quality inspection in the industry, they still have significant limitations and cannot meet the demands of modern, efficient, and precise quality inspection.

[0003] 1. Efficiency bottlenecks and lack of dynamic data in integrated testing equipment Existing integrated testing devices, such as the solution described in patent publication number CN202211553952.7, integrate the measurement functions of multiple sensors such as flow rate, temperature, and pressure, enabling the reading of multiple parameters after a single installation and reducing the hassle of repetitive operations to some extent. However, such devices mostly still measure data at several discrete, stable load points during testing. This method has inherent drawbacks: First, it requires waiting for the operating conditions to stabilize at each steady-state point, resulting in a relatively long overall testing cycle that restricts the pace of the production line; more importantly, measurements at discrete points cannot capture the dynamic performance response of the blower during continuous load changes, potentially masking performance defects that only manifest under specific dynamic conditions, such as slight surge or sudden efficiency drops.

[0004] 2. Limitations and diagnostic inadequacies of traditional gas temperature rise methods As described in patents CN106153128A and CN106840282A, some methods for measuring blower flow rate and efficiency based on the principle of gas temperature rise have emerged. These methods measure the temperature difference between the blower's inlet and outlet and the input power, then use the energy conservation equation to inversely deduce the flow rate. While their accuracy is acceptable under certain high-pressure conditions, their calculation models rely on empirical estimations of various factors such as motor efficiency and heat dissipation losses, introducing significant uncertainties. Measurement errors are amplified, especially under low loads or when heat dissipation conditions change. Furthermore, this method only treats temperature as an intermediate variable for calculating flow rate, failing to deeply explore it as an independent core performance indicator for diagnosing the blower's mechanical health (such as bearing friction or poor cooling), resulting in a limited diagnostic dimension and an inability to pinpoint the root cause of faults.

[0005] 3. Adaptability costs and operational barriers of complex testing systems Some more complex systems, such as the intelligent testing system for Roots blowers published in CN107035712A, employ up to ten test pipelines of different diameters to cover a wider flow range. While this design aims to improve the comprehensiveness of the test, it results in an exceptionally complex system structure, a large footprint, and high hardware costs. In actual production, frequent pipeline switching also affects testing efficiency. On the other hand, the magnetic levitation blower testing system described in patent CN111734670B, although achieving automatic testing of performance curves, requires a high level of expertise in debugging and operation, necessitating specialized personnel and institutions, which is time-consuming and labor-intensive, making it difficult to rapidly popularize and apply on production lines or in the field.

[0006] 4. The one-sidedness of testing methods for specific scenarios In addition, there are some testing schemes tailored to specific application scenarios. For example, the airflow testing method for vehicle blowers (CN112444410A) focuses on testing in a real passenger compartment environment, while the back pressure testing device (CN202323191905.5) uses mechanical counterweights to simulate load. These methods are highly dependent on specific application conditions, have poor universality, cannot form a unified standard for blower performance evaluation, and often can only evaluate a single parameter (such as airflow or back pressure), making it difficult to make a comprehensive judgment on the overall performance of the blower.

[0007] In summary, existing blower performance quality inspection technologies suffer from the following common problems: the testing process is mostly discrete steady-state measurement, lacking efficient dynamic scanning; parameter evaluation is isolated, failing to fully utilize the inherent dynamic correlations between key parameters such as flow rate, pressure, and temperature for comprehensive diagnosis; and the systems are either overly complex and expensive or have limited diagnostic dimensions. Therefore, there is an urgent need in this field for a novel quality inspection method and system capable of rapid dynamic testing and intelligent diagnosis of multiple parameters. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a blower quality inspection method and system based on multi-dimensional factors.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: The blower quality inspection method based on multidimensional factors includes the following specific steps: S1: Set up a test system including the blower under test, adjustable load device, flow sensor, pressure sensor, temperature sensor and central controller; before the test begins, collect the initial no-load flow rate Q0 and initial casing temperature T0 of the blower under no-load conditions. S2: Dynamic load scanning and synchronous data acquisition control the adjustable load device to continuously and uniformly change from no-load state to full-load state; the central controller synchronously acquires instantaneous flow rate value Q(t), instantaneous pressure value P(t) and instantaneous temperature value T(t) at a high sampling rate to form a dynamic data sequence; S3: Based on dynamic data sequences, feature extraction is performed to extract the temperature rise rate per unit flow as a feature parameter and generate a performance map; S4: Cross-validation and pass / fail determination based on performance graphs; S5: Output the comprehensive performance quality inspection results of the blower.

[0010] Furthermore, in step S1, a piezoresistive pressure transmitter is installed on the same side of the outlet pipe as the flow sensor, perpendicularly connected to the pipe axis to reduce airflow impact; a damper is provided to reduce acquisition errors caused by pressure fluctuations; PT100 platinum resistance temperature sensors are installed in the blower bearing chamber, exhaust port casing, and inlet pipe respectively; the sensor probe must be in close contact with the measured part, and an external insulation layer is wrapped to avoid interference from ambient temperature; the initial no-load flow rate Q0 and the initial casing temperature T0 are synchronously acquired through instructions from the central controller; Q0 is averaged through continuous sampling to reduce the impact of instantaneous fluctuations, and T0 is the average value of the bearing chamber and casing temperatures, which serves as the reference for subsequent temperature compensation.

[0011] Furthermore, in step S2, the load scanning cycle is set according to the blower model to ensure that the load changes linearly from no load to full load, avoiding sudden load changes that could cause equipment surge. Data timestamps are synchronized according to the sampling rate and sampling interval of the central controller data acquisition card. The central controller sends control commands to the adjustable load device, and the electric regulating valve adjusts from fully open to fully closed at a preset rate to achieve continuous and uniform load change. The valve opening feedback signal is monitored in real time, and the control command is immediately corrected if it deviates from the preset curve. During the load scanning process, the central controller synchronously collects instantaneous flow rate Q(t), instantaneous pressure P(t), and instantaneous temperature T(t) at a set sampling rate. The collected data is transmitted to the industrial control computer in real time and stored in CSV format.

