Fault detection method, system and equipment for converter tilting device and storage medium
By collecting multi-source sensor signals and rotation speed signals at multiple locations of the converter tilting device, performing signal processing and demodulation, and generating analysis maps, the problem of misdiagnosis of the converter tilting device in complex noise environments was solved, and high-precision and early fault detection was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YIZHONG GRP (HEILONGJIANG) HEAVY IND CO LTD
- Filing Date
- 2025-12-10
- Publication Date
- 2026-04-21
AI Technical Summary
In complex background noise environments, converter tilting devices have difficulty effectively extracting fault signals, leading to misdiagnosis or missed diagnosis, and the accuracy and timeliness of fault detection are low.
By acquiring multi-source sensor signals and speed reference signals at multiple preset locations, noise filtering, analog-to-digital conversion, signal feature extraction, and resonance demodulation are performed to generate an equipment operation status analysis map, and fault detection is performed by combining frequency domain and time domain feature parameters.
It improves the accuracy and timeliness of fault detection, enabling rapid identification of fault signals in complex signal environments, reducing false diagnoses, achieving early warning, and improving equipment operation stability and maintenance efficiency.
Smart Images
Figure CN121901607A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of metallurgical equipment fault diagnosis technology, and more specifically, to a fault detection method, system, equipment, and storage medium for a converter tilting device. Background Technology
[0002] The converter is the core equipment in steelmaking. The converter tilting device, as the power source for the converter's rotation, consists of a motor, brake, primary reducer, secondary reducer, torsion bar device, and lubrication system. Due to the high operating temperature and strong enclosure of the converter tilting device, coupled with its low speed and large load, real-time fault detection of the converter tilting device is necessary.
[0003] In related technologies, the converter tilting device operates at low speed and under heavy load, generating complex background noise during operation. This makes it difficult to extract effective fault signals from the complex background noise, easily leading to misdiagnosis or missed diagnosis. Furthermore, often when a strong fault signal is detected, the converter tilting device is already in the late stage of the fault, affecting not only the accuracy of fault detection but also its real-time performance. Summary of the Invention
[0004] The problem addressed by this invention is how to improve the accuracy and timeliness of fault detection in converter tilting devices.
[0005] To address the aforementioned problems, this invention provides a method, system, equipment, and storage medium for detecting faults in a converter tilting device.
[0006] In a first aspect, the present invention provides a method for detecting faults in a converter tilting device, comprising: Acquire multi-source sensor signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device; The multi-source sensing signals are sequentially subjected to noise filtering and analog-to-digital conversion to obtain the digital sensing signals corresponding to the multi-source sensing signals; Based on the digital sensing signal, signal features are extracted by combining the speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signal; The digital sensing signal is resonantly demodulated to obtain a demodulated signal characterizing the fault features; Based on the demodulated signal at each preset position, and combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position, an equipment operation status analysis map of the converter tilting device is obtained; The converter tilting device is used for fault detection based on the equipment operation status analysis chart.
[0007] Optionally, acquiring multi-source sensor signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device includes: The impact pulse signal and vibration signal at the preset position are obtained by a composite sensor set at the preset position. The impact pulse signal and the vibration signal are used as the multi-source sensing signals; The rotational speed signal of the drive mechanism is obtained by a rotational speed sensor installed in the drive mechanism of the converter tilting device; The rotational speed signal is used as the rotational speed reference signal for the converter tilting device.
[0008] Optionally, the step of sequentially performing noise filtering and analog-to-digital conversion on the multi-source sensing signals to obtain the digital sensing signals corresponding to the multi-source sensing signals includes: According to the preset high-pass filtering parameters, the multi-source sensing signal is subjected to high-pass filtering to obtain the high-pass filtered multi-source sensing signal. According to the preset low-pass filtering parameters, the high-pass filtered multi-source sensing signal is subjected to low-pass filtering to obtain the low-pass filtered multi-source sensing signal. By using the bandpass filtering parameters corresponding to the pulse resonance frequency band and vibration effective frequency band of the composite sensor, the impact pulse signal and the vibration signal in the low-pass filtered multi-source sensing signal are respectively purified by bandpass filtering to obtain the purified impact pulse signal and the purified vibration signal. The purified impact pulse signal and the purified vibration signal are converted from analog to digital using Delta-Sigma A / D conversion technology to obtain digital impact pulse signal and digital vibration signal. The impact pulse digital signal and the vibration digital signal are used as the digital sensing signals.
[0009] Optionally, the step of extracting signal features from the digital sensing signal and combining it with the rotational speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signal includes: Time-domain analysis is performed on the impact pulse digital signal and the vibration digital signal respectively to determine the peak value, effective value, and kurtosis value of the impact pulse digital signal and the peak value, effective value, and skewness value of the vibration digital signal. Based on the peak value, RMS value, and kurtosis value of the impact pulse digital signal and the peak value, RMS value, and skewness value of the vibration digital signal, the time-domain characteristic parameters corresponding to the impact pulse digital signal and the vibration digital signal are obtained respectively. Frequency domain analysis is performed on the impact pulse digital signal and the vibration digital signal respectively to obtain the initial frequency domain characteristics corresponding to the impact pulse digital signal and the vibration digital signal respectively; Based on the fault characteristic frequency associated with the speed reference signal, the initial frequency domain characteristics are calibrated to obtain the frequency domain characteristic parameters corresponding to the impact pulse digital signal and the vibration digital signal, respectively.
[0010] Optionally, the resonant demodulation of the digital sensing signal to obtain a demodulated signal characterizing the fault features includes: For the digital impulse pulse signal in the digital sensing signal, the digital impulse pulse signal is coupled with the pulse resonant frequency band of the composite sensor by electronic tuning to obtain a high-frequency resonant carrier. The high-frequency resonant carrier is sequentially subjected to bandpass filtering and envelope demodulation to obtain a low-frequency envelope sequence; The low-frequency envelope sequence is subjected to frequency band separation to obtain multiple fault characteristic sub-frequency band signals; The demodulated signal is generated by linearly superimposing the fault characteristic sub-band signals.
