A digital equipment fault prediction method and system

Abstract

The present invention discloses a digital equipment fault prediction method and system, belonging to the field of data processing technology. The method specifically includes: real-time collection of input power, output flow and pressure of digital equipment, calculation of energy loss, and establishment of an energy consumption database; use of vibration and acoustic sensors to monitor signals, identification of noise sources and their relationship with energy consumption through frequency domain analysis, and adoption of noise reduction measures; collection of historical data to train a neural network model, construction of a fault prediction model, real-time prediction of potential faults and early warning, and automatic adjustment of operating parameters according to energy consumption analysis results; automatic activation of a noise reduction device based on the noise reduction analysis results, and provision of a noise control solution; implementation of energy efficiency optimization and noise control for digital equipment, and improvement of equipment operation efficiency and reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and system for predicting faults of digital equipment. Background Art

[0002] Digital devices are widely used in many fields such as industrial production. Traditional data analysis methods for digital devices are often limited to single-dimensional data monitoring and analysis. There is a lack of systematic and effective analysis and comprehensive solutions for problems such as energy consumption, noise, and failures during the operation of digital devices, resulting in resource waste, shortened equipment life, and reduced production efficiency. Therefore, it is of great significance to develop a comprehensive digital device data analysis method.

[0003] For example, patent application publication number CN115859023A discloses a standardized format processing method and system for collecting key data from a pump-motor system. The method includes: acquiring key data signals from the pump-motor system in real time through sensors, amplifying them through a signal amplifier; converting them into digital signals through a data collector, and then connecting them to an embedded system for standardized format processing; transmitting the standardized key data signals from the embedded system to a host computer for subsequent data analysis, completing pump-motor status testing or fault diagnosis, and uploading the data to a network. This technical solution is suitable for multiple sensors to simultaneously sense multiple key data parameters, ensuring that multiple types of key data are collected under the same operating conditions of the pump-motor system; and facilitating more accurate judgment of the operating status of the pump-motor system or any equipment faults.

[0004] The above existing technologies all have the following problems: lack of fault diagnosis measures and no noise reduction measures. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes a digital equipment fault prediction method and system, which collects the input power, output flow and pressure of the digital equipment in real time, calculates energy loss, and establishes an energy consumption database; uses vibration and acoustic sensors to monitor signals, identifies noise sources and their relationship with energy consumption through frequency domain analysis, and takes noise reduction measures; collects historical data to train a neural network model, constructs a fault prediction model, predicts potential faults in real time and issues warnings, and automatically adjusts operating parameters according to the energy consumption analysis results; automatically starts the noise reduction device based on the noise reduction analysis results, and provides a noise control plan; realizes energy efficiency optimization and noise control of digital equipment, and improves the equipment operation efficiency and reliability.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A digital device fault prediction method, comprising:

[0008] The input power, output flow and pressure of digital devices are obtained through sensors, and the energy loss under different working conditions is calculated. Based on the calculation results, an energy consumption database is established;

[0009] Real-time monitoring of vibration signals and sound pressure signals of digital equipment, analysis of noise frequency components, and adoption of corresponding noise reduction measures based on energy consumption database;

[0010] Collect historical data during the operation of digital devices and build a fault prediction model. The historical data includes energy consumption data, noise data, temperature data, and pressure data;

[0011] According to the fault prediction model, it is predicted whether the digital device has a potential fault and the type of the fault, and based on the prediction result, a digital device adjustment plan is generated.

[0012] Specifically, the calculation of energy loss under different working conditions includes:

[0013] Use power sensors to collect the input power of digital devices in real time, and use flow sensors and pressure sensors to measure the output flow and pressure;

[0014] According to the output flow and pressure, calculate the output power P out , the formula is: Where ρ represents the liquid density, Q represents the output flow of the digital device, g represents the acceleration of gravity, H represents the head, η f represents the fluid efficiency coefficient, η l represents the energy loss coefficient, T represents the current temperature, T0 represents the reference temperature, α represents the temperature influence coefficient, C represents the current concentration, C0 represents the reference concentration, β represents the concentration influence coefficient, δ represents the time attenuation coefficient, t represents the operating time, and e represents the exponent;

[0015] According to the output power P out and the input power P in , the energy loss under different working conditions is obtained as loss=P in -P out .

[0016] Specifically, the real-time monitoring of vibration signals and sound pressure signals of digital devices, analysis of the frequency components of the noise, and adoption of corresponding noise reduction measures in combination with the energy consumption database include:

[0017] Use vibration sensors and acoustic sensors to monitor vibration signals and sound pressure signals in real time, and convert time domain signals into frequency domain signals through Fourier transform;

[0018] Analyzing the frequency components of the noise based on the frequency domain signal and identifying the noise sources of different frequencies and their relationship with energy consumption in combination with data in an energy consumption database; the data in the energy consumption database is data related to energy loss and operating conditions;

[0019] Based on the identification results, the noise source is determined and corresponding noise reduction measures are taken. The noise reduction measures include optimizing component structure to reduce mechanical vibration and improving flow channel design to reduce fluid pulsation.

