Three-in-one composite sensor and equipment state monitoring method

Abstract

The invention discloses a three-in-one composite sensor and an equipment state monitoring method, and belongs to the technical field of sensors, the three-in-one composite sensor is used for monitoring the state of a bearing and comprises a metal shell, the metal shell is fixedly installed on an outer ring of the bearing, a cable is fixedly installed on the metal shell, and the cable is connected with the metal shell. A pair of temperature probes, a three-axis MEMS acceleration sensor and a piezoelectric ceramic impact pulse sensor are installed in the metal shell in an integrated mode. The three sensors are integrated in the metal shell to form a three-in-one composite sensor, so that the installation space and the installation complexity are remarkably reduced, and the problem of redundant structure of a traditional bearing monitoring system is solved; the early micron-sized damage of the bearing can be effectively identified by adopting the capturing sensitivity of the piezoelectric ceramic impact pulse sensor to a high-frequency impact signal and combining three-axis vibration analysis; through the symmetrically distributed temperature probes and a dynamic weighting algorithm, local heat source interference is eliminated, and the accuracy of temperature monitoring is improved.

Description

Technical Field

[0002] The present invention belongs to the technical field of sensors, and particularly relates to a three-in-one composite sensor and a method for monitoring the state of equipment. Background Art

[0003] For traditional bearing fault monitoring, vibration, temperature and impact pulse sensors need to be installed separately, resulting in structural redundancy, complex installation, and poor signal coordination of multiple sensors. Conventional vibration analysis is difficult to capture early micro-damage of bearings (such as fatigue spalling), while the impact pulse method relies on manual experience for judgment, with low automation. A single temperature monitoring point is vulnerable to environmental interference, and the threshold is not dynamically corrected in combination with vibration signals, resulting in a high false alarm rate and making it difficult to accurately and reliably monitor the bearing state.

[0004] Therefore, in view of the above technical problems, it is necessary to provide a three-in-one composite sensor and a method for monitoring the state of equipment.

[0005] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of implication that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a three-in-one composite sensor and a method for monitoring the state of equipment, which can solve the above-mentioned technical problems.

[0007] To achieve the above purpose, the technical solution provided by a specific embodiment of the present invention is as follows: A three-in-one composite sensor for monitoring the state of a bearing, including a metal housing fixedly installed on the outer ring of the bearing, a cable fixedly installed on the metal housing, and a pair of temperature probes, a triaxial MEMS acceleration sensor and a piezoelectric ceramic impact pulse sensor integrally installed inside the metal housing; The piezoelectric ceramic impact pulse sensor is in contact with the outer ring of the bearing, and the signal conditioning circuit of the piezoelectric ceramic impact pulse sensor is built-in with a band-pass filter and a preamplifier; A non-magnetic isolation layer is provided between the metal housing and the outer ring of the bearing, and the non-magnetic isolation layer is a titanium alloy collar; A pair of the temperature probes are distributed on both sides of the outer ring of the bearing and are connected to a dynamic weighting algorithm module for eliminating local heat source interference.

[0008] In one or more embodiments of the present invention, the contact surface of the piezoelectric ceramic impact pulse sensor with the outer ring of the bearing is provided with a serrated microstructure; The signal conditioning circuit integrates an AD8605 operational amplifier.

[0009] In one or more embodiments of the present invention, the sampling rate of the triaxial MEMS acceleration sensor is ≥10 kHz, and the frequency response range is from 0.5 Hz to 8 kHz; The temperature measurement range of a pair of the temperature probes is -40°C to 200°C.

[0010] In one or more embodiments of the present invention, the thickness of the non-magnetic isolation layer is 1 to 3 mm, and its inner surface is coated with an alumina insulating coating, and the thickness of the alumina insulating coating is 50 to 100 μm.

