Multi-channel bridge measuring device based on sensor

Abstract

The invention discloses a sensor-based multi-channel bridge measuring device, which comprises a signal acquisition module, a signal conditioning module, a data processing module and a communication interface module, and is characterized in that the signal acquisition module is composed of 8-32 independent bridge circuits, each bridge circuit is connected with a resistance strain type sensor, and a full-bridge or half-bridge structure is adopted; the signal conditioning module comprises a multiplexer, an instrument amplifier and a filter circuit; and the multiplexer adopts a low-on-resistance analog switch. According to the multi-channel bridge measuring device based on the sensor, through multi-channel synchronous measurement (8-32 channels can be selected), the problem that a traditional device is low in single-point measurement efficiency is solved, the detection efficiency is improved, and through multiple anti-interference design (optoelectronic isolation, shielding and a filtering algorithm), the detection efficiency is improved. The problem of industrial field noise interference is solved, and measurement precision is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of sensor measurement technology, and in particular to a sensor-based multi-channel bridge measurement device. Background Technology

[0002] Existing bridge measurement devices typically only have single-channel measurement capabilities, with 1-4 channels and a sampling frequency not exceeding 1kHz, making it difficult to meet the needs of multi-point distributed measurement scenarios. In industrial field applications, traditional measurement devices suffer from problems such as complex wiring (requiring separate signal cables), poor anti-interference capabilities (common-mode rejection ratio typically below 60dB), and unstable measurement accuracy (temperature drift coefficient > 0.1%FS / ℃). Furthermore, they struggle to achieve synchronous acquisition and real-time processing of multiple signals, failing to meet the requirements of high-precision dynamic testing.

[0003] Therefore, it is necessary to provide a new sensor-based multi-channel bridge measurement device to solve the above-mentioned technical problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a multi-channel bridge measurement device based on a resistance strain gauge sensor, applicable to fields such as industrial automation inspection, structural health monitoring, and intelligent equipment condition diagnosis.

[0005] The present invention provides a sensor-based multi-channel bridge measurement device, comprising a signal acquisition module, a signal conditioning module, a data processing module, and a communication interface module; The signal acquisition module consists of 8-32 independent bridge circuits, each bridge circuit is connected to a resistance strain gauge sensor, and adopts a full-bridge or half-bridge structure. The signal conditioning module includes a multiplexer, an instrumentation amplifier, and a filter circuit; The data processing module uses the STM32H743 microprocessor as its core and integrates the ADS1248 chip as the front-end acquisition part. The communication interface module supports adaptive communication via RS485, CAN bus, and Ethernet interface.

[0006] A measurement method for a sensor-based multi-channel bridge measurement device employs a moving average filtering algorithm, a wavelet denoising algorithm, an adaptive filtering algorithm, and a Kalman filtering algorithm to improve the signal-to-noise ratio.

[0007] Preferably, the moving average filtering algorithm is based on the time-domain averaging principle, smoothing noise by calculating the arithmetic mean of N consecutive sampling points, as shown in the formula:

[0008] Where N is the window size, adjustable from 16 to 256 points.

[0009] Preferably, the wavelet denoising algorithm is based on the principle of multi-resolution analysis. It decomposes the signal into different frequency bands through wavelet decomposition, and reconstructs the signal after applying a threshold to the high-frequency noise components. The Daubechies wavelet basis db4 is used, with 3-5 decomposition layers. The soft thresholding function is: ={

[0010] The threshold λ = σ√(2logN), where σ is the noise standard deviation and N is the signal length.

[0011] Preferably, the adaptive filtering algorithm employs a 16th-order LMS adaptive filter, which iteratively adjusts the weight coefficients to counteract periodic interference. The weight coefficient update formula is as follows: w(n+1)=w(n)+2μe(n)x(n) Where μ is the convergence factor, which is dynamically adjustable from 0.02 to 0.1, and e(n) = d(n) - y(n) is the error signal.