[0020] Specifically, the process of constructing the fault prediction model includes:

[0021] Collect historical data during the operation of digital devices and label the historical data, including energy consumption data, noise data, temperature data, and pressure data;

[0022] Load the pre-built neural network model and use the labeled historical data to train the pre-built neural network model to generate a trained fault prediction model.

[0023] Specifically, the method of predicting whether a digital device has a potential fault and the type of the fault based on the fault prediction model, and generating a digital device adjustment plan based on the prediction result, includes:

[0024] Real-time collection and pre-processing of operating data of digital devices; the operating data of digital devices includes flow, sound, vibration, temperature, current, electrical protection element tripping, and controller alarm;

[0025] Use signal processing methods to extract features from the pre-processed operating data to generate digital device operating feature data;

[0026] Inputting the digital device operation characteristic data into a trained fault prediction model, the fault prediction model predicts whether the digital device has a potential fault and the type of fault based on the matching degree between the digital device operation characteristic data and the historical fault data characteristics;

[0027] The fault prediction model predicts a possible fault and issues a warning message, and the operator responds immediately and generates a digital equipment adjustment plan through threshold judgment.

[0028] Specifically, the generating of a digital device adjustment plan through threshold determination includes:

[0029] determining whether the energy loss exceeds a preset threshold, and automatically adjusting the operating parameters of the digital device if the energy loss exceeds the preset threshold;

[0030] Determine whether the noise exceeds the preset noise tolerance range. If so, automatically activate the noise reduction device, which includes a shock-absorbing pad and a muffler. At the same time, provide a noise control plan based on the noise frequency analysis results;

[0031] The fault type is determined according to the output result of the fault prediction model, and the operation mode of the digital device is automatically adjusted according to the determination result of the fault type, including reducing the operation speed and switching to spare components.

[0032] Specifically, analyzing the frequency components of the noise based on the frequency domain signal, combining the data in the energy consumption database, and identifying the noise sources of different frequencies and their relationship with energy consumption includes:

[0033] Monitoring the sound pressure signal of the digital device in real time through an acoustic sensor, and converting the sound pressure signal into a frequency domain signal through Fourier transform;

[0034] performing spectrum analysis on the frequency domain signal, identifying the main frequency components of the noise, and evaluating the noise level of each frequency component;

[0035] Extract energy consumption data related to the current working conditions from the energy consumption database and analyze the potential correlation between the energy consumption data and the noise frequency components;

[0036] Identify noise sources based on known noise frequency components and energy consumption data, combined with the working principles and structural characteristics of digital devices; the noise sources include mechanical vibration, fluid pulsation, and electromagnetic interference;

[0037] For each identified noise source, the relationship between the noise source at different frequencies and energy consumption is analyzed.

[0038] Specifically, extracting energy consumption data related to the current working condition from the energy consumption database and analyzing the potential correlation between the energy consumption data and the noise frequency components includes:

[0039] Get the current operating status of the digital device;

[0040] According to the operating conditions, query and extract corresponding energy consumption data from the energy consumption database, and pre-process the extracted energy consumption data; the energy consumption data includes electrical energy consumption, mechanical energy loss, and thermal energy loss;

[0041] Obtaining the noise frequency components and corresponding noise levels of the digital device, and analyzing the potential correlation between the energy consumption data and the noise frequency components using a correlation analysis method; the potential correlation refers to whether there is a correlation between the energy consumption parameters and the frequency noise, and the correlation includes positive correlation and negative correlation;

[0042] Based on the analysis results, the physical meaning of the correlation between energy consumption data and noise frequency components is explained, and optimization suggestions are provided based on the analysis results.

[0043] A digital equipment fault prediction system includes: an energy consumption analysis module, a noise analysis module, a fault prediction module, a noise reduction control module, and a fault processing module;

[0044] The energy consumption analysis module is used to collect the input power, output flow and pressure of digital devices in real time, calculate the energy loss under different working conditions, and establish an energy consumption database;

[0045] The noise analysis module is used to monitor vibration signals and sound pressure signals in real time, analyze the frequency components of noise, identify noise sources and their relationship with energy consumption;

[0046] The fault prediction module trains a neural network model based on the collected historical data, and predicts the potential faults and fault types of digital devices in real time, and automatically adjusts the operating parameters based on the prediction results;

[0047] The noise reduction control module automatically activates the noise reduction device based on the noise reduction analysis results and provides a noise control solution;