[0011] In one or more embodiments of the present invention, a device status monitoring method includes the following steps: S1: Synchronously collect vibration signals, shock wave signals, and temperature signals; The vibration signals obtain acceleration data in the X / Y / Z directions through a triaxial MEMS acceleration sensor; The shock wave signals extract high-frequency impact pulse components through a band-pass filter; The temperature signals output a dynamic temperature curve through mean filtering by a pair of temperature probes; S2: Perform envelope detection and fault frequency matching on the shock wave signals; S3: Generate an alarm instruction by combining the vibration spectrum analysis result and the temperature dynamic threshold; S4: Predict the bearing fault level based on the shock pulse energy trend.

[0012] In one or more embodiments of the present invention, the specific steps of S2 include: S2.1: Perform Hilbert transform on the shock wave signals to generate envelope signals and extract the local maximum value sequence; S2.2: Set a dynamic threshold according to the lower decile of the peak sequence to filter out environmental noise interference; S2.3: Calculate the peak frequency and match it with the inner ring fault characteristic frequency, outer ring fault characteristic frequency, and rolling element fault characteristic frequency of the bearing to determine the type of the faulty component.

[0013] In one or more embodiments of the present invention, the specific steps of S3 include: S3.1: Perform FFT analysis on the vibration signals and extract the amplitudes corresponding to the fault characteristic frequencies; S3.2: When the amplitude of the vibration signal characteristic frequency exceeds 20% of the baseline value, trigger the temperature threshold dynamic adjustment mechanism and reduce the temperature alarm threshold by 5% to 15%; S3.3: If the dynamic temperature curve exceeds the adjusted threshold three times in a row, generate a first-level alarm signal.

[0014] In one or more embodiments of the present invention, the specific steps of S4 include: S4.1: Calculate the impact pulse energy sequence according to a time window, where the length of the time window is adjustable from 1 to 24 hours; S4.2: Use the sliding window mean algorithm to eliminate short-term fluctuations, and the window length is 1 / 10 of the total data length; S4.3: Define the degradation index DI = current window energy / historical baseline energy. When DI > 1.5 for three consecutive times, an early warning is triggered, and when DI > 2.0 for five consecutive times, an emergency shutdown instruction is triggered.

[0015] In one or more embodiments of the present invention, the historical baseline energy is the average value of the impact pulse energy in the thirty days before the normal operation of the device, and the baseline value is automatically updated every thirty days.

[0016] In one or more embodiments of the present invention, the device status monitoring method is deployed on an edge computing terminal. The terminal is built with an adaptive sampling rate module. When the degradation index DI > 1.2, the sampling rate of the shock wave signal is increased from 10 kHz to 20 kHz, and full-band signal analysis is enabled.

[0017] Compared with the prior art, in the present invention, by integrating three types of sensors into a metal shell to form a three-in-one composite sensor, the installation space and installation complexity are significantly reduced, and the problem of structural redundancy in the traditional bearing monitoring system is solved; the piezoelectric ceramic impact pulse sensor is used for the capture sensitivity of high-frequency impact signals, combined with three-axis vibration analysis, which can effectively identify early micron-level damage of bearings; through the symmetrically distributed temperature probe and dynamic weighting algorithm, the interference of local heat sources is eliminated, and the accuracy of temperature monitoring is improved; the design of the non-magnetic isolation layer effectively avoids the interference of the magnetic steel on the signal and enhances the anti-interference ability of the system in a complex industrial environment; the mechanism-based fault diagnosis method does not require a large number of training samples, is suitable for deployment on edge computing devices, and reduces the computational resource requirements; through the collaborative analysis of vibration signals and temperature signals, dynamic temperature threshold adjustment is realized, and the false alarm rate is significantly reduced; the impact pulse energy trend analysis method realizes the early warning of bearing faults and provides sufficient preparation time for equipment maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a structural diagram of the three-in-one composite sensor in an embodiment of the present invention; Figure 2Cross-sectional view of the three-in-one composite sensor in an embodiment of the present invention; Figure 3 In an embodiment of the present invention, the three-in-one composite sensor Figure 2 Enlarged view of part A in; Figure 4 Top view of the three-in-one composite sensor in an embodiment of the present invention; Figure 5 Method flow chart of the device status monitoring method in an embodiment of the present invention.

