Optical fiber sensor strain fatigue limit sensing life testing device

Abstract

The invention is suitable for the field of machine tools, and provides an optical fiber sensor strain fatigue limit sensing life testing device, which comprises a workbench, the workbench is provided with a protective cover, the workbench is fixedly connected with a fixing frame, the fixing frame is fixedly connected with a constant-strength beam, the end part of the constant-strength beam is fixedly provided with a vibrator, and the end part of the vibrator is fixedly provided with an optical fiber sensor strain fatigue limit sensing life testing device. A piezoelectric sensor body is arranged on the end face of one side and fixedly connected with the equal-strength beam through a limiting assembly, an optical fiber sensor body can be fixedly connected to the equal-strength beam through a screw, and the workbench is fixedly connected with the limiting assembly. A laser vibration meter body is arranged at the position, right opposite to the equal-strength beam, of the limiting assembly. The laser vibration meter further comprises a data processing system. According to the invention, the problem of measurement error accumulation caused by environmental interference of a traditional test device is solved, and intelligent fusion of multi-sensor data and adaptive adjustment of a measurement environment are realized.

Description

Technical Field

[0001] This invention belongs to the field of machine tools, and particularly relates to a fiber optic sensor strain fatigue limit sensing life testing device. Background Technology

[0002] Fiber optic sensors are prone to fatigue failure during long-term strain monitoring, and accurately assessing their strain fatigue limit sensing life is crucial for structural health monitoring. Traditional testing devices mostly use a single sensor to measure vibration frequency, which has the following technical drawbacks:

[0003] First, during piezoelectric sensor measurements, changes in preload can lead to unstable contact impedance, electromagnetic interference can introduce signal drift, and internal amplifier noise can reduce the signal-to-noise ratio; all these factors collectively affect measurement accuracy. Second, laser vibrometers have stringent environmental requirements; dust inside the protective enclosure can scatter the laser beam, changes in ambient light can interfere with photoelectric conversion, and fluctuations in emission power can affect measurement stability. A single sensor cannot overcome the interference from complex environments. Furthermore, existing devices lack a multi-source data fusion mechanism and cannot dynamically allocate weights based on the accuracy-influencing factors of the piezoelectric sensor and laser vibrometer, resulting in insufficient reliability of vibration reference values. More importantly, changes in temperature and humidity in the testing environment can cause thermal expansion and contraction of materials, and air pressure fluctuations can affect the air refractive index; these external factors can systematically interfere with measurement results, but current technologies lack the ability to assess and control measurement accuracy in real time, making it difficult to guarantee the reliability of long-term testing.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide a fiber optic sensor strain fatigue limit sensing life testing device, which aims to solve the problem that the existing technology lacks the ability to evaluate and control the measurement accuracy in real time, making it difficult to guarantee the reliability of long-term testing.

[0006] This invention is implemented as follows: a fiber optic sensor strain fatigue limit sensing life testing device includes a worktable with a protective cover, a fixed frame fixedly connected to the worktable, an equal-strength beam fixedly connected to the fixed frame, a vibrator fixedly mounted at one end of the equal-strength beam, a piezoelectric sensor body mounted on one end face of the equal-strength beam, the piezoelectric sensor body being fixedly connected to the equal-strength beam via a limiting component, a fiber optic sensor body being fixedly connected to the equal-strength beam by screws, with the fiber optic sensor body facing the piezoelectric sensor body, a limiting component fixedly connected to the worktable, a laser vibrometer body positioned opposite the limiting component to the equal-strength beam, and a data processing system comprising:

[0007] The data acquisition module is used to acquire the status information of the piezoelectric sensor body, the fiber optic sensor body, and the laser vibration meter body, as well as the external environment information.

[0008] The piezoelectric sensor measurement accuracy impact assessment module can construct a piezoelectric sensor measurement accuracy impact assessment model based on the pre-tightening force of the limiting component on the piezoelectric sensor body, the magnetic field strength in the protective cover, and the noise intensity of the amplifier inside the piezoelectric sensor body, and output the piezoelectric sensor measurement accuracy impact assessment coefficients.

[0009] The laser vibration meter measurement accuracy impact assessment module can construct a laser vibration meter measurement accuracy impact assessment model based on the dust concentration in the protective cover, light intensity, and the emission power of the laser vibration meter itself, and output the laser vibration meter measurement accuracy impact assessment coefficient;

[0010] The weight allocation module can import the evaluation coefficients of the piezoelectric sensor measurement accuracy and the laser vibration meter measurement accuracy into the constructed weight allocation model, and obtain the piezoelectric sensor measurement weight coefficients and the laser vibration meter measurement weight coefficients.

[0011] The piezoelectric sensor measurement weight coefficient, the laser vibration meter measurement weight coefficient, the piezoelectric sensor measurement real-time vibration frequency value, and the laser vibration meter measurement real-time vibration frequency value are imported into the constructed fusion position model, and the standard vibration frequency value is output.

[0012] The environmental impact assessment module can construct an external environmental impact model based on external temperature, humidity, and atmospheric pressure information, and output the external environmental impact coefficients.

[0013] The measurement result accuracy assessment module can construct a measurement result accuracy assessment model based on the standard vibration frequency value and the output environmental influence coefficient, and output the measurement result accuracy assessment coefficient.

[0014] The protective cover is equipped with a temperature controller, a humidity controller, and a pressure controller, and these controllers are electrically connected to the data processing system.

[0015] In a further technical solution, the limiting component includes a bolt and a spring. The bolt is threadedly connected to the equal strength beam and passes through the base of the piezoelectric sensor body. A spring is provided between the bolt and the base of the piezoelectric sensor body. The spring applies a preload force to the base of the piezoelectric sensor body, so that the piezoelectric sensor body and the equal strength beam are elastically pressed together.

[0016] A further technical solution is to take the absolute value of the difference between the actual preload force of the limiting component on the piezoelectric sensor body and the optimal preload force, and then divide it by the optimal preload force to obtain the preload force index; to divide the actual magnetic field strength in the protective cover by the critical magnetic field strength to obtain the magnetic field strength index; and to divide the actual noise strength of the amplifier inside the piezoelectric sensor body by the critical noise strength of the amplifier to obtain the noise index.