[0012] Further, S3 specifically includes: compensating all instantaneous temperature values ​​T(t) based on the initial casing temperature T0 collected in S1, calculating the net temperature rise ΔT(t) = T(t) - T0, and eliminating the influence of ambient temperature on temperature parameters; the compensated data is used for graph generation; outlier data points, including sudden drops in flow rate and pressure jumps, are removed using the 3σ criterion; linear interpolation is performed on the removed data to complete it; then, a scatter plot is drawn in the industrial control computer testing software with flow rate Q(t) as the horizontal axis and pressure P(t) as the vertical axis, and the data points are connected using smooth curve fitting; the graph is labeled with the pre-stored standard pressure-flow rate curve and acceptable tolerance band, with flow rate Q(t) as the criterion. Plot the fitted curve on the horizontal axis; label the flow range as the effective range for calculating the temperature rise rate per unit flow to avoid interference from edge operating conditions; finally, extract all data points within the flow range from the dynamic flow-temperature relationship graph to determine the data coverage of the blower's main operating range, and use linear regression to fit the selected data points, calculate the slope of the fitted line, and the fitted line is the temperature rise rate per unit flow.

[0013] Furthermore, the linear regression is used to fit the filtered data points, including dividing the core flow range into three sub-ranges: low, medium, and high load, corresponding to the low-power, normal, and near-full-load operating conditions of the equipment, respectively. Then, stability screening is performed according to the dynamic load scanning requirements, followed by piecewise linear calculation to determine the start and end points. The sub-range data is sorted by flow rate and the basic slope is calculated and the mean point is corrected. The mean point is obtained using the average flow rate and net temperature rise of the sub-range. The slope is calculated in two segments, and if the difference meets the implicit error requirement, the average is taken as the corrected slope. Finally, based on a comprehensive judgment, the final unit flow rate temperature rise rate is obtained through weighting.

[0014] Further, step S4 specifically includes: traversing all data points on the dynamic flow-pressure relationship graph to determine whether the pressure value of each data point falls within the standard tolerance zone of the corresponding flow point; assisting in analyzing the trend of the graph fitting curve; if the curve shows an overall downward trend, even if the data point is within the tolerance zone, it is marked as needing verification to check for pipeline leaks or sensor installation deviations; retrieving the standard threshold for the unit flow temperature rise rate of the corresponding blower model from the standard database pre-stored in the central controller; comparing the extracted ΔT / ΔQ with the standard threshold; if ΔT / ΔQ is less than or equal to the standard threshold, the temperature rise rate is deemed qualified; if ΔT / ΔQ is greater than the standard threshold, the temperature rise rate is deemed unqualified; simultaneously, based on the threshold correction mechanism, if the deviation between the test ambient temperature and the standard ambient temperature is greater than the set threshold, the standard threshold is corrected to ensure accurate judgment results; only when the dynamic flow-pressure relationship graph meets the requirements and the unit flow temperature rise rate is qualified is the blower's overall performance deemed qualified; if any condition is not met, it is deemed unqualified, and the defect diagnosis process is triggered.

[0015] Further, step S5 specifically includes: defect diagnosis, including aerodynamic performance problems, mechanical performance problems, and overall performance problems. Aerodynamic performance problems include: if the dynamic flow-pressure relationship graph does not meet requirements, but the temperature rise rate per unit flow is qualified, the diagnosis is aerodynamic performance degradation or leakage; causes include impeller wear, poor sealing of inlet and outlet pipes, or valve jamming. Mechanical performance problems include: if the dynamic flow-pressure relationship graph meets requirements, but the temperature rise rate per unit flow is unqualified, the diagnosis is increased mechanical friction or poor cooling; causes include insufficient bearing lubrication, bearing wear, or cooling fan failure, requiring inspection of the mechanical transmission and cooling system. Overall performance problems include: if both do not meet requirements, the diagnosis is severe overall performance degradation; causes include motor failure, severe impeller imbalance, or internal fouling, requiring complete disassembly and repair.

[0016] Furthermore, in S5, if the result is deemed unqualified, a defect type diagnosis is performed: if the dynamic flow-pressure relationship graph is below the tolerance band but the temperature rise rate per unit flow is normal, it is diagnosed as aerodynamic performance degradation or leakage; if the dynamic flow-pressure relationship graph is normal but the temperature rise rate per unit flow exceeds the standard, it is diagnosed as increased mechanical friction or poor cooling; if the dynamic flow-pressure relationship graph is below the tolerance band and the temperature rise rate per unit flow exceeds the standard, it is diagnosed as severe deterioration of overall performance.

[0017] The second invention proposes a blower quality inspection method system based on multi-dimensional factors, used to implement a blower quality inspection method based on multi-dimensional factors, comprising: Central control module: Used to coordinate and control the start-up, shutdown, and workflow of each module; Load control module: Used to receive instructions from the central control module and apply a dynamic load that changes continuously and uniformly from no load to full load to the blower under test; Multi-parameter synchronous acquisition module: used to synchronously acquire the instantaneous flow rate, instantaneous pressure signal and instantaneous temperature signal of the blower under test at a high sampling rate during dynamic load scanning, and then convert the analog signals into digital signals before uploading; Performance graph construction module: used to receive digital signals and generate dynamic flow-pressure relationship graphs; generate dynamic flow-temperature relationship graphs; extract the unit flow temperature rise rate ΔT / ΔQ from the dynamic flow-temperature relationship graphs as characteristic parameters; Intelligent diagnostic module: It is used to compare the dynamic flow-pressure relationship spectrum with the standard tolerance zone and the unit flow temperature rise rate with the standard threshold. Based on the results of the dual comparison, it outputs a comprehensive performance judgment conclusion and defect type diagnosis prompt.

[0018] Furthermore, the multi-parameter synchronous acquisition module includes: Flow sensing unit: A vortex flow meter or a thermal mass flow meter is used and installed in the blower outlet pipe; Pressure sensing unit: A piezoresistive pressure transmitter is used and installed in the blower outlet pipe; Temperature sensing unit: PT100 platinum resistance temperature sensor is used and installed in the blower bearing housing or exhaust port housing; Signal conditioning and analog-to-digital conversion unit: used to filter, amplify and convert the analog signals from the above sensors into digital signals.