[0048] The fault processing module is used to automatically adjust the operation mode of the digital device according to the fault type after the fault prediction model issues an early warning, and record the fault processing process and processing results.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention proposes a digital equipment fault prediction method and system. Through systematic analysis of energy consumption and noise reduction, as well as fault prediction and control, it can effectively improve the energy utilization efficiency of digital equipment, reduce noise pollution, detect potential faults in advance and effectively deal with them, extend the service life of equipment, and improve production efficiency. At the same time, by real-time collection of the input power, output flow and pressure of digital equipment and calculation of energy loss, it can accurately grasp the energy consumption of digital equipment under different working conditions, and then establish an energy consumption database to provide data support for subsequent energy efficiency optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a digital device fault prediction method according to the present invention;

[0052] Figure 2 This is a principle flow chart of a digital device fault prediction method according to the present invention;

[0053] Figure 3 This is a structural diagram of a digital device of the present invention;

[0054] Figure 4This is an architecture diagram of a digital device fault prediction system according to the present invention.

[0055] Reference numerals:

[0056] 1. Digital motor; 1a. Hybrid magnetic motor; 1c. Heat dissipation device; 2. LCD touch screen; 3. Bracket; 4. Centrifugal pump head; 4a. Pump body; 4b. Shaft; 4c. Pump cover; 5. Water inlet; 6. Lifting device; 7. Multi-function sensor; 8. Water outlet; 9. Aviation plug. DETAILED DESCRIPTION

[0057] Example 1

[0058] See also Figure 1-Figure 3 , an embodiment of the present invention provides: a digital device fault prediction method, in the present invention, the digital device refers to a digital pump, Figure 3 In the embodiment, the digital device includes a digital motor 1, a liquid crystal touch screen 2, a bracket 3, a centrifugal pump head 4, a multi-function sensor 7, and a lifting device 6; the digital motor 1 includes a hybrid magnetic motor 1a and a heat dissipation device 1c, the hybrid magnetic motor 1a constitutes the lower part of the digital motor 1, the heat dissipation device 1c constitutes the upper part of the digital motor 1, the digital motor 1 is provided with an aviation plug 9, and the power is connected through the aviation plug 9, the liquid crystal touch screen 2 is embedded in the front side of the digital motor 1, the bracket 3 is arranged below the digital motor 1 by bolt connection, the centrifugal pump head 4 is arranged below the bracket 3 by bolt connection, the centrifugal pump head 4 includes a pump body 4a, a pump cover 4c, a shaft 4b, the output shaft of the digital motor 1 extends into the interior of the centrifugal pump head 4 and serves as the shaft of the centrifugal pump head 4. The digital motor 1 drives the impeller in the centrifugal pump head 4 to rotate through the shared shaft 4b. The centrifugal pump head 4 has a water inlet 5 on one side and a water outlet 8 on the other side. A multifunctional sensor 7 is provided at the connection between the bracket 3 and the pump cover 4c near the water outlet 8. The probe of the multifunctional sensor 7 extends into the pump cavity formed by the pump body 4a and the pump cover 4c, and is connected to the digital integrated controller in the digital device through the aviation plug 9. The lifting device 6 is connected to the digital motor 1 with bolts. The method includes S101 to S104, specifically:

[0059] S101: Obtain the input power, output flow, and pressure of the digital device through sensors, calculate the energy loss under different working conditions, and establish an energy consumption database based on the calculation results;

[0060] S102: Real-time monitoring of vibration signals and sound pressure signals of digital devices, analysis of frequency components of noise, and adoption of corresponding noise reduction measures based on the energy consumption database;

[0061] S103: Collect historical data during the operation of the digital device and build a fault prediction model, wherein the historical data includes energy consumption data, noise data, temperature data, and pressure data;

[0062] S104: Predicting whether the digital device has a potential fault and the type of the fault based on the fault prediction model, and generating a digital device adjustment plan based on the prediction result.

[0063] The calculation of energy loss under different working conditions includes:

[0064] A1: Use a power sensor to collect the input power of digital devices in real time, and use a flow sensor and pressure sensor to measure the output flow and pressure;

[0065] It's important to understand that pressure is a key parameter for evaluating the operating conditions of digital devices. By measuring pressure, we can understand the operating status of digital devices under specific operating conditions, ensuring they operate within a safe and stable range. While pressure isn't directly involved in calculating output power, in some cases it can affect the density or flow rate of the fluid, indirectly impacting output power. For example, when pressure increases, the density of the fluid may change, affecting output power calculations. Furthermore, measuring pressure can aid in fault prediction and diagnosis. Abnormal pressure in a digital device may indicate a fault or problem, such as a blockage, leak, or component wear. Monitoring pressure changes allows these issues to be promptly identified and addressed, preventing further escalation.

[0066] Specific operating conditions refer to the working state of digital devices under specific operating conditions. Specific conditions include the following aspects:

[0067] (1) Rotational speed or operating frequency: Digital devices may have different energy consumption and noise frequency components at different rotational speeds or operating frequencies. For example, when running at high speeds, higher-frequency mechanical vibrations and fluid noise may be generated.

