Method for diagnosing deformation of casing of axial flow fan

Abstract

The invention relates to a method for diagnosing deformation of a casing of an axial flow fan. The method comprises the following steps: (1) acquiring multi-source data; (2) vibration signal processing and spectrum analysis; (3) sound signal feature extraction: carrying out frequency domain analysis on the auscultation signal, extracting sound pressure level and frequency distribution features, judging a through-flow non-uniform region in combination with a vibration spectrum, and positioning a high-risk position of case deformation; (4) detecting the structure state of the casing; and (5) comprehensive diagnosis and fault positioning: if the following conditions are met at the same time, determining that the casing is deformed. The method has the advantages that (1) multi-dimensional fusion diagnosis: vibration, acoustics and structure detection data are combined, and the diagnosis accuracy is improved; and (2) quantitative analysis: realizing deformation degree grading and the like through quantitative indexes such as a frequency spectrum proportion and gap deviation.

Description

Technical Field

[0001] This invention relates to a diagnostic method for the deformation of the casing of an axial flow fan, belonging to the technical field of fan equipment. Background Technology

[0002] Axial flow fans are widely used in industrial fields such as thermal power and ventilation, and the casing is a key component. During long-term operation, the casing is prone to deformation due to factors such as high temperature, high pressure, and vibration, leading to problems such as abnormal vibration, abnormal noise, and decreased efficiency. Traditional diagnostic methods often rely on single vibration analysis or manual inspection, which has the following shortcomings:

[0003] (1) A single vibration signal is difficult to distinguish between casing deformation and other faults such as rotor imbalance;

[0004] (2) Manual auscultation relies on experience and is highly subjective;

[0005] (3) Lack of systematic testing of the casing structure condition.

[0006] While some patents in the existing technology propose fault diagnosis based on vibration spectrum, they do not incorporate the structural characteristics of the casing; although casing deformation is mentioned, a systematic diagnostic process has not been established. Therefore, there is an urgent need for a multi-dimensional, high-precision method for diagnosing casing deformation. Summary of the Invention

[0007] The purpose of this invention is to provide a diagnostic method for axial flow fan casing deformation. By integrating vibration signal analysis, spectral feature identification, casing structure detection, and auscultation technology, it achieves accurate and efficient diagnosis of casing deformation, timely detection of early minor deformation and internal hidden deformation, and provides a reliable basis for fan maintenance and fault early warning.

[0008] To overcome the shortcomings of existing technologies, this invention provides a method for diagnosing deformation of an axial flow fan casing. The technical solution of this invention is: a method for diagnosing deformation of an axial flow fan casing, comprising the following steps:

[0009] (1) Multi-source data acquisition: Vibration sensors, acceleration sensors and temperature sensors are simultaneously arranged at the air inlet, air outlet, bearing box and support point of the axial flow fan casing. Simultaneously, a stethoscope is used to collect the sound signals in the axial and circumferential directions of the casing and record the fan operating parameters, including speed, blade angle and air volume.

[0010] (2) Vibration signal processing and spectrum analysis: The collected vibration signals are filtered and denoised in the time domain to extract vibration velocity, displacement and acceleration features; a spectrum diagram is generated by fast Fourier transform to identify characteristic frequencies related to casing deformation, including the 1x speed frequency and its harmonics, the blade passing frequency and its harmonics, and the asynchronous vibration frequency.

[0011] (3) Sound signal feature extraction: Perform frequency domain analysis on the auscultation signal to extract the sound pressure level and frequency distribution features, and combine the vibration spectrum to determine the uneven flow area and locate the high-risk location of casing deformation;

[0012] (4) Casing structure condition inspection: Measure the uniformity of the blade tip clearance. If the deviation exceeds the ±0.5mm threshold, it is determined that the impeller and casing are not properly matched. Check the condition of the casing welds, reinforcing ribs and connecting bolts. Detect the casing joint surface clearance with a feeler gauge. If the local clearance exceeds 0.1mm, it is determined that the casing is deformed locally. Use a laser rangefinder or 3D scanner to obtain casing deformation data and construct a 3D deformation model of the casing.