[0020] Explanation of main reference numerals: 1. Bearing; 2. Metal housing; 3. Temperature probe; 4. Triaxial MEMS acceleration sensor; 5. Cable; 6. Piezoelectric ceramic shock pulse sensor; 601. Zigzag microstructure; 7. Non-magnetic isolation layer. Detailed implementation manners

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] As Figures 1 to 4 shown, a three-in-one composite sensor in an embodiment of the present invention is used for the status monitoring of a bearing. The three-in-one composite sensor includes a metal housing 2, which is fixedly installed on the outer ring of the bearing 1. A cable 5 is fixedly installed on the metal housing 2. Inside the metal housing 2, a pair of temperature probes 3, a triaxial MEMS acceleration sensor 4, and a piezoelectric ceramic shock pulse sensor 6 are integrally installed; the piezoelectric ceramic shock pulse sensor 6 is in contact with the outer ring of the bearing 1, and the signal conditioning circuit of the piezoelectric ceramic shock pulse sensor 6 is built-in with a band-pass filter and a preamplifier; a non-magnetic isolation layer 7 is provided between the metal housing 2 and the outer ring of the bearing 1, and the non-magnetic isolation layer 7 is a titanium alloy collar; the pair of temperature probes 3 are distributed on both sides of the outer ring of the bearing 1 and are connected to a dynamic weighting algorithm module to eliminate local heat source interference.

[0023] The metal housing 2 is made of aluminum alloy material, having good heat dissipation performance and mechanical strength. The metal housing 2 is generally cylindrical.

[0024] A pair of temperature probes 3 are PT100 platinum resistance temperature probes 3, with a temperature measurement range of -40°C to 200°C. The pair of temperature probes 3 are respectively installed on the left and right sides inside the metal housing 2, maintaining a distance of 2 mm from the outer ring of the bearing 1. The signals of the temperature probes 3 are transmitted to the signal conditioning circuit through shielded cables and then connected to the dynamic weighting algorithm module.

[0025] The dynamic weighting algorithm module is implemented using a 32-bit ARM processor, with a working frequency of 120 MHz, and built-in 4 MB of flash memory and 2 MB of RAM. This module identifies and eliminates local heat source interference by comparing the reading differences of the two temperature probes 3. When the reading difference between the two temperature probes 3 exceeds 5°C, the algorithm automatically adjusts the weighting coefficients, reducing the weight of the interfered sensor and increasing the weight of the other sensor to ensure the accuracy of temperature monitoring.

[0026] The triaxial MEMS acceleration sensor 4 is manufactured using MEMS technology, with a sampling rate of 10 kHz and a frequency response range of 0.5 Hz to 8 kHz. The triaxial MEMS acceleration sensor 4 can simultaneously monitor vibration signals in the X, Y, and Z directions, comprehensively capturing the vibration characteristics of the bearing 1.

[0027] The piezoelectric ceramic impact pulse sensor 6 is in contact with the outer ring of the bearing 1. The contact surface of the piezoelectric ceramic impact pulse sensor 6 with the outer ring of the bearing 1 is provided with a serrated microstructure 601. The serrated microstructure 601 is made of a silicon-based material, which can effectively improve the acoustic wave transmission efficiency and reduce signal attenuation. The piezoelectric ceramic impact pulse sensor 6 maintains stable contact with the outer ring of the bearing 1, with a contact pressure of 5 N.

[0028] The signal conditioning circuit of the piezoelectric ceramic impact pulse sensor 6 is built-in with a band-pass filter and a preamplifier. The passband of the band-pass filter is 100 kHz to 500 kHz, and the stopband attenuation is 40 dB / decade, which is used to filter out low-frequency vibration noise and high-frequency electromagnetic interference. The preamplifier adopts a low-noise design, with a signal-to-noise ratio greater than 60 dB, and an adjustable gain range of 20 dB to 60 dB. The signal conditioning circuit integrates an AD8605 operational amplifier, which has the characteristics of low noise, low distortion, and high bandwidth, meeting the processing requirements of high-frequency acoustic emission signals.