[0068] (2) Load conditions: When digital devices carry different loads, their energy consumption and noise characteristics will also change. An increase in load may lead to an increase in energy consumption and an increase in noise level;

[0069] (3) Fluid properties: For pump equipment, the properties of the fluid will also affect its energy consumption and noise generation. For example, a fluid with high viscosity may require more energy to transport and may generate greater fluid noise;

[0070] (4) Operating environment: The operating environment of digital devices, such as temperature, humidity, and pressure, may also affect their energy consumption and noise characteristics. For example, a high temperature environment may cause the efficiency of the device to decrease, thereby increasing energy consumption; while pressure fluctuations may cause changes in fluid noise;

[0071] (5) Equipment status: The degree of wear, lubrication condition, loose fasteners, etc. of the equipment will also affect its energy consumption and noise level. For example, worn bearings may generate abnormal mechanical noise and increase energy consumption;

[0072] In summary, in the energy consumption database, specific operating conditions are usually associated with energy loss data. These data may come from laboratory tests, field monitoring, or historical operation records and are used to describe the energy consumption characteristics of the equipment under different operating conditions.

[0073] A2: Calculate the output power P based on the output flow and pressure out , the formula is:

[0074]

[0075] Where ρ represents the liquid density, Q represents the output flow of the digital device, g represents the acceleration of gravity, H represents the head, η f Represents the fluid efficiency coefficient, which refers to the efficiency improvement or loss of the fluid during transmission, and 0≤η f ≤1,η l Represents the energy loss coefficient, which is the energy loss caused by friction, leakage, etc., and 0≤η l ≤1, T represents the current temperature, T0 represents the reference temperature, α represents the temperature influence coefficient, C represents the current concentration, C0 represents the reference concentration, β represents the concentration influence coefficient, δ represents the time decay coefficient, t represents the operating time, and e represents the exponent; the fluid efficiency coefficient is the efficiency of the liquid when it flows in the device; the energy loss coefficient is the energy loss caused by friction and leakage during the operation of the digital device; the temperature influence coefficient is the relative change in output power when the temperature changes; the concentration influence coefficient is the relative change in output power when the concentration changes; the time decay coefficient is the rate at which the performance of the digital device decreases over time;

[0076] In the present invention, the values ​​of fluid efficiency coefficient, energy loss coefficient, temperature influence coefficient, concentration influence coefficient and time decay coefficient are: η f =0.5,η l =0.1, α=0.5, β=0.3, δ=0.4.

[0077] It should be noted that the formula in this application can more accurately predict and evaluate the actual performance of the system by comprehensively considering multiple factors such as fluid density, flow rate, height, efficiency coefficient, energy loss coefficient, temperature, concentration and time. Liquid density ρ, output flow rate Q, head H and gravitational acceleration g jointly determine the basic power output, while the fluid efficiency coefficient η f and energy loss coefficient η lThe introduction of reflects the energy conversion efficiency and loss of the system during the transmission process. When the fluid efficiency coefficient η f When the energy loss coefficient η increases, the overall performance improves. l As it increases, the overall performance decreases; at the same time, and They respectively represent the degree of influence of temperature and concentration on system performance. When the temperature and concentration deviate from the reference values, the system performance will change according to the corresponding coefficient relationship. This change may be positive or negative. Specifically: if α>0, the performance improves with increasing temperature; if β>0, the performance improves with increasing concentration. On the other hand, considering the performance changes of the system over time, the time parameter t is introduced, and the time attenuation coefficient δ reflects the performance degradation of the system due to aging, wear and other factors. In summary, the modified formula provides a comprehensive evaluation of the performance of the fluid transmission system by introducing multiple parameters.

[0078] A3: According to the output power P out and the input power P in , the energy loss under different working conditions is obtained as loss=P in -P out .

[0079] The real-time monitoring of vibration signals and sound pressure signals of digital devices, analysis of frequency components of noise, and adoption of corresponding noise reduction measures in combination with the energy consumption database include:

[0080] B1: Use vibration sensors and acoustic sensors to monitor vibration signals and sound pressure signals in real time, and convert time domain signals into frequency domain signals through Fourier transform;

[0081] Furthermore, the specific steps of B1 include:

[0082] (1) Select vibration sensors and acoustic sensors according to monitoring requirements, and install the sensors on the equipment or environment to be monitored, ensuring that the sensors are firmly fixed and installed in the correct direction;

[0083] (2) Use a data acquisition system to connect the sensor wirelessly to collect vibration signals and sound pressure signals in real time, and ensure that the sampling frequency of the acquisition system is high enough to accurately capture the high-frequency components in the signal;

[0084] (3) Perform preprocessing operations such as filtering and denoising on the collected vibration signals and sound pressure signals to generate preprocessed time domain signals to improve signal quality;

[0085] (4) Use Perform Fourier transform on the preprocessed time domain signal to convert the time domain signal into a frequency domain signal, where X(k) represents the kth sample of the frequency domain signal, x(n) represents the nth sample of the time domain signal, k represents the sample index value in the frequency domain signal, N represents the total number of samples in the time domain signal, n represents the sample index value in the time domain signal, and j represents the imaginary unit.