[0011] Optionally, obtaining the equipment operation status analysis map of the converter tilting device based on the demodulated signal at each preset position, combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position, includes: The demodulated signal at each of the preset positions is preprocessed to obtain a denoised demodulated signal; Based on the sampling interval of the composite sensor at the preset position, the denoised demodulated signal is converted into a time-domain waveform, and the time scale and amplitude range are marked in the time-domain waveform to obtain the fault characteristic waveform diagram at the preset position. The time-domain feature parameters corresponding to the preset position are fused with the fault feature waveform to generate a time-domain analysis map; The frequency domain feature parameters corresponding to the preset position are converted into frequency-amplitude distribution data using a spectrum plotting algorithm, and a frequency domain analysis spectrum is generated based on the frequency-amplitude distribution data. By integrating the time-domain analysis spectrum and the frequency-domain analysis spectrum corresponding to each preset position, the equipment operation status analysis spectrum of the converter tilting device is obtained.
[0012] Optionally, the step of fault detection of the converter tilting device based on the equipment operation status analysis map includes: The time-domain analysis spectrum and frequency-domain analysis spectrum of each preset position in the equipment operation status analysis spectrum are compared with the normal time-domain reference spectrum and normal frequency-domain reference spectrum of the corresponding component at the preset position to obtain the time-domain deviation and frequency-domain deviation of the corresponding component at the preset position. Based on the time domain deviation and the frequency domain deviation, and combined with the preset thresholds corresponding to the time domain and frequency domain respectively, the fault detection result of the converter tilting device is determined.
[0013] In a second aspect, the present invention provides a converter tilting device fault detection system, comprising: The sensing unit is used to acquire multi-source sensing signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device. The data processing unit is used to sequentially perform noise filtering and analog-to-digital conversion on the multi-source sensing signals to obtain digital sensing signals corresponding to the multi-source sensing signals; based on the digital sensing signals and combined with the speed reference signal, perform signal feature extraction to obtain frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signals; and perform resonance demodulation on the digital sensing signals to obtain demodulated signals characterizing fault features. The diagnostic analysis unit is used to obtain an equipment operation status analysis map of the converter tilting device based on the demodulated signal at each preset position, combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position; and to perform fault detection on the converter tilting device based on the equipment operation status analysis map.
[0014] Thirdly, an electronic device according to the present invention includes: a processor and a memory, the memory being used to store a computer program; When the computer program is loaded by the processor, it causes the processor to execute the converter tilting device fault detection method as described above.
[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the converter tilting device fault detection method as described above.
[0016] The converter tilting device fault detection method, system, equipment, and storage medium of this invention collect signals from different locations using multi-source sensors, covering multiple key parts of the equipment and providing more comprehensive information. The multi-angle, multi-location signal acquisition effectively avoids the limitations of local signals and reduces misdiagnosis caused by interference from a single signal. Noise filtering and analog-to-digital conversion effectively remove interference from complex background noise, retaining valid signals related to the equipment's operating status. Converting analog signals to digital signals improves signal quality and analyzability, thus ensuring high-precision fault detection. Resonant demodulation amplifies the fault characteristic frequencies and their harmonic components, making fault characteristics more apparent. The demodulated signal clearly shows changes in the fault characteristic frequencies, enabling rapid identification of fault signals even in complex signal environments, further improving the sensitivity and accuracy of fault detection, especially suitable for fault diagnosis in low signal-to-noise ratio environments. Furthermore, comprehensive analysis combining frequency and time domain characteristic parameters allows for a comprehensive evaluation of the equipment's operating status from different perspectives, avoiding misjudgments that may result from single-parameter analysis. By demodulating signals to highlight fault characteristics and providing detailed operational status information through frequency and time domain characteristic parameters, this information is integrated into an equipment operational status analysis graph. This allows for a clear visualization of the equipment's characteristic changes at different locations and under different operating conditions. This not only facilitates maintenance personnel in quickly determining whether a fault exists but also enables rapid fault location and severity assessment. Real-time signal acquisition and processing ensure that the fault detection system can promptly capture changes in equipment operational status, avoiding misjudgments due to data lag. In the early stages of a fault, fault characteristic signals are typically weak, but real-time processing and analysis can quickly identify these early signals, enabling early warning and improving the timeliness of fault detection, reducing equipment downtime and maintenance costs. This invention not only effectively avoids misdiagnosis or missed diagnosis but also enables early fault warning, improving equipment operational stability and maintenance efficiency. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating the fault detection method for the converter tilting device according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the sensor arrangement according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the effect of resonance demodulation of the impact pulse signal according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the equipment operation status analysis graph according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the fault detection system for the converter tilting device according to an embodiment of the present invention. Detailed Implementation
[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.
[0019] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.
[0020] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.
[0021] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0022] Combination Figure 1 As shown in the figure, an embodiment of the present invention provides a method for detecting faults in a converter tilting device, comprising: The multi-source sensor signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device are acquired.
[0023] Specifically, impact pulse and high-definition vibration composite sensors (dual-T sensors) are installed at key locations of the converter tilting device, such as the primary reducer, secondary reducer, and converter bearing housing, to collect multi-source sensor signals of the equipment's operating status. Simultaneously, speed sensors are installed at the connection between the motor and reducer in the primary reducer and at the main shaft of the right free end bearing housing of the converter bearing housing to obtain the speed reference signal of the converter tilting device, providing speed and phase information for subsequent fault early warning and fault diagnosis analysis.
[0024] The multi-source sensor signals are sequentially subjected to noise filtering and analog-to-digital conversion to obtain the digital sensor signals corresponding to the multi-source sensor signals.
[0025] Specifically, multi-source sensor signals of the equipment's operating status are collected using dual-T sensors, impact pulse sensors, and high-definition vibration sensors installed at key locations on the converter tilting device. These signals undergo preliminary processing by a data acquisition unit. In this embodiment, the data acquisition unit incorporates high-pass, low-pass, band-pass, and envelope filtering functions, effectively removing background noise and retaining the relevant signal components associated with equipment faults. The filtered signals are then converted from analog to digital using a 24-bit Delta-Sigma A / D converter. This converter boasts a dynamic range of up to 144dB, accurately converting continuous analog signals into discrete digital signals. In this way, the system obtains high-quality digital sensor signals, providing a reliable data foundation for subsequent signal feature extraction and fault diagnosis analysis.