[0029] A non-magnetic isolation layer 7 is provided between the metal housing 2 and the outer ring of the bearing 1. The non-magnetic isolation layer 7 is a titanium alloy collar. The thickness of the titanium alloy collar is 2 mm, the inner diameter matches the outer diameter of the bearing 1, and the outer diameter matches the inner diameter of the metal housing 2. The titanium alloy collar can effectively isolate the heat generated by the bearing 1 and prevent the heat from being directly conducted to the metal housing 2, which affects the measurement accuracy of the sensor. The inner surface of the titanium alloy collar is coated with an alumina insulating coating, and the thickness of the alumina insulating coating is 75 μm. The alumina insulating coating can effectively prevent the current path between the bearing 1 and the sensor and avoid the interference of the current on the sensor.

[0030] The working principle of the three-in-one composite sensor is as follows: When the bearing 1 operates, the three-axis MEMS acceleration sensor 4 captures the vibration signal of the bearing, the piezoelectric ceramic impact pulse sensor 6 captures the high-frequency acoustic wave signal inside the bearing, and a pair of temperature probes 3 monitors the temperature change of the bearing. After these three signals are processed by their respective signal conditioning circuits, they are transmitted to the data acquisition system. The data acquisition system analyzes and processes these signals to achieve comprehensive monitoring of the bearing status.

[0031] As Figure 5 shown, this embodiment provides a device status monitoring method, which uses the three-in-one composite sensor described in Embodiment 1. The method includes the following steps: S1: Synchronously collect vibration signals, shock wave signals, and temperature signals; The vibration signal obtains the acceleration data in the X / Y / Z directions through the three-axis MEMS acceleration sensor 4; The shock wave signal extracts the high-frequency impact pulse component through a band-pass filter; The temperature signal outputs a dynamic temperature curve through mean filtering by a pair of temperature probes 3; S2: Perform envelope detection and fault frequency matching on the shock wave signal; S3: Generate an alarm instruction by combining the vibration spectrum analysis result and the temperature dynamic threshold; S4: Predict the bearing fault level based on the trend of the impact pulse energy.

[0032] In step S1, the three-in-one composite sensor simultaneously collects the vibration signal, shock wave signal, and temperature signal of the bearing. The vibration signal obtains the acceleration data in the X / Y / Z three directions through the three-axis MEMS acceleration sensor 4, the sampling rate is 10 kHz, and the data resolution is 16 bits. The collected original vibration data is processed by an anti-aliasing filter, and the cut-off frequency is set to 40% of the sampling rate, that is, 4 kHz.

[0033] The shock wave signal is collected by the piezoelectric ceramic impact pulse sensor 6 at a sampling rate of 20 kHz and a data resolution of 16 bits. The collected shock wave signal is processed by a band-pass filter with a passband of 100 kHz to 500 kHz to extract the high-frequency impact pulse components. The band-pass filter is designed using a Butterworth filter with an order of 4 to ensure a flat frequency response characteristic within the passband and good attenuation characteristics in the stopband.

[0034] The temperature signal is collected by a pair of temperature probes 3 at a sampling rate of 1 Hz and a data resolution of 12 bits. The collected temperature data is processed by mean filtering with a filter window length of 10 sampling points, i.e., 10 seconds. Mean filtering can effectively eliminate random noise in temperature measurement and improve the stability of temperature measurement. The temperature data after mean filtering forms a dynamic temperature curve for subsequent temperature trend analysis and alarm judgment.

[0035] In step S2, envelope detection and fault frequency matching are performed on the shock wave signal. The specific steps include: S2.1: Perform Hilbert transform on the shock wave signal to generate an envelope signal and extract the local maximum sequence; S2.2: Set a dynamic threshold according to the lower decile of the peak sequence to filter out environmental noise interference; S2.3: Calculate the peak frequency and match it with the inner race fault characteristic frequency, outer race fault characteristic frequency, and rolling element fault characteristic frequency of the bearing to determine the type of faulty component.

[0036] In step S2.1, Hilbert transform is performed on the shock wave signal after band-pass filtering to generate an envelope signal. Hilbert transform can extract the instantaneous amplitude of the signal and effectively detect the modulation characteristics in the shock wave signal. Peak detection is performed on the envelope signal to extract the local maximum sequence. The peak detection uses the sliding window method with a window length of 100 sampling points. When the amplitude at the center point of the window is greater than the amplitudes of other points in the window, it is determined as a local maximum.