[0086] B2: Based on the frequency domain signal, analyze the frequency components of the noise and, combined with data from an energy consumption database, identify noise sources of different frequencies and their relationship to energy consumption; the data in the energy consumption database is data related to energy loss and operating conditions;

[0087] B3: Based on the identification results, determine the noise source and take corresponding noise reduction measures. The noise reduction measures include optimizing component structure to reduce mechanical vibration and improving flow channel design to reduce fluid pulsation.

[0088] Furthermore, the specific process of determining the noise source in B3 includes:

[0089] (1) Combining the results of frequency domain signal analysis and energy consumption database matching, possible noise sources are identified; the noise sources are generally divided into machinery, equipment, traffic, and human voice types;

[0090] (2) Use the near-field sound intensity method to determine the specific location of the noise source;

[0091] 1) Prepare the equipment required for near-field sound intensity measurement, such as sound intensity probes and data acquisition systems;

[0092] 2) Identify the noise source to be tested and its surrounding environment, and set up a test area to ensure that there is no external interference during the test;

[0093] 3) Arrange a test grid within the test area, where the density and size of the grid are determined based on the characteristics of the test object and the test accuracy requirements, ensuring that the test grid covers the possible locations of noise sources;

[0094] 4) Place the sound intensity probe at each measurement point on the test grid, measure the sound intensity using I = G × v, and record the sound intensity value and corresponding coordinates of each measurement point, where I represents sound intensity, G represents sound pressure, and v represents particle vibration velocity;

[0095] It is important to know that in actual measurements, the sound intensity probe contains two microphones, which measure the phase difference between the sound pressure and the particle vibration velocity respectively, thereby directly calculating the sound intensity.

[0096] 5) Organize and analyze the measurement results and draw a sound intensity distribution map;

[0097] 6) Identify the specific location of the noise source based on the sound intensity distribution map.

[0098] It is important to understand that sound intensity is the time-averaged rate of sound energy flow per unit area, representing the flow of sound energy. The sound intensity distribution diagram is a graph obtained by measuring and plotting the sound intensity values ​​at each point in a certain area. In the sound intensity distribution diagram, different colors or grayscales usually represent different sound intensity levels. By observing the sound intensity distribution diagram, areas with higher sound intensity values ​​are found. These areas indicate that the sound energy is more concentrated and are the locations of the noise sources.

[0099] (3) Verify and confirm the identified noise sources through data analysis methods to ensure that the identified noise sources are consistent with the actual situation and prepare to take corresponding noise reduction measures.

[0100] The method of analyzing the frequency components of the noise based on the frequency domain signal and identifying the noise sources of different frequencies and their relationship with energy consumption in combination with the data in the energy consumption database includes:

[0101] B2.1: Use an acoustic sensor to monitor the sound pressure signal of the digital device in real time, and convert the sound pressure signal into a frequency domain signal through Fourier transform;

[0102] B2.2: Perform spectral analysis on the frequency domain signal to identify the main noise frequency components and evaluate the noise level of each frequency component. Spectral analysis is prior art in this field and does not constitute an inventive solution of this application, and is not described in detail here.

[0103] B2.3: Extract energy consumption data related to the current operating conditions from the energy consumption database and analyze the potential correlation between energy consumption data and noise frequency components;

[0104] B2.4: Based on known noise frequency components and energy consumption data, combined with the operating principles and structural characteristics of digital devices, identify possible noise sources; these sources include mechanical vibration, fluid pulsation, and electromagnetic interference.

[0105] Furthermore, identifying possible noise sources is to identify preliminary noise sources. The specific process includes:

[0106] (1) Obtaining noise frequency component data and energy consumption data of digital devices during operation;

[0107] (2) Analyze the structural characteristics of digital devices and pay attention to possible vibration sources and noise generation points, such as motor bearings, gear transmissions, and fluid channels;

[0108] (3) Matching the collected noise frequency components with the structural characteristics of the digital device. For example, high-frequency noise may come from motor bearings or high-speed rotating parts, while low-frequency noise may be related to fluid flow or mechanical vibration;

[0109] (4) Consider the relationship between noise of different frequencies and energy consumption. High energy consumption may be accompanied by high noise. This is because the load on the motor or mechanical components increases under high energy consumption, resulting in increased vibration and noise.

[0110] (5) Based on the analysis results in (3) and (4), identify possible noise sources.

[0111] B2.5: For each identified noise source, analyze the relationship between noise sources of different frequencies and energy consumption.