[0017] The evaluation model for the impact of piezoelectric sensor measurement accuracy is as follows:

[0018] ;

[0019] in The evaluation coefficient is used to assess the impact of piezoelectric sensor measurement accuracy. Preload index, The magnetic field strength index, Noise level; , as well as These are the weighting coefficients for the effects of preload, magnetic field strength, and noise, respectively. ,and , as well as All greater than .

[0020] A further technical solution is to divide the actual dust concentration in the protective cover by the maximum allowable dust concentration in the protective cover to obtain the dust concentration index; to take the absolute value of the difference between the actual light intensity and the optimal light intensity in the protective cover and divide it by the optimal light intensity to obtain the light intensity index; and to take the absolute value of the difference between the real-time emission power of the laser vibration meter body and the optimal emission power of the laser vibration meter body and divide it by the optimal emission power of the laser vibration meter body to obtain the power index of the laser vibration meter body.

[0021] The evaluation model for the impact of laser vibrometer measurement accuracy is as follows:

[0022] ;

[0023] in The evaluation coefficient for the impact of laser vibrometer measurement accuracy. , as well as These are the weighting coefficients for the effects of dust concentration, light intensity, and emission power, respectively. ,and , as well as All greater than ; The dust concentration index. Light intensity index, This is the power index of the laser vibration meter body.

[0024] A further technical solution is that the weight allocation model is as follows:

[0025] , ;

[0026] in For measuring the weighting coefficients of piezoelectric sensors, The weighting coefficients are measured for the laser vibrometer.

[0027] A further technical solution is provided, wherein the standard vibration frequency value is:

[0028] ;

[0029] in For measuring the weighting coefficients of piezoelectric sensors, For measuring the weighting coefficients of the laser vibrometer, This represents the real-time vibration frequency value of the piezoelectric sensor. This represents the real-time vibration frequency value of the laser vibrometer. This is the standard vibration frequency value.

[0030] A further technical solution is to take the absolute value of the difference between the actual temperature value and the reference temperature value in the protective cover, and then divide it by the reference temperature value to obtain the temperature index; to take the difference between the actual humidity value and the reference humidity value in the protective cover, and then divide it by the reference humidity value to obtain the humidity index; and to take the difference between the actual air pressure value and the reference air pressure value in the protective cover, and then divide it by the reference air pressure value to obtain the air pressure index.

[0031] The external environment impact model is as follows:

[0032] .

[0033] in , as well as These are the weighting coefficients for the effects of temperature, humidity, and air pressure, respectively. ,and , as well as All greater than ; Temperature index Humidity index Barometric pressure index This represents the external environmental impact coefficient.

[0034] A further technical solution involves taking the absolute value of the difference between the standard vibration frequency value and the ideal vibration frequency value, and then dividing it by the ideal vibration frequency value to obtain the vibration frequency index. The accuracy evaluation model for the measurement results is as follows:

[0035] ;

[0036] in For tolerance sensitivity, Greater than , The frequency index is the oscillation index. For accuracy coefficient, This represents the external environmental impact coefficient.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This application addresses the problem of measurement error accumulation caused by environmental interference in traditional testing devices, achieving intelligent fusion of multi-sensor data and adaptive adjustment of the measurement environment. By constructing a hierarchical accuracy evaluation model, the impact of mechanical assembly parameters and environmental factors on the measurement system is effectively quantified. A dynamic weight allocation strategy is adopted to ensure reliable standard vibration frequency values ​​are still output even when some sensors are interfered with. Through a closed-loop environmental control system, the system errors introduced by temperature, humidity, and air pressure fluctuations are significantly reduced, providing a stable and reliable test benchmark for fiber optic sensor lifespan assessment.

[0039] This application addresses the issue of decreased measurement accuracy in piezoelectric sensors due to multi-factor coupling, achieving simultaneous quantitative evaluation of preload drift, magnetic field interference, and internal noise. By combining exponential functions with linear terms, the nonlinear influence characteristics of different interference factors are accurately characterized, providing reliable evaluation coefficient inputs for the subsequent weight allocation module, thereby improving the accuracy of vibration frequency fusion calculation.

[0040] This application effectively solves the problems of optical path offset caused by dust scattering, interference of stray light from the environment with laser signal reception, and measurement inaccuracies caused by power fluctuations. By calculating the dust concentration index in real time, it can provide early warning of excessive dust levels and trigger a cleaning device to maintain the optical path. Dynamic monitoring of the light intensity index can automatically adjust the opening and closing of the light shield to suppress stray light interference. Continuous evaluation of the power index can be linked to the laser power feedback system to achieve stable output power control. Ultimately, this allows the laser vibrometer to maintain measurement accuracy even under dust pollution, light variation, and power fluctuation environments, providing reliable vibration frequency reference data for fiber optic sensor lifespan testing.

[0041] Through the above technical solution, this application solves the problem of system error accumulation caused by fluctuations in environmental parameters, and realizes real-time quantitative evaluation of the accuracy of measurement results. By dynamically correcting the environmental interference weights and adaptively adjusting the tolerance threshold, the impact of temperature, humidity, and air pressure changes on life assessment is effectively reduced. The continuous output of the accuracy coefficient provides a precise feedback signal to the environmental control module, enabling the testing device to automatically optimize operating conditions, thereby improving the reliability of the fatigue limit life assessment of fiber optic sensors. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of each mechanism on the workbench in this invention;

[0044] Figure 3 This is a schematic diagram of the limiting component in this invention;

[0045] Figure 4 This is a schematic diagram of the data processing system in this invention.