[0013] (5) Comprehensive diagnosis and fault location: If the following conditions are met at the same time, it is determined to be a casing deformation: a. The proportion of asynchronous harmonics in the vibration spectrum exceeds 20%;

[0014] b. The intensity of the acoustic signal is significantly higher in a specific circumferential direction than in other areas;

[0015] c. Deviation in blade tip clearance or abnormal clearance at the casing mating surface;

[0016] By combining a 3D deformation model, the deformation area and degree are determined, and maintenance suggestions are generated.

[0017] (6) Real-time early warning and report output: The diagnostic results are compared with the preset thresholds to trigger graded early warnings and automatically generate a diagnostic report containing a diagram of the deformation location, a spectrum feature diagram and maintenance suggestions.

[0018] In step (1), the auscultation device is an acoustic sensor with a sampling frequency of not less than 20kHz.

[0019] In step (2), the asynchronous vibration frequency range of the spectrum analysis is 0.4 to 0.6 times the rotational speed frequency.

[0020] In step (4), the three-dimensional deformation model is constructed using a laser rangefinder with a measurement accuracy of not less than ±0.05 mm.

[0021] In step (6), the threshold for the graded early warning is dynamically adjusted according to the wind turbine model and stored in a cloud database.

[0022] In step (5), the maintenance recommendations include shutdown for inspection, local reinforcement or overall replacement, and are generated based on the degree of deformation of the three-dimensional deformation model.

[0023] In step (1), the temperature sensor is used to synchronously monitor the temperature distribution on the surface of the housing and to perform correlation analysis between the temperature data and vibration and acoustic signals in order to eliminate thermal deformation interference.

[0024] The advantages of this invention are:

[0025] (1) Multi-dimensional integrated diagnosis: Combining vibration, acoustic and structural test data to improve diagnostic accuracy;

[0026] (2) Quantitative analysis: The degree of deformation is graded by quantitative indicators such as spectrum ratio and gap deviation;

[0027] (3) Real-time performance and versatility: Applicable to different models of axial flow fans, supporting online monitoring and offline testing;

[0028] (4) Clear maintenance guidance: Accurately locate fault points based on three-dimensional deformation models to reduce downtime. Detailed Implementation

[0029] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0030] The present invention relates to a diagnostic method for deformation of axial flow fan casing, comprising the following steps: (1) Multi-source data acquisition: Vibration sensors, acceleration sensors and temperature sensors are simultaneously arranged at the air inlet, air outlet, bearing box and support point of the axial flow fan casing, and a stethoscope is used to collect the sound signals in the axial and circumferential directions of the casing, and the fan operating parameters, including speed, blade angle and air volume, are recorded.

[0031] (2) Vibration signal processing and spectrum analysis: The collected vibration signals are filtered and denoised in the time domain to extract vibration velocity, displacement and acceleration features; a spectrum diagram is generated by fast Fourier transform to identify characteristic frequencies related to casing deformation, including the 1x speed frequency and its harmonics, the blade passing frequency and its harmonics, and the asynchronous vibration frequency.

[0032] (3) Sound signal feature extraction: Perform frequency domain analysis on the auscultation signal to extract the sound pressure level and frequency distribution features, and combine the vibration spectrum to determine the uneven flow area and locate the high-risk location of casing deformation;

[0033] (4) Casing structure condition inspection: Measure the uniformity of the blade tip clearance. If the deviation exceeds the ±0.5mm threshold, it is determined that the impeller and casing are not properly matched. Check the condition of the casing welds, reinforcing ribs and connecting bolts. Detect the casing joint surface clearance with a feeler gauge. If the local clearance exceeds 0.1mm, it is determined that the casing is deformed locally. Use a laser rangefinder or 3D scanner to obtain casing deformation data and construct a 3D deformation model of the casing.

[0034] (5) Comprehensive diagnosis and fault location: If the following conditions are met at the same time, it is determined to be a casing deformation: a. The proportion of asynchronous harmonics in the vibration spectrum exceeds 20%;

[0035] b. The intensity of the acoustic signal is significantly higher in a specific circumferential direction than in other areas;

[0036] c. Deviation in blade tip clearance or abnormal clearance at the casing mating surface;

[0037] By combining a 3D deformation model, the deformation area and degree are determined, and maintenance suggestions are generated.