[0012] Preferably, the Kalman filter algorithm is a recursive estimation method based on a state-space model, which is suitable for dynamic measurement scenarios; The state vector is set to three dimensions: X = [displacement, velocity, acceleration]. T The discrete state equation is X k =AX k-1 +BU k + k The observation equation is Z k =HX k +v k； The process noise covariance Q = diag([0.01, 0.01, 0.001]), and the measurement noise covariance R = 0.1; An integrated piecewise linear temperature compensation algorithm is used to establish a compensation model through calibration at three points: -40℃, 25℃, and 85℃. y T =y0+k1(T-T0)+k2(T-T_0) 2 k1 and k2 are temperature coefficients with a compensation accuracy of ±0.05%FS.

[0013] Compared with related technologies, the sensor-based multi-channel bridge measurement device provided by the present invention has the following advantages: This invention provides a sensor-based multi-channel bridge measurement device that enables simultaneous measurement across multiple channels (8-32 channels selectable), solving the problem of low efficiency in single-point measurement of traditional devices and improving detection efficiency. Multiple anti-interference designs (optical isolation + shielding + filtering algorithm) solve the problem of noise interference in industrial environments and ensure measurement accuracy. The modular architecture design supports flexible expansion of the number of channels, solves the problem of difficult maintenance of traditional devices, and facilitates future upgrades. Multi-protocol communication interfaces (RS485 / CAN / Ethernet) solve compatibility issues in different industrial scenarios and adapt to complex application environments; The fully automatic self-testing and calibration function solves the problem of tedious manual calibration and reduces human error. Attached Figure Description

[0014] Figure 1 A schematic diagram of the sensor-based multi-channel bridge measurement device provided by the present invention; Figure 2 The circuit schematic diagram of the signal conditioning module provided by this invention; Figure 3 The data processing flowchart provided for this invention. Detailed Implementation

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] In the specific implementation process, such as Figures 1-3 As shown, a sensor-based multi-channel bridge measurement device includes a signal acquisition module, a signal conditioning module, a data processing module, and a communication interface module.

[0017] The signal acquisition module consists of 8-32 independent bridge circuits, each connected to a resistance strain gauge sensor. It adopts a full-bridge or half-bridge structure (supporting switching between 1 / 4-bridge, half-bridge, and full-bridge operating modes) and can simultaneously acquire multiple sensor signals. The bridge circuits are isolated from each other through a metal shield and opto-isolation circuit, with an isolation voltage ≥2500V, effectively reducing signal crosstalk (crosstalk attenuation >80dB@1kHz). It has a built-in constant current source excitation circuit with an excitation current accuracy of ±0.1% and a temperature coefficient <50ppm / ℃.

[0018] The signal conditioning module includes a multiplexer, an instrumentation amplifier, and a filter circuit. The multiplexer uses a low on-resistance analog switch (on-resistance < 50mΩ) to switch the signals of each channel according to a set timing sequence, with a switching time < 1μs. The instrumentation amplifier uses an INA128 chip with a common-mode rejection ratio > 100dB (50Hz), an input offset voltage < 50μV, and an adjustable gain range of 1-1000 times. The filter circuit uses a 4th-order Butterworth active filter with a continuously adjustable cutoff frequency of 1Hz-10kHz, an in-band ripple of < 0.5dB, and a stopband attenuation of > 60dB / decade, effectively removing high-frequency interference signals.

[0019] The data processing module uses the STM32H743 microprocessor as its core and integrates the ADS1248 chip as the front-end acquisition section. This chip is a 24-bit high-precision ΔΣADC, supporting 8 differential input channels. It has a built-in programmable gain amplifier (PGA, gain range 1-128x) and a low-noise instrumentation amplifier, a sampling bit depth of 24 bits, a maximum conversion rate of 2kSPS (single channel) / 1kSPS (8-channel synchronous), and an integral nonlinearity error of ±0.0015%FS. Multiple algorithms are used to improve the signal-to-noise ratio. The principles and implementation methods of each algorithm are as follows: Moving average filtering: Based on the principle of time-domain averaging, it smooths noise by calculating the arithmetic mean of N consecutive sampling points. The formula is:

[0020] Where N is the window size, adjustable from 16 to 256 points.

[0021] In this device, the window is dynamically switched by real-time monitoring of the signal change rate (|xn-xn-1|): when the change rate is < threshold (0.01mV / s), it is determined to be a static signal (N=256) with a noise suppression ratio >30dB; when the change rate is ≥ threshold, it switches to dynamic mode (N=16) with a phase delay <1 / 8 of the sampling period.