[0046] In the attached diagram: 1. Workbench; 2. Protective cover; 3. Fixing frame; 4. Equal strength beam; 5. Vibrator; 6. Piezoelectric sensor body; 7. Fiber optic sensor body; 8. Laser vibration meter body; 9. Limiting component; 91. Bolt; 92. Spring; 10. Support frame. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0049] like Figures 1-4As shown, an embodiment of the present invention provides a fiber optic sensor body strain fatigue limit sensing life testing device, including a worktable 1, a protective cover 2, a fixed frame 3 fixedly connected to the worktable 1, an equal strength beam 4 fixedly connected to the fixed frame 3, a vibrator 5 fixedly mounted at one end of the equal strength beam 4, a piezoelectric sensor body 6 mounted on one end face of the equal strength beam 4, the piezoelectric sensor body 6 fixedly connected to the equal strength beam 4 via a limiting component 9, a fiber optic sensor body 7 fixedly connected to the other end face of the equal strength beam 4 opposite to the piezoelectric sensor body 6 by screws, a laser vibrometer body 8 fixedly mounted on the worktable 1 opposite to the equal strength beam 4 via the limiting component 9, and a data processing system including:

[0050] The data acquisition module is used to acquire the status information of the piezoelectric sensor body 6, the fiber optic sensor body 7, and the laser vibration meter body 8, as well as the external environment information.

[0051] The piezoelectric sensor measurement accuracy impact assessment module can construct a piezoelectric sensor measurement accuracy impact assessment model based on the pre-tightening force of the limiting component 9 on the piezoelectric sensor body 6, the magnetic field strength in the protective cover 2, and the noise intensity of the amplifier inside the piezoelectric sensor body 6, and output the piezoelectric sensor measurement accuracy impact assessment coefficient.

[0052] The laser vibration meter measurement accuracy impact assessment module can construct a laser vibration meter measurement accuracy impact assessment model based on the dust concentration in the protective cover 2, the light intensity, and the emission power of the laser vibration meter body 8, and output the laser vibration meter measurement accuracy impact assessment coefficient.

[0053] The weight allocation module can import the evaluation coefficients of the piezoelectric sensor measurement accuracy and the laser vibration meter measurement accuracy into the constructed weight allocation model, and obtain the piezoelectric sensor measurement weight coefficients and the laser vibration meter measurement weight coefficients.

[0054] The piezoelectric sensor measurement weight coefficient, the laser vibration meter measurement weight coefficient, the piezoelectric sensor measurement real-time vibration frequency value, and the laser vibration meter measurement real-time vibration frequency value are imported into the constructed fusion position model, and the standard vibration frequency value is output.

[0055] The environmental impact assessment module can construct an external environmental impact model based on external temperature, humidity, and atmospheric pressure information, and output the external environmental impact coefficients.

[0056] The measurement result accuracy assessment module can construct a measurement result accuracy assessment model based on the standard vibration frequency value and the output environmental influence coefficient, and output the measurement result accuracy assessment coefficient.

[0057] The protective cover 2 is equipped with a temperature controller, a humidity controller, and a pressure controller, and the temperature controller, humidity controller, and pressure controller are electrically connected to the data processing system.

[0058] In this embodiment, the fiber optic sensor body 7 is fixedly connected to the other end face of the equal strength beam 4 opposite to the piezoelectric sensor body 6 by screws.

[0059] The vibrator 5 is started and maintains vibration at the same frequency. At this time, the vibrator 5 drives the equal strength beam 4 to vibrate synchronously. The vibration frequency of the equal strength beam 4 is measured simultaneously by the piezoelectric sensor body 6, the fiber optic sensor body 7, and the laser vibration meter body 8. Then, the data processing system generates a standard vibration frequency value. The vibration frequency measured by the fiber optic sensor body 7 is compared with the standard vibration frequency value. The difference between the vibration frequency value measured by the fiber optic sensor body 7 and the standard vibration frequency value is used to determine the strain fatigue limit sensing life of the fiber optic sensor body 7. When the accuracy evaluation coefficient of the measured result exceeds the set threshold, the measurement result accuracy evaluation module can adjust the temperature controller, humidity controller, and air pressure controller in the protective cover 2.

[0060] The limiting component 9 refers to the mechanical structure used to fix the piezoelectric sensor and apply preload. Specifically, it can be implemented using a combination of bolt 91 and spring 92. The preload is controlled by adjusting the screw depth of bolt 91 to change the compression of spring 92. The equal-strength beam 4 refers to a cantilever beam structure with uniform stress distribution. The data acquisition module is a multi-source data acquisition unit, which can be implemented using a multi-channel data acquisition card and sensor interface circuit to simultaneously acquire vibration frequency signals and environmental parameter signals. The weighting model is a dynamic data fusion algorithm that adjusts the fusion weights based on the real-time accuracy evaluation results of the sensor. The environmental impact assessment module is an environmental interference quantification unit, which can be implemented using a linear weighted model to comprehensively evaluate the impact of temperature, humidity, and air pressure changes on the measurement system.

[0061] Specifically, vibrator 5 drives the equal-strength beam 4 to generate a standard vibration load, and the piezoelectric sensor and laser vibrometer synchronously collect vibration signals. The data acquisition module acquires the sensor output signals and environmental parameters inside the protective cover 2 in real time. The piezoelectric sensor measurement accuracy impact assessment module calculates the comprehensive impact coefficient of preload deviation, magnetic field strength, and circuit noise, while the laser vibrometer measurement accuracy impact assessment module calculates the comprehensive impact coefficient of dust concentration, light interference, and power fluctuation. The weight allocation module dynamically allocates data weights based on the accuracy coefficients of the two types of sensors, generating a fused standard vibration frequency value. The environmental impact assessment module calculates the interference coefficient of temperature, humidity, and air pressure changes on the measurement system, and the accuracy assessment module combines the standard vibration frequency value and the environmental interference coefficient to output a reliability index. When the accuracy coefficient is lower than a set threshold, the data processing system automatically adjusts the temperature, humidity, and air pressure controllers inside the protective cover 2, forming a closed-loop control circuit.

[0062] Compared to existing technologies, traditional devices rely on single-sensor measurements and lack environmental interference suppression mechanisms. This solution effectively overcomes the vulnerability of single sensors to interference through multi-sensor collaborative measurement and dynamic data fusion. Existing technologies do not establish a quantitative relationship between environmental parameters and measurement accuracy; this solution achieves real-time compensation for environmental interference through a hierarchical evaluation model. Existing systems cannot automatically optimize the test environment; this solution maintains optimal test conditions through closed-loop feedback control, significantly improving the repeatability of measurement results.