[0038] (6) Real-time early warning and report output: The diagnostic results are compared with the preset thresholds to trigger graded early warnings and automatically generate a diagnostic report containing a diagram of the deformation location, a spectrum feature diagram and maintenance suggestions.

[0039] Based on the above steps, the following advantages are achieved:

[0040] (1) Multi-dimensional integrated diagnosis: By integrating vibration signal analysis, spectral feature recognition, casing structure detection, and auscultation techniques, a comprehensive diagnosis of the axial flow fan casing is performed from different angles. Compared with traditional single diagnostic methods, it can capture various information about casing deformation more comprehensively, effectively avoiding misjudgments and omissions caused by relying on only a single signal, greatly improving the accuracy of diagnosis, achieving precise diagnosis of casing deformation, timely detection of early micro-deformations and internal hidden deformations, and providing a reliable basis for fan maintenance and fault early warning.

[0041] (2) Quantitative Analysis: Several quantitative indicators were introduced during the diagnostic process, such as the proportion of asynchronous harmonics in the vibration spectrum exceeding 20%, the blade tip clearance deviation threshold of ±0.5mm, and the casing joint surface clearance of 0.1mm. These specific quantitative indicators make the assessment of the casing deformation degree more accurate and objective, realize the classification of deformation degree, and help staff to understand the severity of casing deformation more clearly, thereby formulating more scientific and reasonable maintenance strategies.

[0042] (3) Real-time and universality: This diagnostic method is applicable to different models of axial flow fans, supports online monitoring and offline testing, and can acquire data in real time based on the actual operating status of the fan for analysis and diagnosis, so as to detect potential casing deformation problems in a timely manner. At the same time, this method can be used to diagnose casing deformation of axial flow fans of all sizes and in different application scenarios, which has wide applicability and good universality.

[0043] (4) Clear maintenance guidance: By combining the three-dimensional deformation model to accurately locate the fault point, the specific area and degree of casing deformation can be accurately determined, providing maintenance personnel with clear suggestions on maintenance parts and priorities, such as shutdown for inspection, local reinforcement or complete replacement. This helps reduce unnecessary equipment disassembly and inspection work, improve maintenance efficiency, shorten downtime, reduce maintenance costs, and ensure the normal operation and production efficiency of the fan.

[0044] In step (1), the auscultation device is an acoustic sensor with a sampling frequency of not less than 20kHz. The advantages of using a high sampling frequency acoustic sensor (not less than 20kHz) in step (1) are:

[0045] Capturing Rich Acoustic Information: High sampling frequencies enable more detailed capture of various acoustic signals generated by the casing during operation, including sound waves of different frequencies and intensities. This helps obtain more comprehensive and accurate acoustic characteristics, providing a more sufficient data foundation for subsequent acoustic signal feature extraction and analysis, thereby more accurately identifying high-risk locations of casing deformation. Enhancing Weak Signal Detection Capabilities: In the early stages of casing deformation, some weak, high-frequency abnormal acoustic signals may be generated. Sampling frequencies of 20kHz and above can effectively detect these weak signals, making diagnostic methods more sensitive to early, minor deformations, enabling more timely warnings and diagnoses, and preventing further deterioration of the fault.

[0046] Enhanced signal anti-interference capability: A higher sampling frequency provides a wider frequency range, enabling the sensor to better resist interference from external environmental noise when acquiring acoustic signals. Subsequent signal processing algorithms can more effectively extract valid signals related to casing deformation from complex acoustic environments, improving diagnostic reliability.

[0047] The advantage of setting the asynchronous vibration frequency range for spectral analysis in step (2) to 0.4 to 0.6 times the rotational speed frequency is:

[0048] Precise Fault Characterization: Focusing the asynchronous vibration frequency range to 0.4 to 0.6 times the rotational speed frequency allows for more accurate identification of characteristic frequencies closely related to casing deformation. Vibration signals within this frequency range are typically strongly correlated with abnormal conditions such as structural deformation and localized loosening of the casing. In-depth analysis of frequency components within this range allows for a more accurate determination of whether casing deformation exists and its approximate extent, effectively distinguishing casing deformation from other types of faults (such as rotor imbalance and bearing failure), thus improving diagnostic accuracy. Reduced Irrelevant Frequency Interference: Avoiding excessive analysis and processing of numerous irrelevant frequency components outside this range reduces data processing complexity and computational load, improving diagnostic efficiency and making the diagnostic method more efficient and practical. This enables the analysis of vibration signals and fault diagnosis to be completed in a shorter time, providing timely decision-making support for maintenance personnel.

