Diesel generating set fault prevention method and system with real-time sensing function

Abstract

The invention relates to the technical field of state monitoring and fault diagnosis of electrical equipment, and discloses a real-time sensing diesel generating set fault prevention method and system, and the method comprises the steps: obtaining the operation data of a diesel generating set; according to the mechanical vibration data, high-frequency features are extracted, and a mechanical fault trend is obtained; according to the operation data of the diesel generating set, performing trend analysis by adopting Fourier transform to obtain an electrical system fault trend; performing time sequence analysis according to the control signal data to obtain a control signal fault trend; carrying out collaborative analysis according to the load change data to obtain a load influence factor; and according to the operation data of the diesel generating set, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influence factor, carrying out correlation analysis and establishing a comprehensive fault prediction model to obtain a fault prevention strategy. The method can realize global dynamic fault prevention of the diesel generating set.

Description

Technical Field

[0001] The present invention relates to the technical field of state monitoring and fault diagnosis of electrical equipment, and in particular to a real-time sensing diesel generator set fault prevention method and system. Background Art

[0002] At present, diesel generator sets are widely used in industry, commerce and critical infrastructure. As important backup or main power supply equipment, their operating stability and reliability directly affect the safety of power supply. However, due to the influence of load changes, environmental factors and mechanical wear during long-term operation, diesel generator sets are prone to failure, resulting in sudden shutdown, which seriously affects production and power supply stability. Therefore, how to achieve early prevention of diesel generator set failures and improve equipment reliability and operating efficiency has become the focus of current technical research.

[0003] In one existing technology, regular manual inspections, fixed sensor monitoring, and statistical analysis methods based on historical data are used to evaluate the health status of diesel generator sets. For example, vibration sensors, temperature sensors, and oil pressure monitoring devices are installed to collect unit operating data, and fault judgment is made based on preset thresholds or empirical rules. Some systems use fault diagnosis methods based on offline analysis to model fault modes through historical data to predict possible faults, and combine signal processing methods to extract features from vibration signals, noise signals, or emission data of diesel generator sets to identify abnormal operating conditions, and use statistical learning or mechanism-based modeling methods for fault classification and early warning. In addition, a centralized fault management system based on remote monitoring is used to analyze the operating status of diesel generator sets through cloud data to provide operation and maintenance guidance.

[0004] However, the manual inspection cycle is long, and it is difficult to detect hidden dangers in time. The layout range of fixed sensors is limited, and it is impossible to fully perceive the operating status of the unit. The modeling method based on offline analysis is difficult to dynamically adapt to complex working conditions. The signal processing method is prone to recognition errors when facing noise interference or multivariable coupling characteristics. The remote monitoring system may cause inaccurate fault prediction in the case of data transmission delays or insufficient model generalization capabilities. It can be seen that there is a problem in the existing technology that it is difficult to achieve global dynamic fault prevention of diesel generator sets. Summary of the invention

[0005] The present invention provides a real-time sensing diesel generator set fault prevention method and system to achieve global dynamic fault prevention of the diesel generator set.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a real-time sensing diesel generator set fault prevention method, comprising: Obtain diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; According to the mechanical vibration data, high-frequency features are extracted by wavelet transform to obtain mechanical failure trends; Based on the diesel generator set operation data, Fourier transform is used to perform trend analysis to obtain the electrical system fault trend; Perform time series analysis based on the control signal data, extract the time series variation law, and obtain the control signal failure trend; According to the load change data, a multivariate collaborative analysis is performed in combination with the ambient temperature data and the humidity information data to obtain a load influencing factor; According to the diesel generator set operation data, the mechanical failure trend, the electrical system failure trend, the control signal failure trend and the load influencing factor, a correlation analysis is performed and a comprehensive failure prediction model is established to obtain a failure prevention strategy.

[0007] In an optional embodiment, the acquisition of diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data, includes: Obtaining original mechanical vibration data, original electrical parameter data, original control signal data, original load change data, original ambient temperature data and original humidity information data; The original operation data of the diesel generator set is defined to include the original mechanical vibration data, the original electrical parameter data, the original control signal data, the original load change data, the original ambient temperature data and the original humidity information data; Perform integrity analysis based on the original operating data of the diesel generator set, and use an interpolation algorithm to fill in missing values ​​to obtain complete operating data of the diesel generator set; According to the complete operation data of the diesel generator set, data format standardization processing is performed to obtain standardized operation data of the diesel generator set; The diesel generator set operation data includes mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data.