[0019] Furthermore, it also includes a human-computer interaction and report generation module, which is connected to the central control module and the intelligent diagnostic module. It is used to receive operator instructions, display the test process, dynamic graphs, judgment results and diagnostic prompts in real time, and automatically generate standardized quality inspection reports.

[0020] The beneficial effects of this invention are as follows: 1. Significant improvement in quality inspection efficiency: By replacing the traditional steady-state multi-point test with dynamic load scanning, the discrete testing process that originally took tens of minutes is compressed into a continuous scan of tens of seconds, which doubles the quality inspection efficiency and greatly adapts to the fast-paced needs of automated production lines.

[0021] 2. Breakthrough in assessment depth and accuracy: By constructing a dynamic performance spectrum, the assessment object is upgraded from limited discrete data points to a complete continuous characteristic curve, which can comprehensively and without omission reflect the performance of the blower throughout the entire working range; by introducing the derivative parameter of unit flow rate temperature rise rate, temperature is transformed from an isolated safety indicator into a core performance indicator for measuring energy conversion efficiency, which can keenly identify hidden faults caused by increased mechanical friction, improper bearing preload, poor cooling, etc.

[0022] 3. Introduction of intelligent diagnostic capabilities: A judgment logic based on cross-validation of dynamic pressure spectrum and rate was created. When the dynamic pressure spectrum is abnormal but the temperature rise rate is normal, it points to aerodynamic performance problems (such as leakage); when the pressure spectrum is normal but the temperature rise rate exceeds the standard, it points to mechanical friction problems; when both are abnormal, it is judged as comprehensive performance degradation. This upgrades the quality inspection results from simple pass / fail to defect type diagnostic reports with clear guidance significance, directly serving production process improvement and maintenance decisions. Attached Figure Description

[0023] Figure 1 This is a flowchart of the blower quality inspection method based on multi-dimensional factors proposed in this invention; Figure 2 This is a block diagram of the blower quality inspection method system based on multi-dimensional factors proposed in this invention.

[0024] Figure 3 This is a diagram of the real-time monitoring interface of the McGsPro simulator proposed in this invention.

[0025] Figure 4 This is a diagram of the integrated interface for performance parameters and control of the McGsPro simulator proposed in this invention.

[0026] Figure 5 This is a diagram of the interface for refined monitoring of key operating parameters of the blower proposed in this invention.

[0027] Figure 6 This is a time trend analysis chart of the multi-parameter blower proposed in this invention. Detailed Implementation

[0028] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0029] Example 1 Please see the appendix Figure 1 The blower quality inspection method based on multi-dimensional factors includes the following specific steps: S1: System Construction and Benchmark Calibration: Build a test system including the blower under test, adjustable load device, flow sensor, pressure sensor, temperature sensor, and central controller, specifically including: Setting up a blower testing system requires selecting and arranging hardware equipment, installing sensors, and configuring a central controller. The hardware includes a blower, an adjustable load device (such as an electric regulating valve or a variable frequency blower), flow sensors, pressure sensors, temperature sensors, and a central controller. Sensors are installed in the blower's outlet pipe and bearing housing or exhaust port casing. The central controller coordinates all equipment and has high-speed data acquisition, processing, communication, storage, and output functions. The system setup steps are as follows: connect the blower and the adjustable load device, install the sensors, configure and start the central controller for benchmark calibration, control the adjustable load device to perform dynamic load scanning, synchronously collect data and process it to generate dynamic graphs, and finally determine whether the blower's performance is qualified.

[0030] Before the test begins, the initial no-load flow rate Q0 and initial casing temperature T0 of the blower are collected under no-load conditions, specifically including: Set the adjustable load device to the fully open position to put the blower in an unloaded state; Start the blower and let it run under no-load for about 2 minutes to ensure stable operation; During stable operation, the outlet flow of the blower is monitored in real time by a flow sensor, and the instantaneous flow value Q(t) is recorded. To improve the accuracy of the data, a short time window (e.g., 10 seconds) can be selected, and Q(t) can be continuously sampled within this window. Then, the average value of these sampled values ​​is calculated as the initial no-load flow rate Q0.

[0031] Meanwhile, the temperature T(t) of the blower casing is measured using a temperature sensor. A short time window is selected for sampling, and the average value is calculated to obtain the initial casing temperature T0.

[0032] The initial casing temperature T0 is used to compensate for the subsequent instantaneous temperature value T(t), and the net temperature rise ΔT(t) = T(t) - T0 is calculated. The net temperature rise ΔT(t) is used when generating the dynamic flow-temperature relationship graph.

[0033] S2: Dynamic load scanning and synchronous data acquisition: Control the adjustable load device to continuously and uniformly scan and change from no-load state to full-load state; The adjustable load device is an electric regulating valve or a variable frequency fan. Continuous and uniform scanning change means that the load completes a linear change from no load to full load within 10 to 30 seconds. The adjustable load device can be controlled to continuously and uniformly scan from no load to full load. The following method can be used: First, define the load ratio r(t) as being between [0,1] at time t, where r(0)=0 (no load), r(T)=1 (full load), and T is the total scanning time.

[0034] Assuming uniform speed change, then r(t) = t / T; generate a control signal u(t) = r(t) = t / T, the range of which is [0,1], corresponding to the load device from no load to full load. Considering that the actual load device may have a nonlinear response, a proportional-integral-derivative (PID) controller can be introduced for closed-loop control to adjust the control signal in real time, ensuring that the load change meets the expected uniform speed requirement.

[0035] At the same time, set an appropriate sampling rate (such as 100Hz), measure the actual load value at each sampling interval, calculate the error, and adjust the control signal to achieve precise uniform scanning changes.