[0037] In step S2.2, a dynamic threshold is set according to the lower decile of the peak sequence to filter out environmental noise interference. First, calculate the statistical characteristics of the peak sequence, including the maximum value, minimum value, average value, standard deviation, and quantile. Then, take the lower decile of the peak sequence as the dynamic threshold, that is, sort the peak sequence in ascending order of amplitude and take the value at the 10% position as the threshold. Peaks below this threshold are regarded as environmental noise and filtered out. The dynamic threshold can adapt to changes in the environmental noise level and improve the reliability of fault detection.

[0038] In step S2.3, calculate the peak frequency and match it with the bearing fault characteristic frequency to determine the type of faulty component. First, calculate the theoretical fault characteristic frequencies according to the geometric parameters and rotational speed of the bearing, including the inner race fault characteristic frequency, the outer race fault characteristic frequency, and the rolling element fault characteristic frequency. Then, perform spectral analysis on the filtered peak sequence to extract the main frequency components. Finally, match the extracted frequencies with the theoretical fault characteristic frequencies. When the matching degree exceeds 85%, it is determined that the corresponding component is faulty.

[0039] In step S3, generate an alarm instruction by combining the vibration spectral analysis result and the temperature dynamic threshold. The specific steps include: S3.1: Perform FFT analysis on the vibration signal to extract the amplitude corresponding to the fault characteristic frequency; S3.2: When the amplitude of the vibration signal characteristic frequency exceeds the baseline value by 20%, trigger the temperature threshold dynamic adjustment mechanism and reduce the temperature alarm threshold by 5% - 15%; S3.3: If the dynamic temperature curve exceeds the adjusted threshold three times continuously, generate a first-level alarm signal.

[0040] In step S3.1, perform fast Fourier transform (FFT) analysis on the vibration signal collected by the three-axis MEMS acceleration sensor 4 to convert the time-domain signal into a frequency-domain signal. The FFT analysis uses a Hanning window function, the window length is 4096 points, and the overlap rate is 50%. Extract the amplitude corresponding to the fault characteristic frequency in the spectrum, including the fundamental frequency and harmonic components. The fundamental frequency refers to the frequency component that exactly matches the fault characteristic frequency, and the harmonic component refers to the frequency component that is an integer multiple of the fundamental frequency.

[0041] In step S3.2, when the amplitude of the vibration signal characteristic frequency exceeds the baseline value by 20%, trigger the temperature threshold dynamic adjustment mechanism. The baseline value refers to the vibration amplitude in the normal operating state of the device, which is obtained through long-term monitoring. When it is detected that the vibration amplitude increases abnormally, it indicates that the device may have a fault, and at this time, it is necessary to improve the sensitivity of temperature monitoring. The adjustment range of the temperature alarm threshold is proportional to the abnormal degree of the vibration amplitude. When the vibration amplitude exceeds the baseline value by 20% - 50%, the temperature alarm threshold is reduced by 5%; when the vibration amplitude exceeds the baseline value by 50% - 100%, the temperature alarm threshold is reduced by 10%; when the vibration amplitude exceeds the baseline value by more than 100%, the temperature alarm threshold is reduced by 15%.

[0042] In step S3.3, if the dynamic temperature curve exceeds the adjusted threshold three times continuously, generate a first-level alarm signal. The first-level alarm signal is displayed through the human-machine interface of the device control system and notified to the maintenance personnel by means of text messages, emails, etc. The first-level alarm indicates that the device is abnormal but has not reached the level of emergency shutdown. The maintenance personnel need to closely monitor the device status and arrange a maintenance plan.

[0043] In step S4, the bearing fault level is predicted based on the impact pulse energy trend. The specific steps are as follows: S4.1: Calculate the impact pulse energy sequence according to the time window, and the length of the time window is adjustable from 1 to 24 hours; S4.2: Use the moving window mean algorithm to eliminate short-term fluctuations, and the window length is 1 / 10 of the total data length; S4.3: Define the deterioration index DI = current window energy / historical baseline energy. When DI > 1.5 for three consecutive times, a warning is triggered, and when DI > 2.0 for five consecutive times, an emergency shutdown instruction is triggered.