[0008] In an optional implementation, extracting high-frequency features using wavelet transform based on the mechanical vibration data to obtain a mechanical failure trend includes: According to the mechanical vibration data, wavelet transform is used to extract features to obtain high-frequency feature components; According to the high-frequency characteristic component, combined with a preset mechanical fault characteristic library, frequency characteristic matching of mechanical fault types is performed to obtain potential mechanical fault types; According to the mechanical vibration data, the resonance frequency of each component of the mechanical structure is calculated, and the corresponding relationship between the mechanical wear and the vibration spectrum is analyzed to obtain the degree of mechanical wear; Calculating the acceleration characteristics of the mechanical looseness fault according to the mechanical vibration data, performing acceleration time domain signal analysis, and obtaining the mechanical looseness risk; According to the potential mechanical failure type, the mechanical wear degree and the mechanical looseness risk, combined with a preset mechanical failure feature library, a comprehensive feature matching of mechanical failure trends is performed to obtain a mechanical failure trend.

[0009] In an optional implementation, the trend analysis is performed using Fourier transform based on the diesel generator set operation data to obtain the electrical system fault trend, including: According to the electrical parameter data, Fourier transform is used to analyze and obtain harmonic components; According to the harmonic components, combined with a preset electrical fault feature library, electrical fault type harmonic feature matching is performed to obtain potential fault types of the electrical system; Perform insulation aging rate analysis based on the ambient temperature data and the humidity information data to obtain an insulation aging trend; According to the insulation aging trend and the potential fault type of the electrical system, combined with a preset electrical fault feature library, electrical fault trend comprehensive feature matching is performed to obtain the electrical fault trend.

[0010] In an optional implementation, performing time series analysis based on the control signal data, extracting time series variation rules, and obtaining control signal fault trends include: According to the control signal data, time series decomposition is used to perform feature extraction to obtain the time series variation law of the control signal; According to the control signal timing variation rule, matching is performed in combination with the fault waveform features in the preset control signal fault feature library to obtain the control signal abnormality type; According to the abnormal type of the control signal, combined with the operating state of the control system, the response time and overshoot of the control signal are analyzed to obtain the dynamic stability of the control system; According to the control signal timing variation rule, the control signal abnormality type and the control system dynamic stability, combined with a preset control signal fault feature library, control signal fault trend comprehensive feature matching is performed to obtain the control signal fault trend.

[0011] In an optional implementation, the load change data is combined with the ambient temperature data and the humidity information data to perform a multivariate collaborative analysis to obtain a load impact factor, including: Extracting features based on the load change data to obtain a load change feature vector including load size, change rate and periodicity features; According to the load change characteristic vector, a finite element analysis method is used to perform a mechanical stress distribution analysis to obtain a mechanical stress over-limit risk; According to the load variation characteristic vector, combined with the tribology model, the mechanical wear rate under different load conditions is analyzed to obtain the mechanical wear aggravation trend; According to the load change characteristic vector, a modal analysis method is used to perform wavelet analysis on the mutation signal, extract transient vibration characteristics, and obtain the mechanical resonance risk; Based on the risk of mechanical stress exceeding the limit, the tendency of mechanical wear aggravation, the risk of mechanical resonance, the ambient temperature data and the humidity information data, a support vector machine algorithm is used to perform multivariate collaborative analysis to obtain load influencing factors.

[0012] In an optional implementation, the correlation analysis is performed based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor, and a comprehensive fault prediction model is established to obtain a fault prevention strategy, including: According to the mechanical failure trend, the electrical system failure trend, the control signal failure trend and the load impact factor, a correlation impact analysis is performed using multi-source data fusion technology to obtain a multi-dimensional correlation matrix; According to the multi-dimensional correlation matrix, a deep neural network algorithm is used to train and model a fault probability prediction model; According to the fault probability prediction model, combined with a preset maintenance cost and risk consequence weight table, the model parameters are dynamically optimized to obtain a comprehensive fault prediction model; The operating data of the diesel generator set is input into a comprehensive fault prediction model to obtain a fault prevention strategy.

[0013] In a second aspect, the present invention provides a real-time sensing diesel generator set fault prevention system, comprising: A data acquisition module is used to acquire the operating data of the diesel generator set, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; A mechanical inspection module, used to extract high-frequency features using wavelet transform according to the mechanical vibration data to obtain mechanical failure trends; An electrical analysis module, used to perform trend analysis using Fourier transform based on the operating data of the diesel generator set to obtain the fault trend of the electrical system; A control diagnosis module, used to perform time series analysis based on the control signal data, extract the time series variation law, and obtain the control signal fault trend; A load analysis module, used to perform multivariate collaborative analysis based on the load change data in combination with the ambient temperature data and the humidity information data to obtain a load impact factor; The result output module is used to perform correlation analysis and establish a comprehensive fault prediction model based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor to obtain a fault prevention strategy. In a third aspect, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the real-time perception diesel generator set fault prevention method described in any one of the above.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the real-time perception diesel generator set fault prevention methods described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a real-time perception method for preventing diesel generator set faults, comprising obtaining diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; based on the mechanical vibration data, using wavelet transform to extract high-frequency features to obtain a mechanical fault trend; based on the diesel generator set operation data, using Fourier transform to perform trend analysis to obtain an electrical system fault trend; based on the control signal data, performing time series analysis to extract time series change rules to obtain a control signal fault trend; based on the load change data, performing multivariate collaborative analysis in combination with the ambient temperature data and the humidity information data to obtain a load influencing factor; based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor, performing correlation analysis and establishing a comprehensive fault prediction model to obtain a fault prevention strategy.