[0112] Furthermore, the specific steps of B2.5 include:

[0113] (1) Label and classify each identified noise source;

[0114] (2) Use acoustic measuring instruments to measure the frequency characteristics of each noise source, including low-frequency, mid-frequency, and high-frequency components, and record the sound pressure levels at different frequencies. Among them, G1 represents the actual sound pressure, and G0 represents the reference sound pressure;

[0115] (3) For equipment or systems directly related to noise sources, such as mechanical equipment, fluid systems, and transportation vehicles, measure their energy consumption and record the energy consumption data;

[0116] (4) Comparing and analyzing the frequency characteristics of the noise source with the energy consumption data to find the correlation between noise sources of different frequencies and energy consumption. The correlation calculation is a prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0117] (5) Based on the analysis results, draw conclusions and explain the relationship between noise sources of different frequencies and energy consumption.

[0118] The step of extracting energy consumption data related to the current working condition from the energy consumption database and analyzing the potential correlation between the energy consumption data and the noise frequency components includes:

[0119] B2.31: Obtain the current operating status of digital devices;

[0120] B2.32: Based on the operating conditions, query and extract corresponding energy consumption data from the energy consumption database, and pre-process the extracted energy consumption data; the energy consumption data includes electrical energy consumption, mechanical energy loss, and thermal energy loss;

[0121] B2.33: Obtain the noise frequency components and corresponding noise levels of the digital device, and analyze the potential correlation between the energy consumption data and the noise frequency components using a correlation analysis method. The potential correlation refers to whether there is a correlation between the energy consumption parameters and the frequency noise, and the correlation may be positive or negative. The correlation analysis method is prior art in this field and does not constitute an inventive solution of this application, and is not described in detail here.

[0122] B2.34: Based on the analysis results, explain the physical significance of the relationship between energy consumption data and noise frequency components, and provide optimization suggestions based on the analysis results.

[0123] The construction process of the fault prediction model includes:

[0124] C1: Collect historical data during the operation of digital devices and label the historical data, including energy consumption data, noise data, temperature data, and pressure data;

[0125] C2: Load a pre-built neural network model, use the labeled historical data to train the pre-built neural network model, and generate a trained fault prediction model. The neural network model is the existing technology content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0126] The method of predicting whether a digital device has a potential fault and the type of the fault based on the fault prediction model, and generating a digital device adjustment plan based on the prediction result, includes:

[0127] D1: Real-time collection and pre-processing of operating data of digital devices; the operating data of digital devices includes flow, sound, vibration, temperature, current, electrical protection element tripping, and controller alarm;

[0128] D2: Use signal processing methods to extract features from the pre-processed operating data to generate digital device operating feature data;

[0129] Furthermore, the specific steps of D2 include:

[0130] (1) Obtaining pre-processed operating data;

[0131] (2) Extracting features from the pre-processed operating data in the time domain, specifically calculating the mean of the pre-processed operating data and analyzing the waveform characteristics of the signal, such as waveform symmetry and periodicity;

[0132] (3) Perform Fourier transform on the pre-processed running data to convert the time domain signal into the frequency domain signal, extract features in the frequency domain, and analyze the spectrum characteristics;

[0133] (4) Integrate the features extracted in (2) and (3) to form operational feature data;

[0134] (5) Optimizing the operational feature data using a principal component analysis method to remove redundant or irrelevant features. The principal component analysis method is prior art in this field and is not an inventive solution of the present application, and is not described in detail here.

[0135] (6) The optimized operation characteristic data is used as the operation characteristic data of the digital device, and the operation characteristic data of the digital device is marked.

[0136] D3: Inputting the digital device operation characteristic data into a trained fault prediction model. The fault prediction model predicts whether the digital device has a potential fault and the type of fault based on the matching degree between the digital device operation characteristic data and the historical fault data characteristics.

[0137] D4: The fault prediction model issues a warning message after predicting a possible fault, and the operator responds immediately and generates a digital equipment adjustment plan through threshold judgment.

[0138] The step of generating a digital device adjustment plan by threshold determination includes:

[0139] D4.1: Determine whether energy loss exceeds a preset threshold. If so, automatically adjust the operating parameters of the digital device.

[0140] D4.2: Determine whether the noise exceeds the preset noise tolerance range. If so, automatically activate the noise reduction device, which includes a shock-absorbing pad and a muffler. Simultaneously, provide a noise control solution based on the noise frequency analysis results.

[0141] D4.3: Determine the fault type based on the output result of the fault prediction model, and automatically adjust the operating mode of the digital device based on the fault type determination result, including reducing the operating speed and switching to backup components.