[0026] Based on the digital sensing signal, signal features are extracted by combining the speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signal.
[0027] Specifically, frequency and time domain analyses are performed on the digital sensor signals. In the frequency domain analysis, parameters such as the signal's frequency distribution and amplitude spectrum are calculated to identify the main frequency components and characteristic frequencies. In the time domain analysis, parameters such as the signal's amplitude, root mean square value, and kurtosis are extracted to assess the signal's fluctuation characteristics and anomaly degree. By combining this with a speed reference signal, the correlation between fault characteristics and equipment operating status can be determined more accurately, thereby obtaining the frequency and time domain characteristic parameters corresponding to the digital sensor signals.
[0028] The digital sensing signal is resonantly demodulated to obtain a demodulated signal characterizing the fault features.
[0029] Specifically, by utilizing the resonance demodulation principle of impact pulse technology, weak impact signals in the vibration signals of mechanical equipment are effectively extracted. Based on a preset resonance frequency (such as 32kHz), the signal is demodulated to amplify the fault information, thereby making the fault characteristic signals that were originally submerged in background noise more obvious. The demodulated signal can clearly reflect the fault characteristics in the operation of the equipment, such as bearing faults, gear faults, etc., thus obtaining a demodulated signal that characterizes the fault characteristics.
[0030] Based on the demodulated signal at each preset position, and combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position, an equipment operation status analysis map of the converter tilting device is obtained.
[0031] Specifically, for the demodulated signal at each preset location, a comprehensive analysis is performed, combining the corresponding frequency domain and time domain characteristic parameters. This information is then integrated to generate a device operating status analysis graph. The graph includes various analytical diagrams such as waveform spectrum, fault frequency analysis, shaft center trajectory, shaft center position, Bode plot, waterfall plot, single-value bar plot, and envelope spectrum analysis, comprehensively displaying the device's operating status from multiple perspectives, including the time and frequency domains. This intuitive graph format clearly presents the characteristic changes of the device at different locations and under different operating conditions, providing strong evidence for fault detection.
[0032] The converter tilting device is used for fault detection based on the equipment operation status analysis chart.
[0033] Specifically, by viewing the equipment operation status analysis graph and combining it with the system's expert diagnostic function, fault detection is performed on the converter tilting device. Based on the preset fault rule base and diagnostic model, the system automatically compares and analyzes the feature information in the graph to determine whether the equipment has a fault, as well as the type and severity of the fault.
[0034] Meanwhile, in a preferred embodiment of the present invention, manual diagnosis can also be performed based on the information in the graph to further confirm the fault condition. Once a fault is detected, the system will promptly issue an early warning signal and provide a fault diagnosis report to guide maintenance personnel in carrying out targeted maintenance and repair work, thereby achieving effective fault detection of the converter tilting device.
[0035] This invention collects signals from multiple sources at different locations, covering multiple key parts of the equipment and providing more comprehensive information. The multi-angle, multi-location signal acquisition method effectively avoids the limitations of local signals and reduces misdiagnosis caused by interference from a single signal. Noise filtering and analog-to-digital conversion effectively remove interference from complex background noise, retaining valid signals related to the equipment's operating status. Converting analog signals to digital signals improves signal quality and analyzability, thus ensuring high-precision fault detection. Resonant demodulation amplifies the fault characteristic frequencies and their harmonic components, making fault characteristics more apparent. The demodulated signal clearly shows changes in the fault characteristic frequencies, enabling rapid identification of fault signals even in complex signal environments, further improving the sensitivity and accuracy of fault detection, especially suitable for fault diagnosis in low signal-to-noise ratio environments. Furthermore, comprehensive analysis combining frequency and time domain characteristic parameters allows for a comprehensive evaluation of the equipment's operating status from different perspectives, avoiding misjudgments that may result from analysis of a single characteristic parameter. By demodulating signals to highlight fault characteristics and providing detailed operational status information through frequency and time domain characteristic parameters, this information is integrated into an equipment operational status analysis graph. This allows for a clear visualization of the equipment's characteristic changes at different locations and under different operating conditions. This not only facilitates maintenance personnel in quickly determining whether a fault exists but also enables rapid fault location and severity assessment. Real-time signal acquisition and processing ensure that the fault detection system can promptly capture changes in equipment operational status, avoiding misjudgments due to data lag. In the early stages of a fault, fault characteristic signals are typically weak, but real-time processing and analysis can quickly identify these early signals, enabling early warning and improving the timeliness of fault detection, reducing equipment downtime and maintenance costs. This invention not only effectively avoids misdiagnosis or missed diagnosis but also enables early fault warning, improving equipment operational stability and maintenance efficiency.
[0036] Optionally, acquiring multi-source sensor signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device includes: The impact pulse signal and vibration signal at the preset position are obtained by a composite sensor set at the preset position. The impact pulse signal and the vibration signal are used as the multi-source sensing signals; The rotational speed signal of the drive mechanism is obtained by a rotational speed sensor installed in the drive mechanism of the converter tilting device; The rotational speed signal is used as the rotational speed reference signal for the converter tilting device.
[0037] Specifically, composite sensors (dual-T sensors) are installed at key locations in the converter tilting device (such as the primary reducer, secondary reducer, and converter bearing housing). These sensors can simultaneously collect impact pulse signals and vibration signals. For example, dual-T sensors are installed at the gear shaft input end and free end of the primary reducer to monitor the equipment's operating status. The sensors capture weak impact pulse signals generated during equipment operation through resonance demodulation, while simultaneously measuring the vibration amplitude and frequency distribution, thus obtaining comprehensive information on the equipment's operating status. The collected impact pulse and vibration signals are integrated into multi-source sensor signals for subsequent fault detection and analysis. Impact pulse signals can effectively identify minor faults in low-speed equipment, such as early bearing damage, while vibration signals provide macroscopic information on the overall operating status of the equipment. Combining these two signals allows for a comprehensive reflection of the equipment's operating status from different perspectives, providing richer data support for fault diagnosis.