[0036] During this process, the central controller synchronously acquires a series of instantaneous flow rate values ​​Q(t), instantaneous pressure values ​​P(t), and instantaneous temperature values ​​T(t) that change over time at a high sampling rate, forming a dynamic data sequence, specifically including: Define sampling frequency and interval: Set the sampling frequency (e.g., 100 Hz), calculate the sampling interval. (e.g., 0.01 seconds); Synchronous acquisition logic: Before each sampling begins, the central controller sends a synchronous acquisition signal to all sensors to ensure that the flow, pressure and temperature sensors acquire data at the same time. Sampling time planning: based on sampling frequency and scan period (e.g., 20 seconds), calculate the total number of sampling points. (e.g., 2000 points), and generate a sampling time series. ; Buffer settings: To capture rapidly changing parameters, a short buffering period is inserted at each sampling time. Pause load changes to ensure accurate instantaneous value acquisition; Simulated data verification: By simulating instantaneous flow values Pressure value and temperature value The formula is used to generate simulation data and verify its conformity with the standard tolerance band, specifically including: Instantaneous flow rate Assuming ,in: This is the initial traffic. It is the flow attenuation coefficient; Instantaneous pressure value Assuming ,in: It is the initial pressure. It is the pressure rise coefficient; instantaneous temperature value Assuming ,in: It is the initial temperature. It is the coefficient of temperature rise.

[0037] This method ensures that the central controller can synchronously acquire flow, pressure, and temperature data at a high sampling rate during dynamic load scanning, forming an accurate dynamic data sequence.

[0038] S3: Performance Map Generation and Feature Extraction: Processing dynamic data sequences to extract the unit flow rate temperature rise rate as a feature parameter, specifically including: S31: Generate a dynamic flow-pressure relationship graph with flow rate as the horizontal axis, specifically including: With flow rate Q(t) as the horizontal axis and pressure P(t) as the vertical axis, the collected data points are connected sequentially to form a curve; ensure that the curve is smooth and reflects the dynamic relationship between flow rate and pressure.

[0039] Marking standard tolerance bands: Add standard tolerance bands to the graph, usually represented by shaded areas, with the range based on pre-defined acceptance standards; S32: Generate a dynamic flow-temperature relationship graph with flow rate as the horizontal axis, specifically including: Plot the selected data points on a coordinate system with flow rate Q(t) on the horizontal axis and net temperature rise ΔT(t) on the vertical axis to form a scatter plot. A smooth curve is then generated using linear regression or other fitting methods to reflect the dynamic relationship between flow rate and net temperature rise.

[0040] Mark key features: Mark the temperature rise rate per unit flow rate ΔT / ΔQ on the graph. This is a key feature parameter obtained by calculating the slope.

[0041] S33: Extract the unit flow rate temperature rise rate ΔT / ΔQ as a feature parameter from the dynamic flow-temperature relationship graph, where: the calculation method for the unit flow rate temperature rise rate ΔT / ΔQ is as follows: On the dynamic flow-temperature relationship graph, data points within the flow range [0.2Q rated, 0.8Q rated] are selected to exclude marginal anomalies. These data points are fitted by linear regression to calculate the slope k of the flow range. The calculated slope is defined as ΔT / ΔQ, and the specific formula is as follows: in: It is the first Traffic flow for each data point; It is the first Net temperature rise at each data point; and These are the average values ​​of flow rate and net temperature rise, respectively.

[0042] This method allows for the accurate extraction of characteristic parameters, which can be used for blower performance evaluation.

[0043] S4: Cross-validation and acceptance criteria: First, determine whether the overall spectrum falls within the tolerance band. Then, compare the unit flow rate temperature rise rate with the pre-stored standard rate threshold, specifically including: S41: Compare the dynamic flow-pressure relationship graph with the pre-stored standard pressure-flow curve and acceptable tolerance band to determine whether the graph falls within the tolerance band. For each data point on the graph, check whether its measured pressure value is within the tolerance range of the standard pressure value for the corresponding flow point. The standard pressure value is usually determined based on a large amount of qualified product data, and the tolerance range may be a certain percentage of the standard pressure value (e.g., ±6%). If the pressure values ​​of all data points are within the tolerance range, the graph is considered to fall within the tolerance band. Specifically: Input: Actual pressure point Standard upper and lower limit curves Iterate through each point and check if the condition is met. ; If all points meet the requirements, the graph is normal; otherwise, it is abnormal.

[0044] S42: Compare the extracted unit flow rate temperature rise rate ΔT / ΔQ with the pre-stored standard rate threshold, specifically: Input: Actual measurement Standard threshold ; like If so, the rate of temperature rise exceeds the standard.

[0045] The standard rate threshold is determined based on the following steps: Data collection: Dynamic flow-temperature relationship data under different operating conditions were collected from a large number of blowers of the same model that have been verified and qualified. This data covers the entire operating range of the blowers. Calculate the temperature rise rate per unit flow: For the data of each qualified blower, use the linear regression method to calculate the temperature rise rate per unit flow ΔT / ΔQ. This step is consistent with the calculation method described in S33, that is, select data points in the flow range [0.2Q rated, 0.8Q rated] and calculate the slope of the range through linear regression. Statistical analysis: Statistical analysis of the ΔT / ΔQ values ​​of all qualified blowers is performed to calculate the mean and standard deviation, which will help determine a reasonable threshold range; Determine the threshold: Based on the results of statistical analysis, combined with industry standards and actual application requirements, set a standard rate threshold that is slightly higher than the average value. This threshold should ensure that most qualified products are within its range, while effectively distinguishing blowers with poor performance or faults. Verification and Adjustment: In practical applications, newly manufactured blowers are tested and judged using established thresholds. Based on the judgment results, the thresholds may need to be fine-tuned to ensure their accuracy and practicality.

[0046] S5: Comprehensive diagnosis: The blower is deemed to have passed the comprehensive performance quality inspection only when both conditions are met simultaneously: the overall spectrum falls within the tolerance zone and the unit flow rate temperature rise rate is not greater than the standard rate threshold. If the result is deemed unqualified, a defect type diagnosis will be performed: If the dynamic flow-pressure graph is below the tolerance zone, but the temperature rise rate per unit flow is normal, it is diagnosed as aerodynamic performance degradation or leakage. If the dynamic flow-pressure relationship graph is normal, but the temperature rise rate per unit flow exceeds the standard, it is diagnosed as increased mechanical friction or poor cooling. If the dynamic flow-pressure relationship graph is below the tolerance zone and the temperature rise rate per unit flow exceeds the standard, it is diagnosed as a serious deterioration in overall performance.