[0044] In step S4.1, calculate the impact pulse energy sequence according to the time window. The length of the time window is adjusted according to the operating characteristics of the equipment and the monitoring requirements. For continuously operating equipment, the time window length is set to 4 hours; for intermittently operating equipment, the time window length is set to 1 hour. The impact pulse energy calculation method is: square the shock wave signals within the time window and sum them, and then divide by the number of sampling points to obtain the average energy value.

[0045] In step S4.2, use the moving window mean algorithm to eliminate short-term fluctuations. The moving window length is set to 1 / 10 of the total data length, that is, for 30 days of data, the moving window length is 3 days. The moving window mean algorithm can smooth the short-term fluctuations in the energy sequence, highlight the long-term change trend, and improve the reliability of fault prediction.

[0046] In step S4.3, define the deterioration index DI = current window energy / historical baseline energy. The historical baseline energy is the average value of the impact pulse energy in the thirty days before the equipment operates normally, and the baseline value is automatically updated every thirty days. When DI > 1.5 for three consecutive times, a warning is triggered, and the warning signal is displayed through the human-machine interface of the equipment control system and notified to the maintenance personnel by means of text messages, emails, etc. When DI > 2.0 for five consecutive times, an emergency shutdown instruction is triggered, and the emergency shutdown instruction is directly sent to the equipment control system, and the control system executes the safety shutdown procedure to prevent the equipment from suffering serious failures.

[0047] The device status monitoring method is deployed on an edge computing terminal. The terminal is built-in with an adaptive sampling rate module. When the deterioration index DI > 1.2, the sampling rate of the shock wave signal is increased from 10 kHz to 20 kHz, and full-band signal analysis is enabled. The edge computing terminal uses an industrial-grade computer with a quad-core processor, a main frequency of 3.0 GHz, 8 GB of memory, and a storage capacity of 256 GB. The adaptive sampling rate module can automatically adjust the sampling strategy according to the device status. When the device status is normal, a low sampling rate is used to reduce the data storage and processing burden. When a device status anomaly is detected, the sampling rate is increased to obtain more detailed signal characteristics and improve the accuracy of fault diagnosis. The full-band signal analysis includes time-domain analysis, frequency-domain analysis, time-frequency analysis, and envelope analysis, which can comprehensively capture the device fault characteristics and improve the accuracy and reliability of fault diagnosis.

[0048] In the present invention, by integrating three types of sensors into the metal housing 2 to form a three-in-one composite sensor, the installation space and installation complexity are significantly reduced, and the problem of structural redundancy in the traditional bearing 1 monitoring system is solved; the piezoelectric ceramic impact pulse sensor 6 is used to capture the high-frequency impact signal with high sensitivity, and combined with three-axis vibration analysis, it can effectively identify the early micron-level damage of the bearing; through the symmetrically distributed design of the temperature probe 3 and the dynamic weighting algorithm, the interference of local heat sources is eliminated, and the accuracy of temperature monitoring is improved; the design of the non-magnetic isolation layer 7 effectively avoids the interference of the magnetic steel on the signal and enhances the anti-interference ability of the system in a complex industrial environment; the mechanism-based fault diagnosis method does not require a large number of training samples and is suitable for deployment on edge computing devices, reducing the computational resource requirements; through the collaborative analysis of the vibration signal and the temperature signal, the dynamic temperature threshold is adjusted, significantly reducing the false alarm rate; the impact pulse energy trend analysis method realizes the early warning of bearing faults and provides sufficient preparation time for equipment maintenance.