[0063] Through the above technical solutions, this application solves the problem of measurement error accumulation caused by environmental interference in traditional testing devices, and realizes intelligent fusion of multi-sensor data and adaptive adjustment of the measurement environment. By constructing a hierarchical accuracy evaluation model, the influence of mechanical assembly parameters and environmental factors on the measurement system is effectively quantified. A dynamic weight allocation strategy is adopted to ensure that reliable standard vibration frequency values ​​can still be output when some sensors are interfered with. Through a closed-loop environmental control system, the system errors introduced by temperature, humidity, and air pressure fluctuations are significantly reduced, providing a stable and reliable test benchmark for fiber optic sensor lifespan assessment.

[0064] In a preferred embodiment of the present invention, the limiting component 9 includes a bolt 91 and a spring 92. The bolt 91 is threadedly connected to the equal strength beam 4, and the bolt 91 passes through the base of the piezoelectric sensor body 6. A spring 92 is provided between the bolt 91 and the base of the piezoelectric sensor body 6. The spring 92 applies a preload to the base of the piezoelectric sensor body 6, so that the piezoelectric sensor body 6 and the equal strength beam 4 are elastically pressed together.

[0065] Bolt 91 is a threaded fastener, typically made of stainless steel, that secures the piezoelectric sensor body 6 to the equal-strength beam 4 via a threaded connection, providing both mechanical positioning and rigid support. The bolt 91's penetration through the piezoelectric sensor base ensures the sensor remains stationary during vibration testing while providing installation space for spring 92. Spring 92 is a mechanical element with elastic deformation capabilities, typically a helical compression spring, pre-compressed and installed between bolt 91 and the piezoelectric sensor base. The elastic force of spring 92 is converted into a continuous clamping force on the sensor base, counteracting any gaps or loosening that may occur during vibration.

[0066] Compared to existing technologies, traditional piezoelectric sensor fixing methods use rigid bolts 91 for direct locking. The preload gradually decreases as the threaded joint loosens or material creeps, leading to increased contact resistance between the sensor and the test beam and signal drift. This solution introduces a spring 92 as an elastic medium, converting the static preload into a dynamically adjustable elastic clamping force. This maintains mechanical positioning accuracy while eliminating preload instability caused by long-term vibration or environmental temperature changes. Through this technical solution, this application can maintain a constant contact pressure between the piezoelectric sensor and the equal-strength beam 4, suppressing signal distortion caused by preload drift. The elastic clamping structure disperses stress distribution at the mechanical connection points, preventing microcracks or fatigue damage to the sensor due to localized stress concentration, thereby improving the long-term stability of vibration frequency measurement data and extending the effective working time of the sensor in fatigue life testing.

[0067] As a preferred embodiment of the present invention, the absolute value of the difference between the actual preload force of the limiting component 9 on the piezoelectric sensor body 6 and the optimal preload force is taken and then divided by the optimal preload force to obtain the preload force index; the actual magnetic field strength in the protective cover 2 is divided by the critical magnetic field strength to obtain the magnetic field strength index; and the actual noise strength of the internal amplifier of the piezoelectric sensor body 6 is divided by the critical noise strength of the internal amplifier to obtain the noise index.

[0068] The evaluation model for the impact of piezoelectric sensor measurement accuracy is as follows:

[0069] ;

[0070] in The evaluation coefficient is used to assess the impact of piezoelectric sensor measurement accuracy. Preload index, The magnetic field strength index, Noise level; , as well as These are the weighting coefficients for the effects of preload, magnetic field strength, and noise, respectively. , as well as The specific values ​​can be determined through experimental results or production experience. ,and , as well as All greater than .

[0071] The preload index refers to the absolute value of the relative deviation between the actual preload and the optimal preload. Specifically, it is calculated by measuring the actual preload with a force sensor and comparing it to the preset optimal value. This reflects the impact of preload deviation on the contact stability of the piezoelectric sensor. The magnetic field strength index is the ratio of the actual magnetic field strength to the critical magnetic field strength. Specifically, it is calculated by measuring the magnetic field strength inside the protective cover 2 with a magnetometer and comparing it to the critical threshold. This characterizes the degree of interference from the external magnetic field on the piezoelectric sensor's signal output. The noise index is the ratio of the actual noise intensity of the internal amplifier to the critical noise intensity. Specifically, it is calculated by acquiring the amplifier's output signal with a noise analyzer, extracting the noise component, and normalizing it. This assesses the degrading effect of circuit noise on the sensor's signal-to-noise ratio.

[0072] Specifically, the preload index reflects its nonlinear effect through an exponential function. As the preload deviation increases, the exponential term rapidly approaches a saturation value, highlighting the high sensitivity of preload anomalies to measurement accuracy. The magnetic field strength index uses a linear superposition method, directly reflecting the positive correlation between external magnetic field interference and measurement error. The noise index also uses an exponential function; its influence coefficient increases significantly when the noise intensity approaches a critical value. The three indices are integrated into a single evaluation coefficient through weight allocation. The weighting coefficients for preload and noise effects can be dynamically adjusted according to the sensor type. For example, the preload weight can be increased in high-temperature environments to compensate for the thermal expansion effect of materials.

[0073] Compared with existing technologies, traditional methods only handle preload or magnetic field interference through threshold alarms or single-factor compensation mechanisms, without establishing a multi-factor coupling model. This solution introduces noise index and nonlinear functions to simultaneously quantify the combined impact of mechanical assembly, environmental interference, and circuit noise, and achieves dynamic priority allocation of different interference sources through normalized weights.

[0074] Through the above technical solution, this application solves the problem of decreased measurement accuracy of piezoelectric sensors due to multi-factor coupling, and achieves synchronous quantitative evaluation of preload drift, magnetic field interference, and internal noise. By combining exponential functions and linear terms, the nonlinear influence characteristics of different interference factors are accurately characterized, providing reliable evaluation coefficient inputs for the subsequent weight allocation module, thereby improving the accuracy of vibration frequency fusion calculation.

[0075] In a preferred embodiment of the present invention, the dust concentration index is obtained by dividing the actual dust concentration in the protective cover 2 by the maximum allowable dust concentration in the protective cover 2; the light intensity index is obtained by taking the absolute value of the difference between the actual light intensity and the optimal light intensity in the protective cover 2 and dividing it by the optimal light intensity; and the power index of the laser vibration meter body 8 is obtained by taking the absolute value of the difference between the real-time emission power and the optimal emission power of the laser vibration meter body 8 and dividing it by the optimal emission power of the laser vibration meter body 8.