[0038] Speed sensors are installed in the drive mechanism of the converter tilting device, such as at the connection between the motor and the reducer, and at the main shaft of the converter bearing housing, to monitor the rotational speed of the equipment in real time. The speed sensors provide speed signals by measuring the rotational speed of the drive mechanism. These signals not only reflect the operating speed of the equipment but also provide important reference data for subsequent fault diagnosis and analysis. For example, during resonance demodulation, the speed signals can be used for synchronous analysis to improve the accuracy of fault feature extraction. The acquired speed signals are used as the speed reference signals for the converter tilting device for subsequent fault diagnosis and analysis. The speed reference signals provide the fault diagnosis system with real-time information on the equipment's operating speed. Combined with multi-source sensor signals, fault characteristics can be identified more accurately. For example, when analyzing vibration signals, the speed reference signals can be used to determine the relationship between vibration frequency and equipment operating speed, thereby more accurately judging the fault type and severity.
[0039] Combination Figure 2As shown in the preferred embodiment of the present invention, a fault detection system for monitoring the operating status of a converter tilting device mainly consists of the following parts: Sensors are arranged at key parts of the converter tilting device, such as the primary reducer, secondary reducer, bearing housing, and the main shaft rotation area. The sensors are of two types: a dual-T sensor for collecting impact pulse signals and vibration signals, and a speed sensor for collecting speed reference signals. The signals collected by the dual-T sensor and the speed sensor are transmitted to a mechanical data monitoring device. These signals, including impact pulse signals, vibration signals, and speed signals, form the basis for fault detection and diagnosis. The mechanical data monitoring device receives the signals from the sensors and performs preliminary processing, such as signal amplification, filtering, and analog-to-digital conversion (ADC), converting analog signals into digital signals. The processed digital signals are further transmitted to a cloud platform for storage and advanced analysis. The cloud platform is responsible for receiving, storing, and analyzing the data transmitted from the monitoring device, and performing data processing tasks such as signal feature extraction, resonance demodulation, and fault diagnosis. The user interface allows users to access the cloud platform via computers, tablets, or smartphones to view the operating status and fault diagnosis results of the converter tilting device. The user interface provides intuitive charts and analytics tools to help users understand and assess the health of their devices.
[0040] In this embodiment of the invention, composite sensors are installed at key locations of the converter tilting device to acquire impact pulse signals and vibration signals. These signals are used as multi-source sensing signals, while a speed sensor is used to acquire the speed signal of the drive mechanism as a speed reference signal, thereby achieving comprehensive monitoring of the equipment's operating status. The impact pulse signal can effectively capture minute fault characteristics of low-speed equipment, such as early bearing damage, while the vibration signal provides macroscopic information on the overall operating status of the equipment. The combination of the two compensates for the shortcomings of single-signal monitoring, significantly improving the sensitivity and accuracy of fault detection.
[0041] Furthermore, the rotational speed reference signal provides an important reference for fault diagnosis, enabling more accurate extraction of fault features and better adapting to the monitoring needs of equipment operating at different speeds. This embodiment, by combining multi-source signal fusion with the rotational speed reference signal, not only improves the reliability of fault detection but also provides richer data support for subsequent fault diagnosis and analysis. It effectively solves the problems of poor signal capture capability and low diagnostic accuracy of traditional monitoring technologies under complex operating conditions, significantly improving the overall performance of converter tilting device fault detection.
[0042] Optionally, the step of sequentially performing noise filtering and analog-to-digital conversion on the multi-source sensing signals to obtain the digital sensing signals corresponding to the multi-source sensing signals includes: According to the preset high-pass filtering parameters, the multi-source sensing signal is subjected to high-pass filtering to obtain the high-pass filtered multi-source sensing signal. According to the preset low-pass filtering parameters, the high-pass filtered multi-source sensing signal is subjected to low-pass filtering to obtain the low-pass filtered multi-source sensing signal. By using the bandpass filtering parameters corresponding to the pulse resonance frequency band and vibration effective frequency band of the composite sensor, the impact pulse signal and the vibration signal in the low-pass filtered multi-source sensing signal are respectively purified by bandpass filtering to obtain the purified impact pulse signal and the purified vibration signal. The purified impact pulse signal and the purified vibration signal are converted from analog to digital using Delta-Sigma A / D conversion technology to obtain digital impact pulse signal and digital vibration signal. The impact pulse digital signal and the vibration digital signal are used as the digital sensing signals.
[0043] Specifically, a high-pass filter is used to process the acquired multi-source sensor signals. The cutoff frequency of the high-pass filter is preset according to the operating characteristics of the converter tilting device, usually set in a low frequency range (e.g., 10Hz) to remove low-frequency interference components in the signal, such as low-frequency vibration background noise from the equipment. The high-pass filtered signal can effectively remove the influence of low-frequency noise, retaining the high-frequency characteristic parts of the signal, providing a clearer signal basis for subsequent signal processing. Based on the high-pass filtered multi-source sensor signals, further processing is performed using a low-pass filter. The cutoff frequency of the low-pass filter is preset according to the frequency range of the equipment fault characteristic signal, usually set in several kilohertz (e.g., 10kHz) to remove high-frequency noise components in the signal, such as electromagnetic interference or high-frequency vibration noise. The low-pass filtered signal can effectively remove high-frequency noise interference, retaining the mid-frequency and low-frequency fault characteristic parts of the signal, further improving the signal quality and analyzability. According to the characteristics of the composite sensor (dual-T sensor), band-pass filtering is performed on the low-pass filtered impact pulse signal and vibration signal respectively. For impact pulse signals, the bandpass filter is set to the sensor's resonant frequency band (e.g., 32kHz) to highlight the high-frequency characteristics of the impact pulse signal. For vibration signals, the bandpass filter is set to the effective frequency band of the vibration (e.g., 2Hz to 10kHz) to extract key information from the vibration signal. This targeted bandpass filtering purification further removes interference from irrelevant frequency components, making the impact pulse and vibration signals purer and providing high-quality data for subsequent fault feature extraction. The purified impact pulse and vibration signals are then converted from analog to digital using high-precision Delta-Sigma A / D conversion technology. Delta-Sigma A / D converters have high dynamic range (e.g., 144dB) and high precision (e.g., 24 bits), enabling precise conversion of continuous analog signals into discrete digital signals. This high-precision analog-to-digital conversion ensures that more detailed information is retained during the digitization process, reducing quantization errors and providing high-quality digital signals for subsequent digital signal processing and fault diagnosis analysis. After purification through high-pass filtering, low-pass filtering, and band-pass filtering, as well as analog-to-digital conversion, the digital signals of impact pulses and vibrations are integrated into digital sensing signals. These digital sensing signals have high signal-to-noise ratio and high precision, and can accurately reflect the operating status and potential fault characteristics of the converter tilting device, providing a reliable data foundation for subsequent signal feature extraction, resonance demodulation, and fault diagnosis analysis.