[0022] Wavelet noise reduction algorithm: Based on the principle of multi-resolution analysis, the signal is decomposed into different frequency bands through wavelet decomposition. After applying a threshold to the high-frequency noise components, the signal is reconstructed. The Daubechies wavelet basis db4 is used, and the number of decomposition layers is 3-5 (automatically selected according to the signal bandwidth). The soft threshold function is: ={

[0023] The threshold λ = σ√(2logN), where σ is the noise standard deviation and N is the signal length. In strain signal processing, when this algorithm is applied to frequency bands above 500Hz, the non-stationary interference suppression ratio is >40dB.

[0024] Adaptive filtering algorithm: A 16th-order LMS (Least Mean Square) adaptive filter is used to offset periodic interference by iteratively adjusting the weight coefficients. The weight coefficient update formula is w(n+1)=w(n)+2μe(n)x(n), where μ is the convergence factor (dynamically adjustable from 0.02 to 0.1), and e(n)=d(n)-y(n) is the error signal. The device collects reference noise (such as 50Hz power frequency interference) through a dedicated detection channel. After adaptive filtering, the interference cancellation rate is >95%, and the signal-to-noise ratio is improved by 15-20dB.

[0025] Kalman filter algorithm: a recursive estimation method based on a state-space model, suitable for dynamic measurement scenarios; The state vector is set to three dimensions: X = [displacement, velocity, acceleration]. T The discrete state equation is X k =AX k-1 +BU k + k The observation equation is Z k =HX k +v k ; The process noise covariance Q=diag([0.01,0.01,0.001]), and the measurement noise covariance R=0.1; in the shock vibration test, the root mean square error of the signal is reduced by >20% after Kalman filtering, and the dynamic tracking accuracy is improved to ±0.05%FS.

[0026] Simultaneously, a piecewise linear temperature compensation algorithm is integrated, and a compensation model is established through calibration at three points: -40℃, 25℃, and 85℃. y T =y0+k1(T-T0)+k2(T-T_0) 2 k1 and k2 are temperature coefficients with a compensation accuracy of ±0.05%FS.

[0027] It supports multi-channel synchronous sampling, with an adjustable sampling frequency of 1Hz-100kHz, and a channel synchronization error of <1μs. It has a built-in 2MB Flash memory that can locally store 1 million sets of measurement data.

[0028] The communication interface module supports multiple communication methods, including RS485 (baud rate 1200bps-1Mbps, supports Modbus RTU protocol), CAN bus (compliant with ISO11898 standard, baud rate 50kbps-1Mbps), and Ethernet interface (10 / 100Mbps adaptive, supports TCP / IP and UDP protocols), enabling real-time transmission of measurement data. Each communication interface has 2500V electrical isolation protection and ESD protection level of ±8kV (contact discharge), improving system reliability.

[0029] This device also features comprehensive self-testing and calibration functions, which can periodically (adjustable from 1 minute to 24 hours) check the working status of each channel, including sensor disconnection detection, bridge impedance detection (detection range 50Ω-10kΩ), amplifier gain detection, etc.; it has a built-in high-precision reference voltage source (ADR4525, accuracy ±0.02%), which can automatically calibrate zero drift (drift compensation range ±10mV) and gain error (gain calibration accuracy ±0.01%) to ensure measurement accuracy (overall measurement accuracy ±0.05%FS).

[0030] This device adopts a modular design, with each functional module mechanically and electrically connected via a backplane connector. These modules operate independently yet collaboratively. In practical applications, the number of channels (8 / 16 / 24 / 32 channels selectable) and sensor types (strain gauges, pressure sensors, torque sensors, etc.) can be flexibly configured according to measurement requirements. Through the accompanying host computer software (supporting Windows 7 / 10 / 11 systems), it can achieve functions such as setting measurement parameters (sampling frequency, filtering parameters, compensation coefficients, etc.), real-time data acquisition (sampling rate up to 100kHz), data curve display (supporting dynamic waveforms, trend graphs, bar charts, etc.), data storage (supporting CSV, TXT, Excel formats), and report generation. The device operates in an ambient temperature range of -40℃ to 85℃, with a relative humidity of 10%-90%RH (non-condensing), and an IP65 protection rating.