[0076] The evaluation model for the impact of laser vibrometer measurement accuracy is as follows:

[0077] ;

[0078] in The evaluation coefficient for the impact of laser vibrometer measurement accuracy. , as well as These are the weighting coefficients for the effects of dust concentration, light intensity, and emission power, respectively. ,and , as well as All greater than ; The dust concentration index. Light intensity index, This is the power index of the laser vibration meter body 8.

[0079] The dust concentration index refers to the ratio of the actual dust concentration to the maximum allowable value. Specifically, it can be calculated using a particulate sensor to monitor dust concentration data in real time, quantifying the dust pollution level and characterizing the degree of dust interference with laser scattering. The illumination intensity index refers to the absolute value of the relative deviation between the actual illumination intensity and the optimal value. Specifically, it can be calculated using a photosensitive sensor to collect ambient illumination data, reflecting the intensity of stray light interference with the laser signal. The power index refers to the absolute value of the relative deviation between the real-time transmitted power and the optimal power. Specifically, it can be calculated using a power meter to monitor the laser output power, assessing the impact of laser energy fluctuations on measurement stability through normalization. The weighting coefficients for dust concentration, illumination intensity, and transmitted power in the linear weighted model can be determined using the analytic hierarchy process (AHP) to determine the contribution of each factor to measurement accuracy, and then a weighted summation is used to comprehensively evaluate the overall impact of environmental interference on the laser vibrometer.

[0080] Specifically, the dust concentration index uses a particulate sensor to acquire real-time dust concentration data within the protective cover 2, and compares this data with a preset maximum allowable value. When the actual value approaches the allowable threshold, the index approaches 1, indicating that dust interference has reached a critical state. The illuminance index uses a photosensitive sensor to measure ambient light intensity, and calculates the absolute value of the difference with a preset optimal illuminance, then normalizes the result. When the actual illuminance deviates from the optimal value, the index increases, reflecting enhanced stray light interference. The power index monitors the instantaneous fluctuations of the laser emission power using a power meter, and calculates the absolute value of its relative deviation from the preset optimal power, characterizing the measurement error caused by laser energy instability. The three indices are weighted and summed to generate a comprehensive evaluation coefficient, with the weighting coefficients calibrated based on experimental data or production experience.

[0081] Compared with existing technologies, traditional methods only use a single threshold to determine whether dust or light intensity exceeds the standard, without establishing a quantitative assessment model for the coupled effects of multiple factors. This solution transforms dust concentration, light intensity, and power fluctuations into dimensionless exponents through normalization processing, and uses a linear weighted model to comprehensively assess the dynamic interference of each factor, which can more accurately reflect the actual working state of the laser vibrometer in complex environments.

[0082] Through the above technical solutions, this application effectively solves the problems of optical path offset caused by dust scattering, interference of stray light from the environment with laser signal reception, and measurement inaccuracies caused by power fluctuations. Real-time calculation of the dust concentration index can provide early warning of excessive dust levels, triggering a cleaning device to maintain the optical path cleanliness; dynamic monitoring of the light intensity index can automatically adjust the opening and closing of the light shield to suppress stray light interference; continuous evaluation of the power index can be linked to the laser power feedback system to achieve stable output power control. Ultimately, this enables the laser vibrometer to maintain measurement accuracy even under dust pollution, light variation, and power fluctuation environments, providing reliable vibration frequency reference data for fiber optic sensor lifespan testing.

[0083] In a preferred embodiment of the present invention, the weight allocation model is as follows:

[0084] , ;

[0085] in The evaluation coefficient is used to assess the impact of piezoelectric sensor measurement accuracy. The evaluation coefficient for the impact of laser vibrometer measurement accuracy. For measuring the weighting coefficients of piezoelectric sensors, The weighting coefficients are measured for the laser vibrometer.

[0086] The piezoelectric sensor measurement accuracy impact assessment coefficient is a parameter characterizing the degree of interference to the piezoelectric sensor, calculated by weighting the preload index, magnetic field strength index, and noise index. Specifically, it can be implemented using an exponential weighted summation model to quantify the measurement reliability of the piezoelectric sensor in the current environment. Similarly, the laser vibrometer measurement accuracy impact assessment coefficient is a parameter characterizing the degree of interference to the laser vibrometer, calculated by weighting the dust concentration index, light intensity index, and power index. Specifically, it can be implemented using a linear weighted model to quantify the measurement reliability of the laser vibrometer in the current environment. The weight allocation model is a mathematical expression based on the reciprocals of the two sensor assessment coefficients, normalized. Specifically, it can be implemented using reciprocal operations and normalization calculations, dynamically allocating weight coefficients to prioritize sensor data with less interference.

[0087] Specifically, the evaluation coefficients for the piezoelectric sensor and laser vibrometer measurements reflect the degree of interference to each sensor in the real-time environment. When the evaluation coefficient of the piezoelectric sensor increases, it indicates that it is more susceptible to interference from preload drift, magnetic fields, or noise, and its weight coefficient will decrease accordingly. Conversely, if the evaluation coefficient of the laser vibrometer decreases, it indicates that it is less affected by dust, light, or power fluctuations, and its weight coefficient will increase. By inputting the reciprocals of the two evaluation coefficients into the weight allocation model and performing normalization, the sensor less affected by interference is assigned a higher weight. This dynamic allocation mechanism can adapt to environmental changes and avoid measurement inaccuracies caused by sudden interference from a single sensor.

[0088] Compared to existing technologies, traditional testing devices typically employ fixed weights or rely solely on data from a single sensor, failing to dynamically adjust the measurement benchmark based on environmental interference. This solution, however, calculates the interference levels of both sensors in real time and dynamically allocates weights. This allows the system to automatically reduce the weight of affected sensor data when dust concentration changes abruptly or electromagnetic interference intensifies, thereby suppressing the propagation of measurement errors. For example, when the evaluation coefficient of a piezoelectric sensor increases due to preload relaxation, its weight coefficient decreases, increasing the proportion of laser vibrometer data in the fusion result and ensuring the stability of the vibration frequency value.