[0047] Please see the appendix Figure 2 A multi-dimensional factor-based blower quality inspection method system is used to implement a multi-dimensional factor-based blower quality inspection method, including: Central control module: As the core of the system, it is used to coordinate and control the start-up, shutdown, and workflow of other modules; Load control module: Electrically connected to the central control module, used to receive instructions from the central control module and apply a dynamic load that changes continuously and uniformly from no load to full load to the blower under test; Multi-parameter synchronous acquisition module: Electrically connected to the central control module, it is used to synchronously acquire the instantaneous flow rate, instantaneous pressure signal, and instantaneous temperature signal of the blower under test at a high sampling rate during dynamic load scanning, and then convert the analog signals into digital signals before uploading; specifically including: Flow sensing unit: A vortex flow meter or a thermal mass flow meter is used and installed in the blower outlet pipe; Pressure sensing unit: A piezoresistive pressure transmitter is used and installed in the blower outlet pipe; Temperature sensing unit: PT100 platinum resistance temperature sensor is used and installed in the blower bearing housing or exhaust port housing; Signal conditioning and analog-to-digital conversion unit: used to filter, amplify and convert the analog signals from the above sensors into digital signals.

[0048] Performance graph construction module: It communicates with the central control module and the multi-parameter synchronous acquisition module to receive digital signals and generate dynamic flow-pressure relationship graphs; generate dynamic flow-temperature relationship graphs; and extract the unit flow temperature rise rate ΔT / ΔQ from the dynamic flow-temperature relationship graphs as characteristic parameters. Intelligent diagnostic module: It communicates with the central control module and the performance graph construction module. It has pre-stored standard flow-pressure curves, qualified tolerance bands and standard unit flow temperature rise rate thresholds. It is used to compare the dynamic flow-pressure relationship graph with the standard tolerance band and the unit flow temperature rise rate with the standard threshold. Based on the results of the dual comparison, it outputs a comprehensive performance judgment conclusion and defect type diagnosis prompts. The human-computer interaction and report generation module is connected to the central control module and the intelligent diagnostic module. It is used to receive operator instructions, display the test process, dynamic graphs, judgment results and diagnostic prompts in real time, and automatically generate standardized quality inspection reports.

[0049] Example 2 The blower quality inspection method based on multi-dimensional factors in this embodiment specifically includes the following steps: 1. Test Objects and System Configuration The blower under test is a centrifugal blower of model GB-150 with a rated flow rate of 150 m³ / h and a rated outlet pressure of 15 kPa.

[0050] Test system: Built according to the method of this invention, specifically including: Adjustable load device: DN50 electric regulating valve, control signal is 4~20mA.

[0051] Flow sensor: Vortex flow meter, range 0-200 m³ / h, accuracy ±1%.

[0052] Pressure sensor: Piezoresistive pressure transmitter, range 0-25 kPa, accuracy ±0.5%.

[0053] Temperature sensor: PT100 platinum resistance thermometer, installed in the non-drive end bearing housing of the blower, used to monitor the temperature rise of the core machinery.

[0054] Central controller: It adopts a combination of industrial PLC and industrial computer, with built-in data acquisition card and customized testing software, and is responsible for unified control and data analysis.

[0055] 2. Testing Process and Data Acquisition S1: System Construction and Benchmark Calibration Start the test system and control the electric regulating valve to the fully open position (no load).

[0056] Start the blower under test and wait for it to run under no-load for 2 minutes before the operating condition stabilizes.

[0057] The central controller records the initial no-load flow rate Q0 = 158 m³ / h and the initial casing temperature T0 = 32.5℃ (ambient temperature is 28℃).

[0058] S2: Dynamic Load Scanning and Synchronous Data Acquisition The central controller sends a command to the electric regulating valve, causing it to close gradually and continuously from the fully open state to the fully closed state (full load) within 20 seconds.

[0059] During this 20-second scan, the central controller simultaneously acquired instantaneous flow rate Q(t), instantaneous pressure P(t), and instantaneous temperature T(t) at a sampling rate of 100 Hz, obtaining approximately 2000 sets of synchronous data points.

[0060] 3. Performance map generation and feature extraction S3: Performance Map Generation and Feature Extraction S31: The software automatically processes the data, using flow rate as the horizontal axis and pressure as the vertical axis, to generate a measured dynamic flow rate-pressure relationship graph.

[0061] S32: Generate a measured dynamic flow-temperature relationship graph with flow rate as the horizontal axis and net temperature rise ΔT(t) = T(t) - T0 as the vertical axis.

[0062] S33: The software automatically selects data points within the flow-temperature relationship graph that fall within the range of 30% to 70% of the rated flow (i.e., 45 m³ / h to 105 m³ / h), and performs linear fitting using the least squares method. The slope of the fitted line is 0.085℃ / (m³ / h), which is defined as the temperature rise rate per unit flow of the blower, ΔT / ΔQ.

[0063] 4. Cross-validation and comprehensive diagnosis S4 & S5: Cross-validation and comprehensive diagnosis The testing software calls a pre-stored standard performance model, which is based on a large amount of data from qualified products of the same model, including: Standard flow-pressure curve and its acceptable tolerance band of ±6%.

[0064] Standard unit flow rate temperature rise rate threshold: 0.10℃ / (m³ / h).

[0065] Comparison results: The measured dynamic flow-pressure relationship graph (curve M1) falls entirely within the standard tolerance zone.

[0066] The calculated temperature rise rate per unit flow rate is 0.085 ℃ / (m³ / h) < standard threshold of 0.10 ℃ / (m³ / h).

[0067] Diagnostic Conclusion: Since both conditions are met—the graph is within the tolerance band and the temperature rise rate is below the threshold—the system determines that the GB-150 blower passes the comprehensive performance quality inspection. The software interface displays a green "pass" light and generates a test report containing all dynamic graphs, key data, and conclusions.

[0068] (Comparative Verification) Fault Simulation Test To verify the diagnostic capability of this method, one bearing end cover of the blower was slightly loosened to simulate a fault condition caused by increased mechanical friction. The above test steps were repeated.

[0069] Test results: The measured dynamic flow-pressure relationship graph still falls within the acceptable tolerance range, indicating that its aerodynamic performance has not been significantly affected.

[0070] However, the calculated temperature rise rate per unit flow rate increased significantly to 0.125 ℃ / (m³ / h), which exceeded the standard threshold.