[0142] Example 2

[0143] See also Figure 4 Another embodiment of the present invention provides a digital device fault prediction system, comprising:

[0144] Energy consumption analysis module, noise analysis module, fault prediction module, noise reduction control module, fault handling module;

[0145] Energy consumption analysis module, used to collect the input power, output flow and pressure of digital equipment in real time, calculate the energy loss under different working conditions, and establish an energy consumption database;

[0146] Noise analysis module, used to monitor vibration signals and sound pressure signals in real time, analyze the frequency components of noise, identify noise sources and their relationship with energy consumption;

[0147] The fault prediction module trains a neural network model based on collected historical data, and predicts potential faults and fault types of digital devices in real time, and automatically adjusts operating parameters based on the prediction results;

[0148] The noise reduction control module automatically activates the noise reduction device based on the noise reduction analysis results and provides noise control solutions;

[0149] The fault handling module is used to automatically adjust the operation mode of the digital device according to the fault type after the fault prediction model issues an early warning, and record the fault handling process and results.

[0150] The energy consumption analysis module includes: power acquisition unit, measurement unit, energy consumption calculation unit, and database storage unit;

[0151] The power acquisition unit collects the input power of digital devices in real time through power sensors, providing basic energy consumption data during the operation of digital devices;

[0152] A measuring unit, which measures output flow and pressure through a flow sensor and a pressure sensor;

[0153] The energy consumption calculation unit calculates the output power according to the input power, output flow and pressure, and calculates the energy loss.

[0154] The database storage unit is used to establish an energy consumption database and store energy consumption data under different working conditions.

[0155] The noise analysis module includes: signal monitoring unit, frequency domain analysis unit, and correlation analysis unit;

[0156] The signal monitoring unit uses vibration sensors and acoustic sensors to monitor vibration signals and sound pressure signals in real time and obtain the original data of noise signals;

[0157] The frequency domain analysis unit converts the time domain signal into the frequency domain signal through Fourier transform, analyzes the frequency components of the noise, and identifies the noise sources of different frequencies;

[0158] The correlation analysis unit combines the data in the energy consumption database to identify the noise source and its relationship with energy consumption.

[0159] The fault prediction module includes: historical data collection unit, model training unit, prediction unit, and early warning unit;

[0160] Historical data collection unit, used to collect historical data during the operation of digital equipment, including energy consumption data, noise data, temperature data, and pressure data, to provide data support for the training of neural network models;

[0161] The model training unit uses the collected historical data to train the pre-built neural network model to generate a trained fault prediction model;

[0162] The prediction unit is used to collect the current operating data of the digital device in real time and input it into the trained fault prediction model to predict whether there is a potential fault in the digital device and the type of fault;

[0163] The early warning unit will issue early warning information in a timely manner when a possible failure is predicted, and automatically adjust the operating parameters of the digital equipment according to the energy consumption analysis results to improve the reliability and operating efficiency of the equipment.

[0164] The fault processing module includes: a fault response unit, an operation mode adjustment unit, and a fault recording unit;

[0165] Fault response unit: When the fault prediction model issues an early warning, the operator responds immediately;

[0166] An operating mode adjustment unit automatically adjusts the operating mode of digital devices according to the type of fault, including reducing operating speed and switching to spare parts;

[0167] The fault recording unit is used to automatically record the fault handling process and results.

[0168] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention. These are all protected by the present invention.

[0169] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A digital device fault prediction method, characterized in that: include: The input power, output flow and pressure of digital devices are obtained through sensors, and the energy loss under different working conditions is calculated. Based on the calculation results, an energy consumption database is established; Real-time monitoring of vibration signals and sound pressure signals of digital equipment, analysis of noise frequency components, and adoption of corresponding noise reduction measures based on energy consumption database; Collect historical data during the operation of digital devices and build a fault prediction model. The historical data includes energy consumption data, noise data, temperature data, and pressure data; Predicting whether a digital device has a potential fault and the type of fault according to the fault prediction model, and generating a digital device adjustment plan based on the prediction result; The calculation of energy loss under different working conditions includes: Use power sensors to collect the input power of digital devices in real time, and use flow sensors and pressure sensors to measure the output flow and pressure; According to the output flow and pressure, calculate the output power P out , the formula is: Where ρ represents the liquid density, Q represents the output flow of the digital device, g represents the acceleration of gravity, H represents the head, η f represents the fluid efficiency coefficient, η l represents the energy loss coefficient, T represents the current temperature, T0 represents the reference temperature, α represents the temperature influence coefficient, C represents the current concentration, C0 represents the reference concentration, β represents the concentration influence coefficient, δ represents the time attenuation coefficient, t represents the operating time, and e represents the exponent; According to the output power P out and the input power P in , the energy loss under different working conditions is obtained as loss=P in -P out .

2. A digital device fault prediction method according to claim 1, characterized in that: The real-time monitoring of vibration signals and sound pressure signals of digital devices, analysis of frequency components of noise, and adoption of corresponding noise reduction measures in combination with the energy consumption database include: Use vibration sensors and acoustic sensors to monitor vibration signals and sound pressure signals in real time, and convert time domain signals into frequency domain signals through Fourier transform; Analyzing the frequency components of the noise based on the frequency domain signal and identifying the noise sources of different frequencies and their relationship with energy consumption in combination with data in an energy consumption database; the data in the energy consumption database is data related to energy loss and operating conditions; Based on the identification results, the noise source is determined and corresponding noise reduction measures are taken. The noise reduction measures include optimizing component structure to reduce mechanical vibration and improving flow channel design to reduce fluid pulsation.