[0089] Through the above technical solution, this application solves the problem of lack of dynamic calibration in traditional testing devices when sensor measurement accuracy fluctuates due to environmental interference. By weighting the data based on the real-time interference level, the measurement data from the piezoelectric sensor and the laser vibrometer can be optimized and fused according to the current environmental conditions, effectively improving the reliability of the vibration frequency reference value. For example, when a sudden increase in dust concentration causes the evaluation coefficient of the laser vibrometer to rise, the system automatically reduces its weight and uses more data from the less disturbed piezoelectric sensor, thereby avoiding measurement deviations caused by the failure of a single sensor. Therefore, the life assessment results of the strain fatigue limit of the fiber optic sensor can be established on a more stable vibration frequency measurement basis.

[0090] In a preferred embodiment of the present invention, the standard vibration frequency value is:

[0091] ;

[0092] in This represents the real-time vibration frequency value of the piezoelectric sensor. This represents the real-time vibration frequency value of the laser vibrometer. The standard frequency value, For measuring the weighting coefficients of piezoelectric sensors, The weighting coefficients are measured for the laser vibrometer.

[0093] The piezoelectric sensor measurement weight coefficient refers to the contribution of the piezoelectric sensor in the fusion model. It can be calculated using a weighted allocation model, and its value is negatively correlated with the evaluation coefficient of the piezoelectric sensor's measurement accuracy. When the piezoelectric sensor's accuracy decreases due to preload drift or electromagnetic interference, this weight coefficient automatically decreases. Similarly, the laser vibrometer measurement weight coefficient refers to the contribution of the laser vibrometer in the fusion model. It can also be calculated using a weighted allocation model, and its value is negatively correlated with the evaluation coefficient of the laser vibrometer's measurement accuracy. When the laser vibrometer's accuracy decreases due to dust concentration or power fluctuations, this weight coefficient automatically decreases.

[0094] Specifically, after the real-time vibration frequency values ​​of the piezoelectric sensor and the laser vibrometer are synchronously acquired, they are multiplied by their corresponding weighting coefficients, and the weighted sum of the two is the standard vibration frequency value. The weighting coefficients are dynamically determined by the evaluation coefficients affecting the measurement accuracy of the piezoelectric sensor and the laser vibrometer: when the accuracy of a sensor decreases due to environmental interference, its corresponding evaluation coefficient increases, and its weighting coefficient decreases accordingly, thereby reducing the negative impact of the sensor's error on the final result. For example, when the piezoelectric sensor experiences measurement deviation due to magnetic field interference, its weighting coefficient is lowered, and the measurement data from the laser vibrometer accounts for a higher proportion in the fusion result. Through this mechanism, the measurement advantages of the two sensors are complemented and utilized, and the final output standard vibration frequency value can adapt to environmental changes, providing a reliable benchmark for the lifetime assessment of fiber optic sensors.

[0095] Compared to existing technologies, traditional methods rely on single sensors or fixed-weight multi-sensor fusion, failing to dynamically adjust the data fusion strategy based on environmental interference. For example, in existing technologies, the weight allocation for piezoelectric sensors and laser vibrometers is fixed. When a sensor fails due to sudden environmental factors (such as a sudden increase in dust concentration), the fusion result still contains significant errors. This proposed solution, however, evaluates sensor accuracy in real time and dynamically allocates weights, ensuring that the sensor with higher measurement accuracy dominates at any given time, effectively suppressing the impact of environmental interference on the reference value.

[0096] Through the above technical solution, this application solves the problem of unreliable vibration frequency reference values ​​caused by environmental interference from a single sensor, and realizes dynamic optimization and fusion of multi-sensor data. In scenarios where piezoelectric sensors drift due to preload or laser vibrometers are interfered with by dust, the weighting mechanism can automatically reduce the contribution of affected sensors, ensuring the accuracy of the standard vibration frequency value. This solution provides vibration frequency reference data with stronger anti-interference capabilities for fatigue life assessment of fiber optic sensors, significantly improving the reliability of test results.

[0097] In a preferred embodiment of the present invention, the absolute value of the difference between the actual temperature value and the reference temperature value in the protective cover 2 is taken and then divided by the reference temperature value to obtain the temperature index; the difference between the actual humidity value and the reference humidity value in the protective cover 2 is taken and then divided by the reference humidity value to obtain the humidity index; the difference between the actual air pressure value and the reference air pressure value in the protective cover 2 is taken and then divided by the reference air pressure value to obtain the air pressure index.

[0098] The external environment impact model is as follows:

[0099] .

[0100] in , as well as These are the weighting coefficients for the effects of temperature, humidity, and air pressure, respectively. ,and , as well as All greater than ; Temperature index Humidity index Barometric pressure index This represents the external environmental impact coefficient.

[0101] The temperature index refers to the dimensionless ratio of the relative deviations between the actual temperature and the reference temperature. It can be calculated by collecting real-time data from a temperature sensor and combining it with a preset reference value, and is used to characterize the degree of interference of temperature fluctuations on the measurement system. The humidity index refers to the dimensionless ratio of the relative deviations between the actual humidity and the reference humidity. It can be calculated by comparing humidity sensor data with standard humidity, and is used to quantify the impact of humidity changes on the refractive index of optical devices. The barometric pressure index refers to the dimensionless ratio of the relative deviations between the actual air pressure and the reference air pressure. It can be calculated by processing the difference between data acquired by a barometric pressure sensor and a reference air pressure, and is used to reflect the interference of air pressure fluctuations on the laser propagation path. The weighting coefficient is a dynamic adjustment parameter for the influence of temperature, humidity, and air pressure on the measurement results. It can be implemented using regression analysis based on historical data or expert experience, and is used to adjust the contribution ratio of each factor according to changes in environmental conditions.