[0049] Matching the vibration characteristics of the casing deformation: This frequency range is set based on the vibration characteristics of the axial flow fan casing during actual operation and the typical vibration response patterns generated when the casing deforms. It conforms to the vibration characteristic distribution during casing deformation, highlighting abnormal vibration components related to casing deformation in the spectrum analysis. This provides more targeted and valuable information for subsequent comprehensive diagnosis and fault location, helping to achieve early and accurate diagnosis of casing deformation and ensuring the safe and stable operation of the fan.

[0050] In step (4), the three-dimensional deformation model is constructed using a laser rangefinder with a measurement accuracy of not less than ±0.05mm. The advantages of using a laser rangefinder with a measurement accuracy of not less than ±0.05mm in step (4) are:

[0051] High-precision deformation detection: The high measurement accuracy of ±0.05mm can accurately acquire the deformation data of the casing, and even very small deformations can be accurately detected, providing reliable data support for subsequent deformation analysis and diagnosis.

[0052] Precise 3D model construction: High-precision measurement data helps to construct a more accurate and realistic 3D deformation model of the casing, which can clearly reflect the specific shape, location and degree of casing deformation, providing maintenance personnel with intuitive and detailed fault information.

[0053] Objective deformation assessment basis: Based on a precise three-dimensional deformation model, the degree of deformation of the casing can be quantitatively assessed, providing an objective and accurate basis for maintenance decisions and avoiding errors and uncertainties caused by human experience judgment.

[0054] In step (6), the threshold for the graded early warning is dynamically adjusted according to the wind turbine model and stored in a cloud database. The advantages of this step (6) are:

[0055] Personalized early warning settings: Different models of wind turbines have different operating characteristics and design parameters. Dynamically adjusting the threshold can be precisely set according to the characteristics of the wind turbine model, making the early warning more in line with the actual operating conditions and improving the accuracy and reliability of the early warning.

[0056] Intelligent early warning update: By storing and managing thresholds in a cloud database, the threshold settings can be continuously optimized and updated based on the wind turbine's operating data, enabling the early warning system to learn and upgrade itself intelligently to adapt to changes in the wind turbine's operating status.

[0057] Remote monitoring and management: Cloud storage enables relevant managers to access early warning threshold information anytime and anywhere via the network, facilitating centralized monitoring and management of multiple wind turbines, timely understanding of the operating status and early warning situation of each wind turbine, and improving the efficiency and level of equipment management.

[0058] In step (5), the maintenance recommendations include shutdown for inspection, local reinforcement, or complete replacement, and are generated based on the deformation degree of the three-dimensional deformation model. The advantages of this step (5), where maintenance recommendations include shutdown for inspection, local reinforcement, or complete replacement, and are generated based on the deformation degree of the three-dimensional deformation model, are as follows:

[0059] Targeted maintenance strategies: Based on different degrees of deformation, different maintenance suggestions are provided, such as shutdown for inspection, partial reinforcement or complete replacement. The most appropriate maintenance measures can be taken for specific fault conditions to ensure the effectiveness of maintenance work.

[0060] Rational allocation of maintenance resources: Based on the deformation degree calculation of the three-dimensional deformation model, maintenance suggestions are generated, which can quantitatively assess the severity of the wind turbine failure, thereby rationally allocating maintenance resources, prioritizing the treatment of severely deformed parts, and improving the efficiency and economy of maintenance work.

[0061] Extending equipment lifespan: Timely and accurate repair of deformed parts prevents further deterioration of equipment failure, helps extend the service life of the fan, and reduces equipment replacement costs.

[0062] In step (1), the temperature sensor is used to synchronously monitor the temperature distribution on the surface of the housing and to perform correlation analysis between the temperature data and vibration and acoustic signals in order to eliminate thermal deformation interference.