3. A digital device fault prediction method according to claim 2, characterized in that: The construction process of the fault prediction model includes: Collect historical data during the operation of digital devices and label the historical data, including energy consumption data, noise data, temperature data, and pressure data; Load the pre-built neural network model and use the labeled historical data to train the pre-built neural network model to generate a trained fault prediction model.

4. A digital device fault prediction method according to claim 3, characterized in that: The method of predicting whether a digital device has a potential fault and the type of the fault based on the fault prediction model, and generating a digital device adjustment plan based on the prediction result, includes: Real-time collection and pre-processing of operating data of digital devices; the operating data of digital devices includes flow, sound, vibration, temperature, current, electrical protection element tripping, and controller alarm; Use signal processing methods to extract features from the pre-processed operating data to generate digital device operating feature data; Inputting the digital device operation characteristic data into a trained fault prediction model, the fault prediction model predicts whether the digital device has a potential fault and the type of fault based on the matching degree between the digital device operation characteristic data and the historical fault data characteristics; The fault prediction model issues a warning message after predicting a fault, and the operator responds immediately and generates a digital equipment adjustment plan through threshold judgment.

5. A digital device fault prediction method according to claim 4, characterized in that: The step of generating a digital device adjustment plan by threshold determination includes: determining whether the energy loss exceeds a preset threshold, and automatically adjusting the operating parameters of the digital device if the energy loss exceeds the preset threshold; Determine whether the noise exceeds the preset noise tolerance range. If so, automatically activate the noise reduction device, which includes a shock-absorbing pad and a muffler. At the same time, provide a noise control plan based on the noise frequency analysis results; The fault type is determined according to the output result of the fault prediction model, and the operation mode of the digital device is automatically adjusted according to the determination result of the fault type, including reducing the operation speed and switching to spare components.

6. A digital device fault prediction method according to claim 5, characterized in that: The method of analyzing the frequency components of the noise based on the frequency domain signal and identifying the noise sources of different frequencies and their relationship with energy consumption in combination with the data in the energy consumption database includes: Monitoring the sound pressure signal of the digital device in real time through an acoustic sensor, and converting the sound pressure signal into a frequency domain signal through Fourier transform; performing spectrum analysis on the frequency domain signal, identifying the main frequency components of the noise, and evaluating the noise level of each frequency component; Extract energy consumption data related to the current working conditions from the energy consumption database and analyze the potential correlation between the energy consumption data and the noise frequency components; Identify noise sources based on known noise frequency components and energy consumption data, combined with the working principles and structural characteristics of digital devices; the noise sources include mechanical vibration, fluid pulsation, and electromagnetic interference; For each identified noise source, the relationship between the noise source at different frequencies and energy consumption is analyzed.

7. A digital device fault prediction method according to claim 6, characterized in that: The step of extracting energy consumption data related to the current working condition from the energy consumption database and analyzing the potential correlation between the energy consumption data and the noise frequency components includes: Get the current operating status of the digital device; According to the operating conditions, query and extract corresponding energy consumption data from the energy consumption database, and pre-process the extracted energy consumption data; the energy consumption data includes electrical energy consumption, mechanical energy loss, and thermal energy loss; Obtaining the noise frequency components and corresponding noise levels of the digital device, and analyzing the potential correlation between the energy consumption data and the noise frequency components using a correlation analysis method; the potential correlation refers to whether there is a correlation between the energy consumption parameters and the frequency noise, and the correlation includes positive correlation and negative correlation; Based on the analysis results, the physical meaning of the correlation between energy consumption data and noise frequency components is explained, and optimization suggestions are provided based on the analysis results.

8. A digital device fault prediction system, used to implement a digital device fault prediction method according to any one of claims 1 to 7, characterized in that: include: Energy consumption analysis module, noise analysis module, fault prediction module, noise reduction control module, fault handling module; The energy consumption analysis module is used to collect the input power, output flow and pressure of digital devices in real time, calculate the energy loss under different working conditions, and establish an energy consumption database; The noise analysis module is used to monitor vibration signals and sound pressure signals in real time, analyze the frequency components of noise, identify noise sources and their relationship with energy consumption; The fault prediction module trains a neural network model based on the collected historical data, and predicts the potential faults and fault types of digital devices in real time, and automatically adjusts the operating parameters based on the prediction results; The noise reduction control module automatically activates the noise reduction device based on the noise reduction analysis results and provides a noise control solution; The fault processing module is used to automatically adjust the operation mode of the digital device according to the fault type after the fault prediction model issues an early warning, and record the fault processing process and processing results.

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