[0102] Specifically, by normalizing the actual measured values ​​of temperature, humidity, and air pressure against preset reference values, the interference of different physical dimensions on the evaluation model is eliminated, transforming the temperature, humidity, and air pressure indices into dimensionless relative deviation ratios. Subsequently, each index is linearly combined with its corresponding weighting coefficient, with the sum of the weighting coefficients being 1, ensuring that the external environmental influence coefficient remains within a comparable numerical range. This model, by dynamically adjusting the weighting coefficients, can adapt to situations where temperature, humidity, or air pressure becomes the dominant interference factor in different environmental scenarios. For example, in high-temperature and high-humidity environments, the humidity weighting coefficient can be set to a higher value to amplify the contribution of humidity changes to measurement error. The resulting external environmental influence coefficient serves as a unified quantitative indicator and can be directly used for dynamic correction of the accuracy of subsequent measurement results.

[0103] Compared with existing technologies, traditional methods only compensate for single environmental parameters independently, without establishing a comprehensive interference model for multi-dimensional environmental factors. In existing technologies, temperature compensation modules and humidity control devices often operate independently, resulting in the lack of quantitative evaluation of the coupling effect of environmental parameters. This solution integrates the synergistic interference effects of temperature, humidity, and air pressure into a single evaluation coefficient through normalized exponential transformation and weight allocation mechanisms, thus solving the problem of the difficulty in analyzing the cross-influence of multiple environmental parameters.

[0104] Through the above technical solution, this application achieves a comprehensive quantitative assessment of interference from changes in external environmental temperature, humidity, and air pressure, solving the problem of measurement reference drift caused by the coupling effect of environmental parameters in traditional devices. By transforming multidimensional environmental interference into a linear model with dynamically adjustable weights, a data foundation is provided for real-time calibration of measurement results, improving the anti-interference capability of vibration frequency measurement during fiber optic sensor fatigue life testing.

[0105] In a preferred embodiment of the present invention, the absolute value of the difference between the standard vibration frequency value and the ideal vibration frequency value is taken, and then divided by the ideal vibration frequency value to obtain the vibration frequency index. The accuracy evaluation model for the measurement result is as follows:

[0106] ;

[0107] in For tolerance sensitivity, Greater than , The frequency index is the oscillation index. For accuracy coefficient, This represents the external environmental impact coefficient.

[0108] The standard vibration frequency value refers to the benchmark vibration frequency obtained by fusing real-time measurements from a piezoelectric sensor and a laser vibrometer. This is achieved using a weighted average algorithm combined with dynamically adjusted weighting coefficients based on environmental factors, eliminating measurement errors from a single sensor. The ideal vibration frequency value is the theoretical benchmark vibration frequency measured by a fiber optic sensor under interference-free conditions. This can be obtained through laboratory calibration or calculation using a theoretical model, serving as a reference for the accuracy of the measurement results. The vibration frequency index is a quantitative indicator of the relative deviation between the standard and ideal vibration frequency values. It is calculated as the ratio of the absolute difference to the ideal vibration frequency value, characterizing the degree of deviation between the measured and theoretical values. The external environmental influence coefficient is a quantitative value of the comprehensive interference caused by changes in temperature, humidity, and air pressure to the measurement system. It is obtained by fusing the indices of various environmental parameters using a linear weighted model, dynamically correcting the weighting of environmental factors on accuracy assessment. Tolerance sensitivity is a parameter adjusting the system's sensitivity to vibration frequency deviation. This is achieved through a preset exponential function decay rate parameter λ, used to adjust the response characteristics of the accuracy assessment model according to testing requirements. The accuracy coefficient refers to the real-time quantitative evaluation value of the reliability of the measurement results. It is specifically calculated by a nonlinear function that integrates the vibration frequency index and the external environmental influence coefficient, and is used to trigger the feedback adjustment of the environmental control module.

[0109] Specifically, by collecting vibration frequency data from piezoelectric sensors and laser vibrometers in real time, a standard vibration frequency value is generated through fusion calculation and compared with a preset ideal vibration frequency value. The vibration frequency index is calculated to quantify the deviation between the actual measured value and the theoretical benchmark. Simultaneously, an external environmental influence coefficient is calculated based on temperature, humidity, and air pressure parameters. These two parameters are input into an exponential function model, where the external environmental influence coefficient dynamically adjusts the weighting of environmental interference through a (1-K_e) term, and the tolerance sensitivity λ controls the model's sensitivity to vibration frequency deviation. When environmental interference increases, the external environmental influence coefficient increases, causing the (1-K_e) term to decrease. At this point, the decay rate of the accuracy coefficient A accelerates, prompting the system to prioritize the activation of the environmental control module for adjustment. When the vibration frequency index exceeds a threshold, the nonlinear decay characteristic of the exponential function can rapidly reduce the accuracy coefficient, triggering an alarm or pausing the testing process.

[0110] Compared to existing technologies, traditional methods measure vibration frequency using only a single sensor, lack a dynamic fusion mechanism for standard vibration frequency values, and lack quantitative assessment of environmental interference. Existing life assessment systems cannot distinguish whether measurement deviations originate from sensor errors or environmental interference, making error tracing difficult. This solution establishes a correlation model between the vibration frequency index and the external environmental influence coefficient, achieving accurate identification of the source of measurement deviations. Simultaneously, a tolerance sensitivity parameter enables the system to adaptively adjust the tolerance threshold for deviations.