[0049] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0050] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A three-in-one composite sensor for bearing condition monitoring, characterized in that, It includes a metal housing which is fixedly installed on the outer ring of the bearing. A cable is fixedly installed on the metal housing. Inside the metal housing, a pair of temperature probes, a three-axis MEMS acceleration sensor and a piezoelectric ceramic shock pulse sensor are integrally installed. The piezoelectric ceramic shock pulse sensor is in contact with the outer ring of the bearing. The signal conditioning circuit of the piezoelectric ceramic shock pulse sensor incorporates a band-pass filter and a preamplifier. A non-magnetic isolation layer is provided between the metal housing and the outer ring of the bearing. The non-magnetic isolation layer is a titanium alloy collar. A pair of the temperature probes are distributed on both sides of the outer ring of the bearing and are connected to a dynamic weighted algorithm module for eliminating local heat source interference.

2. The triple composite sensor according to claim 1, wherein The contact surface of the piezoelectric ceramic shock pulse sensor with the outer ring of the bearing is provided with a serrated micro-structure. The signal conditioning circuit integrates an AD8605 operational amplifier.

3. The three-in-one composite sensor according to claim 1, characterized in that The sampling rate of the three-axis MEMS acceleration sensor is ≥10 kHz, and the frequency response range is from 0.5 Hz to 8 kHz. The temperature measurement range of a pair of the temperature probes is -40°C to 200°C.

4. The triple composite sensor according to any one of claims 1 to 3, characterized in that The thickness of the non-magnetic isolation layer is 1 to 3 mm, and its inner surface is coated with an alumina insulating coating with a thickness of 50 to 100 μm.

5. A device status monitoring method, which uses the triple composite sensor as described in any one of claims 1 to 4, characterized in that It includes the following steps: S1: Synchronously collect vibration signals, shock wave signals and temperature signals. The vibration signals obtain acceleration data in the X / Y / Z directions through the three-axis MEMS acceleration sensor. The shock wave signals extract high-frequency impact pulse components through a band-pass filter. The temperature signals output a dynamic temperature curve after mean filtering by a pair of temperature probes. S2: Perform envelope detection and fault frequency matching on the shock wave signals. S3: Generate an alarm instruction by combining the vibration spectrum analysis results and the temperature dynamic threshold. S4: Predict the bearing fault level based on the trend of the impact pulse energy.

6. The device status monitoring method according to claim 5, characterized in that, The specific steps of S2 include: S2.1: Perform Hilbert transform on the shock wave signals to generate envelope signals and extract the local maximum value sequence. S2.2: Set a dynamic threshold according to the lower decile of the peak sequence to filter out environmental noise interference. S2.3: Calculate the peak frequency and match it with the inner ring fault characteristic frequency, outer ring fault characteristic frequency and rolling element fault characteristic frequency of the bearing to determine the type of the faulty component.

7. The device status monitoring method according to claim 5, characterized in that, The specific steps of S3 include: S3.1: Perform FFT analysis on the vibration signals to extract the amplitude corresponding to the fault characteristic frequency. S3.2: When the amplitude of the vibration signal characteristic frequency exceeds 20% of the baseline value, trigger the temperature threshold dynamic adjustment mechanism and reduce the temperature alarm threshold by 5% - 15%. S3.3: If the dynamic temperature curve exceeds the adjusted threshold three times continuously, generate a first-level alarm signal.

8. The device status monitoring method according to claim 5, wherein The specific steps of S4 include: S4.1: Calculate the impact pulse energy sequence according to a time window, and the length of the time window is adjustable from 1 to 24 hours. S4.2: Use the sliding window mean algorithm to eliminate short-term fluctuations, and the window length is 1 / 10 of the total data length. S4.3: Define the deterioration index DI = current window energy / historical baseline energy. When DI > 1.5 continuously for 3 times, trigger a warning, and when DI > 2.0 continuously for 5 times, trigger an emergency shutdown instruction.

9. The device status monitoring method according to any one of claims 5 to 8, characterized in that, The historical baseline energy is the average value of the impact pulse energy in the thirty days before the normal operation of the device, and the baseline value is automatically updated every thirty days.

10. The device status monitoring method according to claim 5, wherein The device status monitoring method is deployed on the edge computing terminal. The terminal is built with an adaptive sampling rate module. When the deterioration index DI > 1.2, the sampling rate of the shock wave signal is increased from 10 kHz to 20 kHz, and the full-band signal analysis is enabled.

Images