[0031] The sensor-based multi-channel bridge measurement device of the present invention solves the problem of low efficiency of single-point measurement in traditional devices by multi-channel synchronous measurement (8-32 channels selectable), thereby improving detection efficiency.

[0032] The sensor-based multi-channel bridge measurement device of the present invention solves the problem of noise interference in industrial sites and ensures measurement accuracy through multiple anti-interference designs (photoelectric isolation + shielding + filtering algorithm).

[0033] The sensor-based multi-channel bridge measurement device of the present invention, through modular architecture design, supports flexible expansion of the number of channels, solves the problem of difficult maintenance of traditional devices, and facilitates future upgrades.

[0034] The sensor-based multi-channel bridge measurement device of the present invention solves the compatibility problem of different industrial scenarios and adapts to complex application environments through multi-protocol communication interfaces (RS485 / CAN / Ethernet). The fully automatic self-testing and calibration function solves the problem of tedious manual calibration and reduces human error.

[0035] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A sensor-based multi-channel bridge measurement device, characterized in that, It includes a signal acquisition module, a signal conditioning module, a data processing module, and a communication interface module; The signal acquisition module consists of 8-32 independent bridge circuits, each bridge circuit is connected to a resistance strain gauge sensor, and adopts a full-bridge or half-bridge structure. The signal conditioning module includes a multiplexer, an instrumentation amplifier, and a filter circuit; The data processing module uses the STM32H743 microprocessor as its core and integrates the ADS1248 chip as the front-end acquisition part. The communication interface module supports adaptive communication via RS485, CAN bus, and Ethernet interface.

2. A measurement method based on the sensor-based multi-channel bridge measurement device as described in claim 1, characterized in that: The signal-to-noise ratio is improved by employing moving average filtering, wavelet denoising, adaptive filtering, and Kalman filtering algorithms.

3. The measurement method of a sensor-based multi-channel bridge measurement device according to claim 2, characterized in that: The moving average filtering algorithm is based on the principle of time-domain averaging. It smooths noise by calculating the arithmetic mean of N consecutive sampling points. The formula is as follows: Where N is the window size, adjustable from 16 to 256 points.

4. The measurement method of a sensor-based multi-channel bridge measurement device according to claim 2, characterized in that: The wavelet denoising algorithm described above is based on the principle of multi-resolution analysis. It decomposes the signal into different frequency bands using wavelet decomposition, and reconstructs the signal after applying a threshold to the high-frequency noise components. The algorithm uses the Daubechies wavelet basis db4, with 3-5 decomposition layers. The soft thresholding function is as follows: ={ The threshold λ = σ√(2logN), where σ is the noise standard deviation and N is the signal length.

5. The measurement method of a sensor-based multi-channel bridge measurement device according to claim 2, characterized in that: The adaptive filtering algorithm employs a 16th-order LMS adaptive filter, which iteratively adjusts the weight coefficients to counteract periodic interference. The weight coefficient update formula is as follows: w(n+1)=w(n)+2μe(n)x(n) Where μ is the convergence factor, which is dynamically adjustable from 0.02 to 0.1, and e(n) = d(n) - y(n) is the error signal.

6. The measurement method of a sensor-based multi-channel bridge measurement device according to claim 2, characterized in that: The Kalman filter algorithm is a recursive estimation method based on a state-space model, suitable for dynamic measurement scenarios. The state vector is set to three dimensions: X = [displacement, velocity, acceleration]. T The discrete state equation is X k =AX k-1 +BU k + k The observation equation is Z k =HX k +v k ; The process noise covariance Q = diag([0.01, 0.01, 0.001]), and the measurement noise covariance R = 0.1; An integrated piecewise linear temperature compensation algorithm is used to establish a compensation model through calibration at three points: -40℃, 25℃, and 85℃. y T =y0+k1(T-T0)+k2(T-T_0) 2 k1 and k2 are temperature coefficients with a compensation accuracy of ±0.05%FS.

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