[0044] In this embodiment of the invention, high-pass filtering effectively removes low-frequency background noise, while low-pass filtering further removes high-frequency interference. This dual filtering ensures that the main fault characteristics in the signal are preserved. Subsequently, band-pass filtering is applied to the impact pulse signal and vibration signal to refine them, highlighting their key frequency components and further reducing noise interference, resulting in a purer signal. Finally, high-precision Delta-Sigma A / D conversion technology is used to accurately convert the analog signal into a digital signal, ensuring that more detailed information is retained during the digitization process and reducing quantization errors. This effectively removes background noise, highlights fault characteristic signals, significantly improves the signal-to-noise ratio and analyzability, and provides high-quality digital sensor signals for subsequent fault diagnosis, thereby improving the accuracy and reliability of fault detection.
[0045] Optionally, the step of extracting signal features from the digital sensing signal and combining it with the rotational speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signal includes: Time-domain analysis is performed on the impact pulse digital signal and the vibration digital signal respectively to determine the peak value, effective value, and kurtosis value of the impact pulse digital signal and the peak value, effective value, and skewness value of the vibration digital signal. Based on the peak value, RMS value, and kurtosis value of the impact pulse digital signal and the peak value, RMS value, and skewness value of the vibration digital signal, the time-domain characteristic parameters corresponding to the impact pulse digital signal and the vibration digital signal are obtained respectively. Frequency domain analysis is performed on the impact pulse digital signal and the vibration digital signal respectively to obtain the initial frequency domain characteristics corresponding to the impact pulse digital signal and the vibration digital signal respectively; Based on the fault characteristic frequency associated with the speed reference signal, the initial frequency domain characteristics are calibrated to obtain the frequency domain characteristic parameters corresponding to the impact pulse digital signal and the vibration digital signal, respectively.
[0046] Specifically, time-domain analysis is performed on the digital signals of impact pulses and vibrations. For the digital signals of impact pulses, the peak value (maximum amplitude), RMS value (root mean square value), and kurtosis value (a statistical measure reflecting the impact characteristics of the signal) are calculated. For the digital signals of vibrations, the peak value, RMS value, and skewness value (a statistical measure reflecting the asymmetry of the signal) are calculated. These parameters are automatically extracted by the system's built-in algorithm, enabling rapid assessment of the signal's fluctuation characteristics and degree of anomaly. The extracted peak value, RMS value, and kurtosis value of the digital signals of impact pulses and vibrations are integrated into their respective time-domain feature parameters. These parameters can reflect changes in the equipment's operating status from a time-domain perspective. For example, an increase in kurtosis value may indicate an abnormal impact in the impact pulse signal, while a change in skewness value may suggest an increase in the asymmetry of the vibration signal, providing important basis for subsequent fault diagnosis. Frequency-domain analysis is performed on the digital signals of impact pulses and vibrations using Fast Fourier Transform (FFT). The system calculates the frequency spectrum of the signals, extracts information such as amplitude spectrum and phase spectrum, and obtains initial frequency-domain features. These features can reveal periodic components and fault characteristic frequencies in the signal. For example, bearing faults typically generate specific fault characteristic frequencies and their harmonics in the spectrum. The initial frequency domain features are calibrated using fault characteristic frequencies associated with a speed reference signal. The speed reference signal provides real-time information on the equipment's operating speed; combined with the equipment's structural parameters (such as bearing geometry), characteristic frequencies related to equipment faults can be calculated. By comparing and calibrating the initial frequency domain features with these characteristic frequencies, fault characteristic frequencies can be identified more accurately, resulting in calibrated frequency domain characteristic parameters. This process improves the accuracy of frequency domain analysis and ensures the reliability of fault diagnosis results.
[0047] Specifically, in a preferred embodiment of the present invention, firstly, the system acquires the current operating speed of the converter tilting device (such as the actual speed value of the drive mechanism) in real time based on the speed reference signal. Combined with the preset structural parameters of the equipment (such as the inner / outer ring diameter of the bearing, the number and diameter of the rolling elements, the number of teeth of the gear, etc.), the system calculates the theoretical fault characteristic frequency corresponding to each component under the current working condition using fault characteristic frequency formulas (e.g., bearing outer ring fault characteristic frequency = 0.5 × speed × number of rolling elements × (1 - rolling element diameter / bearing pitch circle diameter), gear meshing fault characteristic frequency = speed × number of teeth of the driving gear). Subsequently, the initial frequency domain features obtained by fast Fourier transform (such as each frequency component in the amplitude spectrum) are matched with the theoretical fault characteristic frequencies calculated above. If a certain frequency component in the initial frequency domain features deviates from the theoretical fault characteristic frequency, the frequency value offset in the initial frequency domain is adjusted to the theoretical fault characteristic frequency based on the theoretical fault characteristic frequency, while retaining the amplitude information corresponding to the frequency component. Finally, the corrected frequency and the corresponding amplitude are integrated to obtain the calibrated frequency domain feature parameters.
[0048] In this embodiment of the invention, by performing time-domain and frequency-domain analysis on the digital signals of impact pulses and vibrations, and calibrating them in conjunction with a speed reference signal, the invention can accurately extract the time-domain characteristic parameters (such as peak value, RMS value, kurtosis value, and skewness value) and frequency-domain characteristic parameters (such as fault characteristic frequencies and their harmonics) of the signals. This multi-dimensional feature extraction method not only comprehensively reflects the changes in the operating status of the equipment, but also improves the accuracy of feature extraction through the calibration of the speed reference signal, thereby providing richer and more reliable data support for subsequent fault diagnosis and significantly improving the accuracy and reliability of fault detection.