[0071] Diagnostic conclusion: Based on preset logic (normal pressure spectrum but excessive temperature rise rate), the system determined the product to be unqualified and accurately output a diagnostic prompt: excessive mechanical friction or malfunctioning cooling system. This result matches the actual fault setting, demonstrating the superior effectiveness of the method of this invention in detecting latent defects.

[0072] Conclusion: This verification example fully demonstrates that the blower quality inspection method based on multi-dimensional factors can not only quickly and automatically complete the performance qualification judgment, but also accurately locate potential fault types through multi-parameter dynamic correlation analysis, achieving a qualitative leap from measurement to diagnosis.

[0073] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects: Significant improvement in quality inspection efficiency: By replacing traditional steady-state multi-point testing with dynamic load scanning, the discrete testing process that originally took tens of minutes is compressed into a continuous scan of tens of seconds, which doubles the quality inspection efficiency and greatly adapts to the fast-paced needs of automated production lines.

[0074] Breakthroughs in assessment depth and accuracy: By constructing dynamic performance maps, the assessment object is elevated from limited discrete data points to complete continuous characteristic curves, which can comprehensively and without omission reflect the performance of the blower throughout the entire working range; by introducing the derivative parameter of unit flow rate temperature rise rate, temperature is transformed from an isolated safety indicator into a core performance indicator for measuring energy conversion efficiency, which can keenly identify hidden faults caused by increased mechanical friction, improper bearing preload, poor cooling, etc.

[0075] The introduction of intelligent diagnostic capabilities: A judgment logic based on cross-validation of dynamic pressure graphs and rates has been created. When the dynamic pressure graph is abnormal but the temperature rise rate is normal, it points to aerodynamic performance problems (such as leakage); when the pressure graph is normal but the temperature rise rate exceeds the standard, it points to mechanical friction problems; when both are abnormal, it is judged as overall performance degradation. This upgrades the quality inspection results from simple pass / fail to defect type diagnostic reports with clear guidance significance, directly serving production process improvement and maintenance decisions.

[0076] Data-driven quality control: The entire testing process is automated and data-driven, and all dynamic graphs, characteristic parameters and diagnostic results are fully recorded, providing a valuable data foundation for product quality traceability, production consistency analysis and process parameter optimization, and helping to upgrade intelligent manufacturing.

[0077] like Figure 3 The image shown is a real-time monitoring interface of the McGsPro simulator, corresponding to the actual operating scenario of dynamic load scanning and synchronous data acquisition in step S2. It visually presents the dynamic changes of multi-dimensional operating parameters of the blower under high sampling rates. The interface displays the core parameters of the blower in real-time in numerical form, mainly divided into four categories: 1. Electrical parameters: Total active power (233.79kW), A / B / C phase current (369.9A, 346.1A, 361.2A), A / B / C line voltage (approximately 407.1V), inverter related parameters (current 434.5A, voltage 339.1V, power 221.9kW, temperature 37.9℃), reflecting the operating status of the motor and drive system, and assisting in judging the stability of load regulation.

[0078] 2. Temperature parameters: winding temperature (60.4℃), bearing temperature at end A (37.9℃), bearing temperature at end B (42.2℃), axial bearing temperature (52.5℃), bore inlet temperature (68.8℃), exhaust temperature (73.3℃), corresponding to the instantaneous temperature T(t) collected in S2, are the core data source for calculating the unit flow rate temperature rise rate ΔT / ΔQ, and are used to diagnose mechanical friction or cooling problems.

[0079] 3. Flow and pressure parameters: Pipe flow rate (200.12), inlet flow rate (217.90), inlet pressure (99.7 kPa), exhaust pressure (55.9 kPa), orifice plate differential pressure (4.535), and orifice plate pressure (4.622) directly correspond to the instantaneous flow rate Q(t) and instantaneous pressure P(t) collected by S2. These are the basic data for generating the dynamic flow-pressure relationship graph and are used to determine whether the aerodynamic performance meets the standards.

[0080] like Figure 4The diagram shown is an integrated interface diagram of the performance parameters and control of the McGsPro simulator, corresponding to steps S4 cross-validation and pass / fail judgment and S5 comprehensive diagnosis. It also includes operation control functions to realize closed-loop operation of test-judgment-control.

[0081] The interface is divided into two main areas: performance parameter display and operation control. The core content includes: 1. Core parameters for performance evaluation: outlet pressure (85.3 kPa), outlet temperature (123.2℃), inlet flow rate (10.7 m³ / min), motor temperature (106.0℃), and motor speed (87420 RPM). The outlet pressure and inlet flow rate are used to plot the dynamic flow-pressure relationship graph and compare it with the standard tolerance band. The outlet temperature and inlet flow rate are combined with the initial temperature T0 to calculate the net temperature rise ΔT, and then the unit flow rate temperature rise rate ΔT / ΔQ is extracted, which is the key basis for qualification evaluation.

[0082] 2. Operating Mode and Status Parameters: Operating mode (local), control mode (constant speed), frequency percentage (100%), nacelle pressure difference (-0.20 kPa), drive temperature (57.0℃), current operating time (0:41), cumulative operating time (0.69 H). The operating mode parameters ensure that the dynamic load scan meets the requirements of continuous uniform speed; the nacelle pressure difference helps to judge the equipment's sealing performance and is associated with the diagnosis of aerodynamic performance degradation or leakage; the cumulative operating time provides data support for batch quality inspection and traceability.

[0083] 3. Human-machine interaction and control functions: The system includes function entry points such as speed setting bar, start / stop / reset button, alarm record historical data system settings, etc., corresponding to the human-machine interaction and report generation module. It supports operators to issue test commands, retrieve historical test data, and view alarm information. At the same time, the test threshold can be adjusted through parameter settings to adapt to the quality inspection needs of different models of blowers.

[0084] like Figure 5 The image shows a detailed monitoring interface for key operating parameters of a blower. For parameters directly related to core quality control indicators such as flow rate, pressure, and temperature, the interface presents highly accurate core parameters in a simple layout, including: 1. Flow and pressure parameters: Flow rate (14.5 m³ / min), inlet pressure (100.61 kPa), and exhaust pressure (64.87 kPa). Among them, the flow rate (Q(t)) is the core data of the horizontal axis of the dynamic flow-pressure relationship graph and the dynamic flow-temperature relationship graph, which directly determines the graph resolution. The difference between inlet and exhaust pressure reflects the aerodynamic performance of the blower and is the key basis for judging the degradation of aerodynamic performance.