[0111] Through the above technical solution, this application solves the problem of system error accumulation caused by fluctuations in environmental parameters, and realizes real-time quantitative evaluation of the accuracy of measurement results. By dynamically correcting the environmental interference weights and adaptively adjusting the tolerance threshold, the impact of temperature, humidity, and air pressure changes on life assessment is effectively reduced. The continuous output of the accuracy coefficient provides a precise feedback signal to the environmental control module, enabling the testing device to automatically optimize operating conditions, thereby improving the reliability of the fatigue limit life assessment of fiber optic sensors.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fiber optic sensor strain fatigue limit sensing life testing device, comprising a worktable (1), wherein the worktable (1) is provided with a protective cover (2), characterized in that, The workbench (1) is fixedly connected to a fixed frame (3), and the fixed frame (3) is fixedly connected to an equal strength beam (4). A vibrator (5) is fixedly installed at the end of the equal strength beam (4). A piezoelectric sensor body (6) is installed on one end face of the equal strength beam (4). The piezoelectric sensor body (6) is fixedly connected to the equal strength beam (4) through a limiting component (9). An optical fiber sensor body (7) can be fixedly connected to the equal strength beam (4) by screws, and the optical fiber sensor body (7) faces the piezoelectric sensor body (6). The workbench (1) is fixedly connected to a limiting component (9). A laser vibrometer body (8) is installed at the position of the limiting component (9) facing the equal strength beam (4). The workbench (1) also includes a data processing system, which includes: The data acquisition module is used to acquire the status information of the piezoelectric sensor body (6), the fiber optic sensor body (7) and the laser vibration meter body (8) as well as the external environment information. The piezoelectric sensor measurement accuracy impact assessment module can construct a piezoelectric sensor measurement accuracy impact assessment model based on the pre-tightening force of the limit component (9) on the piezoelectric sensor body (6), the magnetic field strength in the protective cover (2), and the noise intensity of the amplifier inside the piezoelectric sensor body (6), and output the piezoelectric sensor measurement accuracy impact assessment coefficient. The laser vibration meter measurement accuracy impact assessment module can construct a laser vibration meter measurement accuracy impact assessment model based on the dust concentration, light intensity and emission power of the laser vibration meter body (8) in the protective cover (2), and output the laser vibration meter measurement accuracy impact assessment coefficient; The weight allocation module can import the evaluation coefficients of the piezoelectric sensor measurement accuracy and the laser vibration meter measurement accuracy into the constructed weight allocation model, and obtain the piezoelectric sensor measurement weight coefficients and the laser vibration meter measurement weight coefficients. The piezoelectric sensor measurement weight coefficient, the laser vibration meter measurement weight coefficient, the piezoelectric sensor measurement real-time vibration frequency value, and the laser vibration meter measurement real-time vibration frequency value are imported into the constructed fusion position model, and the standard vibration frequency value is output. The environmental impact assessment module can construct an external environmental impact model based on external temperature, humidity, and atmospheric pressure information, and output the external environmental impact coefficients. The measurement result accuracy assessment module can construct a measurement result accuracy assessment model based on the standard vibration frequency value and the output environmental influence coefficient, and output the measurement result accuracy assessment coefficient. The protective cover (2) is equipped with a temperature controller, a humidity controller and a pressure controller, and the temperature controller, humidity controller and pressure controller are electrically connected to the data processing system.

2. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 1, characterized in that, The limiting component (9) includes a bolt (91) and a spring (92). The bolt (91) is threadedly connected to the equal strength beam (4), and the bolt (91) passes through the base of the piezoelectric sensor body (6). A spring (92) is provided between the bolt (91) and the base of the piezoelectric sensor body (6). The spring (92) applies a preload to the base of the piezoelectric sensor body (6), so that the piezoelectric sensor body (6) and the equal strength beam (4) are elastically pressed together.

3. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 1, characterized in that, The absolute value of the difference between the actual preload force of the limiting component (9) on the piezoelectric sensor body (6) and the optimal preload force is taken and then divided by the optimal preload force to obtain the preload force index. The actual magnetic field strength in the protective cover (2) is divided by the critical magnetic field strength to obtain the magnetic field strength index. The actual noise strength of the amplifier inside the piezoelectric sensor body (6) is divided by the critical noise strength of the amplifier to obtain the noise index. The evaluation model for the impact of piezoelectric sensor measurement accuracy is as follows: ； in The evaluation coefficient is used to assess the impact of piezoelectric sensor measurement accuracy. Preload index, The magnetic field strength index. Noise level; , as well as These are the weighting coefficients for the effects of preload, magnetic field strength, and noise, respectively. ,and , as well as All greater than .

4. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 3, characterized in that, The dust concentration index is obtained by dividing the actual dust concentration in the protective cover (2) by the maximum allowable dust concentration in the protective cover (2). The light intensity index is obtained by taking the absolute value of the difference between the actual light intensity and the optimal light intensity in the protective cover (2) and dividing it by the optimal light intensity. The power index of the laser vibration meter body (8) is obtained by taking the absolute value of the difference between the real-time emission power of the laser vibration meter body (8) and the optimal emission power of the laser vibration meter body (8) and dividing it by the optimal emission power of the laser vibration meter body (8). The evaluation model for the impact of laser vibrometer measurement accuracy is as follows: ； in The evaluation coefficient for the impact of laser vibrometer measurement accuracy. , as well as These are the weighting coefficients for the effects of dust concentration, light intensity, and emission power, respectively. ,and , as well as All greater than ; The dust concentration index. Light intensity index, The power index of the laser vibration meter body (8) is given.

5. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 4, characterized in that, The weight allocation model is as follows: ， ； in The evaluation coefficient is used to assess the impact of piezoelectric sensor measurement accuracy. The evaluation coefficient for the impact of laser vibrometer measurement accuracy. For measuring the weighting coefficients of piezoelectric sensors, The weighting coefficients are measured for the laser vibrometer.

6. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 5, characterized in that, The standard vibration frequency value is: ； in This represents the real-time vibration frequency value of the piezoelectric sensor. This represents the real-time vibration frequency value of the laser vibrometer. The standard frequency value, For measuring the weighting coefficients of piezoelectric sensors, The weighting coefficients are measured for the laser vibrometer.

7. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 6, characterized in that, The absolute value of the difference between the actual temperature value and the reference temperature value in the protective cover (2) is taken and then divided by the reference temperature value to obtain the temperature index; the difference between the actual humidity value and the reference humidity value in the protective cover (2) is taken and then divided by the reference humidity value to obtain the humidity index; the difference between the actual air pressure value and the reference air pressure value in the protective cover (2) is taken and then divided by the reference air pressure value to obtain the air pressure index. The external environment impact model is as follows: 。 in , as well as These are the weighting coefficients for the effects of temperature, humidity, and air pressure, respectively. ,and , as well as All greater than ; Temperature index Humidity index Barometric pressure index This represents the external environmental impact coefficient.

8. The fiber optic sensor strain fatigue limit sensing life testing device according to claim 7, characterized in that, The absolute value of the difference between the standard vibration frequency value and the ideal vibration frequency value is taken, and then divided by the ideal vibration frequency value to obtain the vibration frequency index. The accuracy evaluation model for the measurement results is as follows: ； in For tolerance sensitivity, Greater than , The frequency index is the oscillation index. For accuracy coefficient, This represents the external environmental impact coefficient.

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