[0049] Optionally, the resonant demodulation of the digital sensing signal to obtain a demodulated signal characterizing the fault features includes: For the digital impulse pulse signal in the digital sensing signal, the digital impulse pulse signal is coupled with the pulse resonant frequency band of the composite sensor by electronic tuning to obtain a high-frequency resonant carrier. The high-frequency resonant carrier is sequentially subjected to bandpass filtering and envelope demodulation to obtain a low-frequency envelope sequence; The low-frequency envelope sequence is subjected to frequency band separation to obtain multiple fault characteristic sub-frequency band signals; The demodulated signal is generated by linearly superimposing the fault characteristic sub-band signals.
[0050] Specifically, electronic tuning technology is used to couple the digital impulse pulse signal with the pulse resonant frequency band (e.g., 32kHz) of the composite sensor. By adjusting the electronic parameters in the signal processing module, the frequency of the digital impulse pulse signal is matched with the resonant frequency of the sensor, thereby generating a high-frequency resonant carrier. The high-frequency resonant carrier signal is then subjected to bandpass filtering and envelope demodulation. First, a bandpass filter removes high-frequency noise and irrelevant frequency components from the carrier signal, retaining the signal within the frequency range relevant to the fault characteristics. Subsequently, envelope demodulation technology is used to extract the envelope of the signal, obtaining a low-frequency envelope sequence. Envelope demodulation removes the high-frequency carrier, highlighting the low-frequency modulation portion of the signal, thus clearly displaying the fault characteristics. The low-frequency envelope sequence is further decomposed into multiple fault characteristic sub-band signals. Using techniques such as multi-bandpass filters or wavelet transform, the low-frequency envelope sequence is divided into different frequency bands, each corresponding to a different fault characteristic frequency range. For example, bearing faults may generate characteristic frequencies in a specific low-frequency band; frequency band separation allows for the extraction of these characteristic frequencies, providing more detailed frequency domain information for subsequent fault diagnosis. Multiple fault characteristic sub-band signals are linearly superimposed to generate the final demodulated signal. Linear superposition integrates fault characteristic information from different frequency bands into a complete demodulated signal. This demodulated signal centrally reflects the fault characteristics during equipment operation and can be used more intuitively for fault diagnosis and analysis. Figure 3 As shown, when the signal frequency reaches 32kHz (corresponding to the sensor's inherent resonant frequency band), the signal amplitude rapidly jumps from the reference value of 1 to a high amplitude range of 5-7, intuitively demonstrating the amplification effect of the high-frequency resonant carrier generated by electronic tuning coupling. The two waveforms on the right compare the signal morphology before and after resonant demodulation. The upper waveform is the high-frequency resonant carrier waveform after coupling (amplitude fluctuations are severe, and high-frequency components are dense), while the lower waveform is the low-frequency envelope sequence waveform after bandpass filtering and envelope demodulation (amplitude fluctuations tend to be gentler, and low-frequency fault characteristics are clearer). In this embodiment of the invention, by performing resonant demodulation processing on the digital signal of the impact pulse, weak fault characteristic signals can be significantly amplified, improving the sensitivity of fault detection. First, electronic tuning couples the signal to the sensor's resonant frequency band, generating a high-frequency resonant carrier to amplify the weak impact signal. Subsequently, bandpass filtering and envelope demodulation remove high-frequency noise and extract the low-frequency envelope sequence, highlighting the fault characteristics. Further frequency band separation and linear superposition integrate the fault characteristics of different frequency bands into a demodulated signal, making the fault characteristics clearer and easier to identify.
[0051] Optionally, obtaining the equipment operation status analysis map of the converter tilting device based on the demodulated signal at each preset position, combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position, includes: The demodulated signal at each of the preset positions is preprocessed to obtain a denoised demodulated signal; Based on the sampling interval of the composite sensor at the preset position, the denoised demodulated signal is converted into a time-domain waveform, and the time scale and amplitude range are marked in the time-domain waveform to obtain the fault characteristic waveform diagram at the preset position. The time-domain feature parameters corresponding to the preset position are fused with the fault feature waveform to generate a time-domain analysis map; The frequency domain feature parameters corresponding to the preset position are converted into frequency-amplitude distribution data using a spectrum plotting algorithm, and a frequency domain analysis spectrum is generated based on the frequency-amplitude distribution data. By integrating the time-domain analysis spectrum and the frequency-domain analysis spectrum corresponding to each preset position, the equipment operation status analysis spectrum of the converter tilting device is obtained.
[0052] Specifically, for the demodulated signal at each preset location, digital signal processing techniques are used for denoising. By applying algorithms such as adaptive filters or wavelet transforms, random noise and interference components in the demodulated signal are removed, retaining the effective signal related to the fault characteristics. Based on the sampling interval of the composite sensor (e.g., the set sampling frequency), the denoised demodulated signal is converted into a time-domain waveform. In the time-domain waveform, time scales (e.g., seconds) and amplitude ranges (e.g., acceleration units or voltage units) are marked to clearly show the signal's changes over time. The extracted time-domain feature parameters (e.g., peak value, RMS value, kurtosis value, etc.) are fused with the fault feature waveform. The specific values and locations of these feature parameters are marked on the waveform, for example, the peak value is marked at the peak of the waveform, and the RMS value is marked below the waveform. A spectrum plotting algorithm (e.g., Fast Fourier Transform, FFT) is used to convert the frequency-domain feature parameters into frequency-amplitude distribution data. In the frequency domain analysis spectrum, the horizontal axis represents frequency (e.g., Hz), and the vertical axis represents amplitude (e.g., acceleration units or voltage units), graphically displaying the frequency component distribution of the signal. By highlighting the characteristic frequencies of faults and their harmonic components, frequency domain analysis maps can clearly reflect the periodic fault characteristics during equipment operation, such as the characteristic frequencies of bearing faults. Integrating the time domain and frequency domain analysis maps for each preset location generates a comprehensive equipment operating status analysis map. The map displays the analysis results in both the time and frequency domains, and uses colors or markers to indicate the location and severity of fault characteristics. This comprehensive map can fully reflect the operating status of the equipment from multiple perspectives, providing a more intuitive and comprehensive basis for fault diagnosis, helping maintenance personnel quickly locate faults and assess their severity.