[0085] 2. Temperature parameters: intake air temperature (29℃), exhaust air temperature (84℃), motor temperature (102℃), inverter temperature (46℃). The intake air temperature is used to assist in calibrating the initial casing temperature T0 in S1, reducing the interference of ambient temperature on the calculation of net temperature rise ΔT. After obtaining the net temperature rise by subtracting the intake air temperature from the exhaust air temperature, the temperature rise rate per unit flow rate ΔT / ΔQ can be calculated by combining the flow rate. If the motor temperature exceeds the limit, it directly indicates increased mechanical friction.

[0086] 3. Electrical and safety parameters: motor voltage (179V), motor current (37.5A), motor power (17.26kW), surge monitoring value (0.4647), ECO mode switch. The electrical parameters indirectly reflect the load size and ensure the linear change of dynamic load scanning (such as the current increasing steadily with the flow rate). The surge monitoring value ensures that the equipment does not enter a dangerous operating condition during dynamic scanning and avoids data distortion caused by surge.

[0087] like Figure 6 The chart shown is a multi-parameter time trend analysis graph of the blower, corresponding to the extended presentation of the performance spectrum generation and feature extraction in step S3. The time-parameter curve visually reflects the continuous change pattern of core parameters during dynamic load scanning, aiding in the analysis of parameter correlation and stability. The horizontal axis of the chart represents time (10:00-14:00, a total of 4 hours), and the vertical axis represents parameter values ​​(0-100, normalized based on different parameter ranges), showing the changing trends of 5 types of core parameters: 1. Import flow trend: It first stabilizes and then rises slightly over time, reflecting the continuous scanning process of the adjustable load device in S2 from no load to full load. The continuity and stability of the flow directly determine the integrity of the dynamic spectrum and avoid spectrum breakpoints caused by sudden changes in flow.

[0088] 2. Export pressure trend: It is positively correlated with the import flow rate (increased flow rate → increased pressure), which conforms to the standard law of dynamic flow-pressure relationship graph. This trend can be used to preliminarily determine whether the aerodynamic performance of the blower is normal.

[0089] 3. Outlet temperature trend: It rises slowly with the increase of inlet flow rate. The slope of the rise corresponds to the temperature rise rate per unit flow rate ΔT / ΔQ. If the slope suddenly increases, it indicates increased mechanical friction or poor cooling.

[0090] 4. Trends in inlet temperature and pressure: Overall, the trend remains stable (small fluctuations in inlet temperature and basically constant inlet pressure), proving that the S1 benchmark calibration is effective. This eliminates the interference of ambient temperature and inlet pressure fluctuations on net temperature rise calculation and flow-pressure spectrum comparison, ensuring the accuracy of the quality inspection results.

[0091] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0092] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this specification. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for quality inspection of a blower based on multi-dimensional factors, characterized in that, The method comprises the following specific steps: S1: building a test system comprising a measured blower, an adjustable load device, a flow sensor, a pressure sensor, a temperature sensor, and a central controller; collecting initial no-load flow Q0 and initial casing temperature T0 of the blower in a no-load state before the test starts; S2: dynamic load scanning and synchronous data acquisition, controlling the adjustable load device to continuously and uniformly change from a no-load state to a full-load state; using the central controller to synchronously collect instantaneous flow value Q(t), instantaneous pressure value P(t), and instantaneous temperature value T(t) at a high sampling rate to form a dynamic data sequence; S3: feature extraction based on the dynamic data sequence, extracting unit flow temperature rise rate as a feature parameter, and generating a performance map; S4: cross-validation and eligibility determination based on the performance map; S5: outputting the comprehensive performance quality inspection result of the blower.

2. The multi-dimensional factor-based blower quality inspection method according to claim 1, wherein, In S1, a piezoresistive pressure transmitter is installed on the same side of the outlet pipeline as the flow sensor, and is connected perpendicularly to the pipeline axis to reduce airflow impact; a damper is provided to reduce acquisition errors caused by pressure fluctuations; a PT100 platinum resistance temperature sensor is installed in the blower bearing chamber, the exhaust port casing, and the inlet pipeline; the sensor probe needs to be in close contact with the measured part, and is wrapped with an external thermal insulation layer to avoid environmental temperature interference; the initial no-load flow Q0 and the initial casing temperature T0 are synchronously collected through the central controller instruction; Q0 is averaged by continuous sampling to reduce the influence of instantaneous fluctuations, and T0 is the average value of the bearing chamber and casing temperature, which is used as the subsequent temperature compensation reference.

3. The method of claim 2, wherein, In S2, the load scanning period is set according to the blower model to ensure that the load changes linearly from no-load to full-load, avoiding load mutation causing equipment surge; the data timestamp is synchronized according to the sampling rate and sampling interval of the central controller data acquisition card; the central controller sends control instructions to the adjustable load device; the electric regulating valve adjusts from full opening to full closing at a preset rate to realize continuous and uniform load change; the valve opening feedback signal is monitored in real time, and the control instruction is immediately corrected if it deviates from the preset curve; during the load scanning process, the central controller synchronously collects instantaneous flow value Q(t), instantaneous pressure value P(t), and instantaneous temperature value T(t) at a set sampling rate; the collected data is transmitted to the industrial computer in real time.

4. The method of claim 3, wherein, The S3 specifically includes: according to the initial casing temperature T0 collected in S1, compensating all instantaneous temperature values T(t), calculating net temperature rise values ΔT(t)=T(t)-T0 to eliminate the influence of ambient temperature on temperature parameters; the compensated data is used for atlas generation; and abnormal data points including flow sudden drop and pressure jump are removed; the removed data is linearly interpolated to complete; then a scatter plot is drawn in the industrial computer test software with flow Q(t) as the horizontal axis and pressure P(t) as the vertical axis, and the data points are connected by a smooth curve fitting; the standard pressure-flow curve and the qualified tolerance band are labeled in the atlas, and the fitting curve is drawn with flow Q(t) as the horizontal axis; the flow interval is labeled as the effective range for calculating the unit flow temperature rise rate to avoid edge condition data interference; finally, all data points in the flow interval are extracted from the dynamic flow-temperature relationship atlas to determine the data coverage of the main working interval of the blower, and linear regression is used to fit the screened data points to calculate the slope of the fitting straight line, and the fitting straight line is the unit flow temperature rise rate.