[0016] The present invention realizes comprehensive perception of the operating status of the diesel generator set by constructing a multi-source data fusion analysis system, and constructs an accurate fault prediction model based on feature extraction and trend analysis of different types of data. First, the mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data of the diesel generator set are collected to ensure the comprehensiveness of the monitoring data. Then, the high-frequency features of the mechanical vibration data are extracted by wavelet transform to identify potential mechanical fault trends; the electrical parameter data is analyzed in the frequency domain by Fourier transform to reveal the electrical system fault trends that may exist in the electrical system; the control signal data is processed by time series analysis method to extract the time series change law, so as to identify the abnormal trend of the control system; combined with load change data, ambient temperature and humidity information, multivariate collaborative analysis is carried out to obtain load influencing factors. Based on the above analysis results, correlation analysis is carried out to establish a comprehensive fault prediction model to fuse multi-source data, improve the accuracy of fault prevention, and obtain a fault prevention strategy. The present invention can adaptively adjust the early warning mechanism and maintenance measures according to the different operating states of the diesel generator set, making up for the limitations of the prior art in fault prevention, thereby realizing the global dynamic fault prevention of the diesel generator set. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for preventing diesel generator set faults in real time according to a first embodiment of the present invention; Figure 2 It is a structural schematic diagram of a real-time sensing diesel generator set fault prevention system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a real-time sensing diesel generator set fault prevention method, comprising the following steps: S11, obtaining the diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; S12, extracting high-frequency features using wavelet transform according to the mechanical vibration data to obtain a mechanical failure trend; S13, performing trend analysis using Fourier transform based on the diesel generator set operation data to obtain an electrical system fault trend; S14, performing time series analysis according to the control signal data, extracting the time series variation rule, and obtaining the control signal fault trend; S15, performing multivariate collaborative analysis based on the load change data in combination with the ambient temperature data and the humidity information data to obtain a load impact factor; S16, performing correlation analysis and establishing a comprehensive fault prediction model based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor to obtain a fault prevention strategy.

[0020] In step S11, it is necessary to obtain the operating data of the diesel generator set, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data.

[0021] In one implementation, the obtaining of diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data, includes: Obtain original mechanical vibration data, original electrical parameter data, original control signal data, original load change data, original ambient temperature data and original humidity information data; define the original operation data of the diesel generator set to include the original mechanical vibration data, the original electrical parameter data, the original control signal data, the original load change data, the original ambient temperature data and the original humidity information data; perform integrity analysis based on the original operation data of the diesel generator set, use an interpolation algorithm to fill in missing values, and obtain the complete operation data of the diesel generator set; perform data format standardization processing based on the complete operation data of the diesel generator set to obtain standardized diesel generator set operation data; the diesel generator set operation data includes mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data.

[0022] It is worth noting that the original mechanical vibration data can be used to measure the vibration of the mechanical system through accelerometers, displacement sensors and other devices, and record the amplitude, frequency and other parameters of the vibration; the original electrical parameter data can be obtained through electrical parameter measuring instruments, such as voltmeters, ammeters, power analyzers, etc., to obtain the electrical parameter data such as voltage, current, power, etc. in the circuit; the original control signal data can be recorded by the data acquisition system The signal sent by the control system, such as PWM signals, analog signals, etc.; the original load change data can be collected in real time through sensors; the original ambient temperature data can be measured by temperature sensors, such as thermocouples, thermistors, etc., to measure the change of ambient temperature; the original humidity information data can be obtained by humidity sensors. Environmental humidity information. Integrity analysis can identify missing values ​​in the data, including checking whether the data record is continuous and whether there is data loss; after identifying the missing values, an interpolation algorithm is used to fill these missing values. There are many interpolation algorithms, such as linear interpolation, Lagrange interpolation, KNN interpolation, etc., which are not required in the embodiments of the present invention. Data standardization is the process of normalizing and unifying data so that data from different sources, in different formats, and with different precisions are consistent and comparable during analysis and application, thereby improving the quality and credibility of the data.

[0023] In step S12, it is necessary to extract high-frequency features using wavelet transform based on the mechanical vibration data to obtain the mechanical failure trend.