[0053] In this embodiment of the invention, by denoising the demodulated signal, generating fault feature waveforms, fusing time-domain feature parameters, and plotting frequency-domain analysis graphs, and finally integrating them into an equipment operation status analysis graph, the operating status and fault characteristics of the converter tilting device can be comprehensively and intuitively reflected from both time and frequency domain dimensions. This multi-dimensional and comprehensive analysis graph not only improves the accuracy of fault detection but also provides maintenance personnel with intuitive diagnostic basis, significantly improving the efficiency and reliability of fault diagnosis, and effectively solving the problems of unclear fault characteristics and low diagnostic efficiency in traditional methods.
[0054] Optionally, the step of fault detection of the converter tilting device based on the equipment operation status analysis map includes: The time-domain analysis spectrum and frequency-domain analysis spectrum of each preset position in the equipment operation status analysis spectrum are compared with the normal time-domain reference spectrum and normal frequency-domain reference spectrum of the corresponding component at the preset position to obtain the time-domain deviation and frequency-domain deviation of the corresponding component at the preset position. Based on the time domain deviation and the frequency domain deviation, and combined with the preset thresholds corresponding to the time domain and frequency domain respectively, the fault detection result of the converter tilting device is determined.
[0055] Specifically, the system extracts time-domain and frequency-domain analysis maps for each preset location from the equipment operation status analysis map. Simultaneously, the system pre-stores time-domain and frequency-domain reference maps for the corresponding components under normal operating conditions. Using image processing algorithms or feature matching techniques, the analysis maps are compared with the reference maps to calculate the differences. For the time-domain analysis maps, differences in parameters such as peak value, RMS value, and kurtosis are compared; for the frequency-domain analysis maps, changes in fault characteristic frequencies and their amplitudes are compared, thus obtaining the time-domain deviation (e.g., deviations in peak value, RMS value, and kurtosis) and frequency-domain deviation (e.g., deviations in fault characteristic frequencies and their amplitudes) for the corresponding components at each preset location. The time-domain and frequency-domain deviations are judged based on preset time-domain and frequency-domain thresholds, which are set based on the characteristic differences between the equipment's normal operating and fault states. For example, if the peak value deviation in the time-domain deviation exceeds a preset threshold, or the fault characteristic frequency amplitude deviation in the frequency-domain deviation exceeds a threshold, then the component is judged to have a potential fault. Each parameter can be set with the most reasonable preset threshold according to preset standards (such as ISO2372 and ISO10816 standards). The system integrates the judgment results in the time domain and frequency domain to finally determine the fault detection result of the converter tilting device, and feeds back the fault information to the operator through a visual interface or alarm system to guide subsequent maintenance and repair work. Various fault symptoms of the equipment can be preset, and trend analysis of each symptom can be performed based on the specific values of time domain deviation and frequency domain deviation, thereby realizing early monitoring and prediction of each fault and significantly increasing the equipment warning time. In the preferred embodiment of the present invention, as Figure 4 As shown, the operational status analysis graphs for all devices, motors, and pumps at preset locations can intelligently display their operational status using three colors: green, yellow, and red. Specifically, when the operating parameters of a component (such as the kurtosis value of an impact pulse signal or the skewness value of a vibration signal) are in the green range of the graph, it indicates that the corresponding device is operating normally. If the parameters fluctuate to the yellow range (approaching a preset threshold), it indicates that the operational trend needs close monitoring. When the parameters exceed the threshold and enter the red range (such as the peak value of an impact pulse or the amplitude of an abnormal characteristic frequency exceeding a critical value), the time-domain and frequency-domain deviations of the operational status analysis graphs are combined to quickly determine that the device or component (such as a motor bearing or a pump gear) has a fault.
[0056] In this embodiment of the invention, by comparing the equipment operating status analysis graph with normal reference graphs in the time and frequency domains, and combining this with preset thresholds for judgment, the invention can accurately identify the fault characteristics of each component of the converter tilting device, effectively improving the accuracy and reliability of fault detection. This method based on multi-dimensional deviation analysis can not only quickly locate the fault position, but also accurately assess the severity of the fault, providing a scientific basis for equipment maintenance and significantly improving the operational stability and maintenance efficiency of the equipment.
[0057] Combination Figure 5 As shown, another embodiment of the present invention provides a converter tilting device fault detection system, comprising: The sensing unit is used to acquire multi-source sensing signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device. The data processing unit is used to sequentially perform noise filtering and analog-to-digital conversion on the multi-source sensing signals to obtain digital sensing signals corresponding to the multi-source sensing signals; based on the digital sensing signals and combined with the speed reference signal, perform signal feature extraction to obtain frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signals; and perform resonance demodulation on the digital sensing signals to obtain demodulated signals characterizing fault features. The diagnostic analysis unit is used to obtain an equipment operation status analysis map of the converter tilting device based on the demodulated signal at each preset position, combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position; and to perform fault detection on the converter tilting device based on the equipment operation status analysis map.
[0058] The converter tilting device fault detection system of the present invention has the same advantages over the prior art as the above-mentioned converter tilting device fault detection method, and will not be repeated here.
[0059] Another embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store a computer program; When the computer program is loaded by the processor, it causes the processor to execute the converter tilting device fault detection method as described above.
[0060] The electronic device of the present invention has the same advantages over the prior art as the above-mentioned converter tilting device fault detection method, and will not be repeated here.
[0061] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the converter tilting device fault detection method as described above.
[0062] The computer-readable storage medium of the present invention has the same advantages over the prior art as the aforementioned converter tilting device fault detection method over the prior art, and will not be repeated here.
[0063] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.
Claims
1. A method for detecting faults in a converter tilting device, characterized in that, include: Acquire multi-source sensor signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device; The multi-source sensing signals are sequentially subjected to noise filtering and analog-to-digital conversion to obtain the digital sensing signals corresponding to the multi-source sensing signals; Based on the digital sensing signal, signal features are extracted by combining the speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signal; The digital sensing signal is resonantly demodulated to obtain a demodulated signal characterizing the fault features; Based on the demodulated signal at each preset position, and combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position, an equipment operation status analysis map of the converter tilting device is obtained; The converter tilting device is fault detected based on the equipment operation status analysis chart.