[0063] This invention utilizes multiple sensors positioned at key locations on the casing of an axial flow fan to simultaneously collect multi-source data, including vibration, acceleration, temperature, and acoustic signals, and records the fan's operating parameters. The vibration signals undergo time-domain filtering and noise reduction, feature extraction, and spectral analysis to identify characteristic frequencies related to casing deformation. Simultaneously, frequency-domain analysis of the acoustic signals is performed to extract features and, combined with the vibration spectrum, identify areas of non-uniform flow, pinpointing high-risk locations for casing deformation. The uniformity of blade tip clearance and casing joint surface clearance are measured, and casing deformation data is acquired using a laser rangefinder or 3D scanner to construct a 3D deformation model. Finally, a comprehensive diagnostic assessment determines whether the casing is deformed, generating maintenance suggestions and a diagnostic report, while simultaneously triggering tiered early warning systems.

[0064] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for diagnosing deformation of an axial flow fan casing, characterized in that, Includes the following steps: (1) Multi-source data acquisition: Vibration sensors, acceleration sensors and temperature sensors are simultaneously arranged at the air inlet, air outlet, bearing box and support point of the axial flow fan casing. Simultaneously, a stethoscope is used to collect the sound signals in the axial and circumferential directions of the casing and record the fan operating parameters, including speed, blade angle and air volume. (2) Vibration signal processing and spectrum analysis: The collected vibration signals are filtered and denoised in the time domain to extract vibration velocity, displacement and acceleration features; a spectrum diagram is generated by fast Fourier transform to identify characteristic frequencies related to casing deformation, including the 1x speed frequency and its harmonics, the blade passing frequency and its harmonics, and the asynchronous vibration frequency. (3) Sound signal feature extraction: Perform frequency domain analysis on the auscultation signal to extract the sound pressure level and frequency distribution features, and combine the vibration spectrum to determine the uneven flow area and locate the high-risk location of casing deformation; (4) Casing structure condition inspection: Measure the uniformity of the blade tip clearance. If the deviation exceeds the ±0.5mm threshold, it is determined that the impeller and casing are not properly matched. Check the condition of the casing welds, reinforcing ribs and connecting bolts. Detect the casing joint surface clearance with a feeler gauge. If the local clearance exceeds 0.1mm, it is determined that the casing is deformed locally. Use a laser rangefinder or 3D scanner to obtain casing deformation data and construct a 3D deformation model of the casing. (5) Comprehensive diagnosis and fault location: If the following conditions are met simultaneously, the problem is determined to be casing deformation: a. Asynchronous harmonics account for more than 20% of the vibration spectrum; b. The intensity of the acoustic signal is significantly higher in a specific circumferential direction than in other areas; c. Deviation in blade tip clearance or abnormal clearance at the casing mating surface; By combining a 3D deformation model, the deformation area and degree are determined, and maintenance suggestions are generated. (6) Real-time early warning and report output: The diagnostic results are compared with the preset thresholds to trigger graded early warnings and automatically generate a diagnostic report containing a diagram of the deformation location, a spectrum feature diagram and maintenance suggestions.

2. The diagnostic method for axial flow fan casing deformation according to claim 1, characterized in that, In step (1), the auscultation device is an acoustic sensor with a sampling frequency of not less than 20kHz.

3. The diagnostic method for axial flow fan casing deformation according to claim 1, characterized in that, In step (2), the asynchronous vibration frequency range of the spectrum analysis is 0.4 to 0.6 times the rotational speed frequency.

4. The diagnostic method for axial flow fan casing deformation according to claim 1, characterized in that, In step (4), the three-dimensional deformation model is constructed using a laser rangefinder with a measurement accuracy of not less than ±0.05 mm.

5. The diagnostic method for axial flow fan casing deformation according to claim 1, characterized in that, In step (6), the threshold for the graded early warning is dynamically adjusted according to the wind turbine model and stored in a cloud database.

6. The diagnostic method for axial flow fan casing deformation according to claim 1, characterized in that, In step (5), the maintenance recommendations include shutdown for inspection, local reinforcement or overall replacement, and are generated based on the degree of deformation of the three-dimensional deformation model.

7. The diagnostic method for axial flow fan casing deformation according to claim 1, characterized in that, The temperature sensor mentioned in step (1) is used to synchronously monitor the temperature distribution on the surface of the casing and to correlate the temperature data with vibration and acoustic signals to eliminate thermal deformation interference.