5. The multi-dimensional factor-based blower quality inspection method according to claim 4, wherein, The linear regression fitting of the screened data points includes interval division based on the core flow interval, including three sub-intervals of low, medium and high load, respectively corresponding to low power, normal and near full load operation conditions of the equipment; then stability screening is performed according to the dynamic load scanning requirements, and then segmented linear calculation is performed to determine the first and last points, wherein the sub-interval data is sorted by flow and the basic slope is calculated and the average point is corrected, the average point is obtained by using the sub-interval flow and the net temperature rise average, the slope is calculated in two segments, the difference meets the implicit error requirement, and the average is taken as the corrected slope, and finally the final unit flow temperature rise rate is obtained by weighting according to the comprehensive judgment.

6. The multi-dimensional factor-based blower quality inspection method according to claim 5, wherein, The step S4 specifically includes judging whether the pressure value of each data point falls within the standard tolerance band of the corresponding flow point by traversing all data points on the dynamic flow-pressure relationship atlas; The trend of the atlas fitting curve is analyzed, if the overall curve shows a downward trend, even if the data point is within the tolerance band, it is marked as to be reviewed to check whether there is a pipeline leakage or sensor installation deviation; the standard threshold value of the unit flow temperature rise rate of the corresponding type of blower is called from the standard database of the central controller, and the extracted ΔT / ΔQ is compared with the standard threshold value, if ΔT / ΔQ is less than or equal to the standard threshold value, it is determined that the temperature rise rate is qualified; if ΔT / ΔQ is greater than the standard threshold value, it is determined that the temperature rise rate is unqualified, and at the same time based on the threshold correction mechanism, if the test environment temperature deviates from the standard environment temperature by more than the set threshold value, the standard threshold value is corrected to ensure the accuracy of the determination result; Only when the dynamic flow-pressure relationship atlas meets the requirements and the unit flow temperature rise rate is qualified at the same time, the comprehensive performance of the blower is determined to be qualified; If any condition is not met, it is determined to be unqualified, and a defect diagnosis process is triggered.

7. The multi-dimensional factor-based blower quality inspection method according to claim 6, wherein, The step S5 specifically comprises: unqualified defect diagnosis, including pneumatic performance problem, mechanical performance problem and comprehensive performance problem, wherein the pneumatic performance problem includes: if the dynamic flow-pressure relationship atlas does not meet the requirements, but the unit flow temperature rise rate is qualified, diagnosing as pneumatic performance recession or leakage; the reasons include impeller wear, poor sealing of inlet and outlet pipelines or valve jam; the mechanical performance problem includes: if the dynamic flow-pressure relationship atlas meets the requirements, but the unit flow temperature rise rate is unqualified, diagnosing as mechanical friction increase or poor cooling; the reasons include insufficient bearing lubrication, bearing wear or cooling fan failure, needing to check the mechanical transmission and cooling system; the comprehensive performance problem includes: if both do not meet the requirements, diagnosing as serious deterioration of comprehensive performance; the reasons include motor failure, serious imbalance of impeller or internal fouling of the machine body, needing to overhaul in an overall manner.

8. The multi-dimensional factor-based blower quality inspection method according to claim 7, wherein, In the S5, if it is determined that it is unqualified, then the defect type diagnosis is performed: If the dynamic flow-pressure relationship atlas is lower than the tolerance band, but the unit flow temperature rise rate is normal, then diagnosing as pneumatic performance recession or leakage; if the dynamic flow-pressure relationship atlas is normal, but the unit flow temperature rise rate is over standard, then diagnosing as mechanical friction increase or poor cooling; if the dynamic flow-pressure relationship atlas is lower than the tolerance band and the unit flow temperature rise rate is over standard, then diagnosing as serious deterioration of comprehensive performance.

9. A method system for quality inspection of a blower based on multi-dimensional factors, characterized in that, The method for implementing the multi-dimensional factor-based blower quality inspection method of any one of claims 1-8 comprises: a central control module: for coordinating and controlling the start-stop and workflow of each module; a load regulation module: for receiving the instructions of the central control module and applying a dynamic load continuously and uniformly changing between no load and full load to the blower under test; a multi-parameter synchronous acquisition module: for synchronously acquiring the instantaneous flow signal, instantaneous pressure signal and instantaneous temperature signal of the blower under test at a high sampling rate during the dynamic load scanning process, and uploading the analog signals converted into digital signals; a performance atlas construction module: for receiving the digital signals and generating a dynamic flow-pressure relationship atlas; generating a dynamic flow-temperature relationship atlas; extracting the unit flow temperature rise rate ΔT / ΔQ as a characteristic parameter from the dynamic flow-temperature relationship atlas; an intelligent diagnosis module: for comparing the dynamic flow-pressure relationship atlas with the standard tolerance band, and comparing the unit flow temperature rise rate with the standard threshold, and outputting the comprehensive performance determination conclusion and defect type diagnosis prompt according to the double comparison results.

10. The multi-dimensional factor-based blower quality inspection method system according to claim 9, wherein, The multi-parameter synchronous acquisition module comprises: a flow sensing unit: adopting a vortex flowmeter or a thermal mass flowmeter, installed on the outlet pipeline of the blower; a pressure sensing unit: adopting a piezoresistive pressure transmitter, installed on the outlet pipeline of the blower; a temperature sensing unit: adopting a PT100 platinum resistance temperature sensor, installed on the bearing chamber or exhaust port shell of the blower; a signal conditioning and analog-digital conversion unit: for filtering and amplifying the analog signals of the above-mentioned sensors and converting them into digital signals; The man-machine interaction and report generation module is connected with the central control module and the intelligent diagnosis module, and is used for receiving operator instructions, displaying a test process, a dynamic atlas, a judgment result and a diagnosis prompt in real time, and automatically generating a standardized quality inspection report.

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