2. The method for detecting faults in the converter tilting device according to claim 1, characterized in that, The acquisition of multi-source sensor signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device includes: The impact pulse signal and vibration signal at the preset position are obtained by a composite sensor set at the preset position. The impact pulse signal and the vibration signal are used as the multi-source sensing signals; The rotational speed signal of the drive mechanism is obtained by a rotational speed sensor installed in the drive mechanism of the converter tilting device; The rotational speed signal is used as the rotational speed reference signal for the converter tilting device.
3. The method for detecting faults in the converter tilting device according to claim 2, characterized in that, The step of sequentially performing noise filtering and analog-to-digital conversion on the multi-source sensing signals to obtain the corresponding digital sensing signals includes: According to the preset high-pass filtering parameters, the multi-source sensing signal is subjected to high-pass filtering to obtain the high-pass filtered multi-source sensing signal. According to the preset low-pass filtering parameters, the high-pass filtered multi-source sensing signal is subjected to low-pass filtering to obtain the low-pass filtered multi-source sensing signal. By using the bandpass filtering parameters corresponding to the pulse resonance frequency band and vibration effective frequency band of the composite sensor, the impact pulse signal and the vibration signal in the low-pass filtered multi-source sensing signal are respectively purified by bandpass filtering to obtain the purified impact pulse signal and the purified vibration signal. The purified impact pulse signal and the purified vibration signal are converted from analog to digital using Delta-Sigma A / D conversion technology to obtain digital impact pulse signal and digital vibration signal. The impact pulse digital signal and the vibration digital signal are used as the digital sensing signals.
4. The method for detecting faults in the converter tilting device according to claim 3, characterized in that, The step of extracting signal features based on the digital sensing signal and the rotational speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signal includes: Time-domain analysis is performed on the impact pulse digital signal and the vibration digital signal respectively to determine the peak value, effective value, and kurtosis value of the impact pulse digital signal and the peak value, effective value, and skewness value of the vibration digital signal. Based on the peak value, RMS value, and kurtosis value of the impact pulse digital signal and the peak value, RMS value, and skewness value of the vibration digital signal, the time-domain characteristic parameters corresponding to the impact pulse digital signal and the vibration digital signal are obtained respectively. Frequency domain analysis is performed on the impact pulse digital signal and the vibration digital signal respectively to obtain the initial frequency domain characteristics corresponding to the impact pulse digital signal and the vibration digital signal respectively; Based on the fault characteristic frequency associated with the speed reference signal, the initial frequency domain characteristics are calibrated to obtain the frequency domain characteristic parameters corresponding to the impact pulse digital signal and the vibration digital signal, respectively.
5. The method for detecting faults in the converter tilting device according to claim 3, characterized in that, The resonant demodulation of the digital sensing signal to obtain a demodulated signal characterizing the fault features includes: For the digital impulse pulse signal in the digital sensing signal, the digital impulse pulse signal is coupled with the pulse resonant frequency band of the composite sensor by electronic tuning to obtain a high-frequency resonant carrier. The high-frequency resonant carrier is sequentially subjected to bandpass filtering and envelope demodulation to obtain a low-frequency envelope sequence; The low-frequency envelope sequence is subjected to frequency band separation to obtain multiple fault characteristic sub-frequency band signals; The demodulated signal is generated by linearly superimposing the fault characteristic sub-band signals.
6. The method for detecting faults in the converter tilting device according to claim 2, characterized in that, The step of obtaining the equipment operation status analysis map of the converter tilting device based on the demodulated signal at each preset position, combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position, includes: The demodulated signal at each of the preset positions is preprocessed to obtain a denoised demodulated signal; Based on the sampling interval of the composite sensor at the preset position, the denoised demodulated signal is converted into a time-domain waveform, and the time scale and amplitude range are marked in the time-domain waveform to obtain the fault characteristic waveform diagram at the preset position. The time-domain feature parameters corresponding to the preset position are fused with the fault feature waveform to generate a time-domain analysis map; The frequency domain feature parameters corresponding to the preset position are converted into frequency-amplitude distribution data using a spectrum plotting algorithm, and a frequency domain analysis spectrum is generated based on the frequency-amplitude distribution data. By integrating the time-domain analysis spectrum and the frequency-domain analysis spectrum corresponding to each preset position, the equipment operation status analysis spectrum of the converter tilting device is obtained.
7. The method for detecting faults in the converter tilting device according to claim 6, characterized in that, The fault detection of the converter tilting device based on the equipment operation status analysis map includes: The time-domain analysis spectrum and frequency-domain analysis spectrum of each preset position in the equipment operation status analysis spectrum are compared with the normal time-domain reference spectrum and normal frequency-domain reference spectrum of the corresponding component at the preset position to obtain the time-domain deviation and frequency-domain deviation of the corresponding component at the preset position. Based on the time domain deviation and the frequency domain deviation, and combined with the preset thresholds corresponding to the time domain and frequency domain respectively, the fault detection result of the converter tilting device is determined.
8. A fault detection system for a converter tilting device, characterized in that, include: The sensing unit is used to acquire multi-source sensing signals of the converter tilting device at multiple preset positions and the rotational speed reference signal of the converter tilting device. The data processing unit is used to sequentially perform noise filtering and analog-to-digital conversion on the multi-source sensing signals to obtain the digital sensing signals corresponding to the multi-source sensing signals; and to extract signal features based on the digital sensing signals and the rotational speed reference signal to obtain the frequency domain feature parameters and time domain feature parameters corresponding to the digital sensing signals. The digital sensing signal is resonantly demodulated to obtain a demodulated signal characterizing the fault features; The diagnostic analysis unit is used to obtain an equipment operation status analysis map of the converter tilting device based on the demodulated signal at each preset position, combined with the frequency domain characteristic parameters and time domain characteristic parameters corresponding to the preset position; and to perform fault detection on the converter tilting device based on the equipment operation status analysis map.
9. An electronic device, characterized in that, include: Processor and memory, the memory being used to store computer programs; When the computer program is loaded by the processor, it causes the processor to execute the converter tilting device fault detection method as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the converter tilting device fault detection method as described in any one of claims 1-7.