[0024] In one implementation, extracting high-frequency features using wavelet transform based on the mechanical vibration data to obtain a mechanical failure trend includes: Based on the mechanical vibration data, wavelet transform is used to perform feature extraction to obtain high-frequency feature components; based on the high-frequency feature components, in combination with a preset mechanical fault feature library, frequency feature matching of mechanical fault types is performed to obtain potential mechanical fault types; based on the mechanical vibration data, the resonance frequency of each component of the mechanical structure is calculated, and the corresponding relationship between mechanical wear and vibration spectrum is analyzed to obtain the degree of mechanical wear; based on the mechanical vibration data, the acceleration characteristics of mechanical looseness faults are calculated, and acceleration time domain signal analysis is performed to obtain the risk of mechanical looseness; based on the potential mechanical fault type, the degree of mechanical wear and the mechanical looseness risk, in combination with a preset mechanical fault feature library, comprehensive feature matching of mechanical fault trends is performed to obtain mechanical fault trends.

[0025] It should be noted that wavelet transform can generate wavelet coefficients at different scales. These coefficients reflect the characteristics of the signal at different scales and positions, which is convenient for screening and extracting different signal components. The mechanical fault trend refers to the type of faults that may occur in mechanical parts during the operation of the diesel generator set and their development trends, including but not limited to mechanical wear, looseness, breakage, etc. The acquisition of mechanical fault trends depends on the in-depth analysis of mechanical vibration data. Specifically, high-frequency features are extracted through wavelet transform, and vibration signals are decomposed to identify different frequency components, thereby revealing abnormal vibration patterns. By matching the extracted high-frequency feature components with the preset mechanical fault feature library, the potential mechanical fault type can be identified, and the degree of mechanical wear can be analyzed by combining the resonance frequency calculation, and the risk of mechanical looseness can be determined by acceleration time domain signal analysis. Finally, the mechanical fault trend is obtained through comprehensive fault feature matching. The mechanical fault trend is used for real-time health monitoring of diesel generator sets, providing data support for subsequent fault prediction and prevention strategies to ensure the stable operation of the equipment.

[0026] In step S13, it is necessary to perform trend analysis using Fourier transform based on the operating data of the diesel generator set to obtain the fault trend of the electrical system.

[0027] In one implementation, the trend analysis is performed using Fourier transform based on the diesel generator set operation data to obtain the electrical system fault trend, including: According to the electrical parameter data, Fourier transform is used for analysis to obtain harmonic components; according to the harmonic components, in combination with a preset electrical fault feature library, electrical fault type harmonic feature matching is performed to obtain potential fault types of the electrical system; according to the ambient temperature data and the humidity information data, insulation aging rate analysis is performed to obtain insulation aging trends; according to the insulation aging trends and potential fault types of the electrical system, in combination with a preset electrical fault feature library, electrical fault trend comprehensive feature matching is performed to obtain electrical fault trends.

[0028] It should be noted that the electrical system fault trend refers to the types of faults that may occur in the electrical system during the operation of the diesel generator set and their evolution trends, including but not limited to harmonic distortion, insulation aging, short circuit, overload and other problems. The acquisition of electrical system fault trends depends on the spectral analysis of electrical parameter data. Specifically, the electrical signal is decomposed through Fourier transform to identify the harmonic components. The potential fault types of the electrical system are obtained by matching the extracted harmonic components with the preset electrical fault feature library. The insulation aging rate is analyzed in combination with the ambient temperature and humidity information to evaluate the long-term health of the electrical system. Finally, the electrical fault trend is obtained through comprehensive feature matching. The electrical fault trend can be used for electrical health monitoring of diesel generator sets, providing support for fault prediction and prevention strategies, and ensuring the stability and reliability of system operation.

[0029] In step S14, it is necessary to perform time series analysis based on the control signal data, extract the time series variation law, and obtain the control signal fault trend.

[0030] In one implementation, performing time series analysis based on the control signal data, extracting time series variation rules, and obtaining control signal fault trends include: According to the control signal data, time series decomposition is adopted to perform feature extraction to obtain the control signal timing variation law; according to the control signal timing variation law, the fault waveform features in the preset control signal fault feature library are matched to obtain the control signal abnormality type; according to the control signal abnormality type, combined with the operating state of the control system, the response time and overshoot of the control signal are analyzed to obtain the control system dynamic stability; according to the control signal timing variation law, the control signal abnormality type and the control system dynamic stability, combined with the preset control signal fault feature library, the control signal fault trend comprehensive feature matching is performed to obtain the control signal fault trend.

[0031] It should be noted that the control signal fault trend refers to the signal anomaly in the diesel generator control system and its trend over time, including but not limited to signal delay, loss, fluctuation abnormality, overshoot and undershoot. The acquisition of the control signal fault trend depends on the time series analysis of the control signal data. Specifically, the time series change law of the signal is extracted by time series decomposition, and the long-term trend, periodic fluctuation and sudden abnormality of the signal characteristics are identified. By matching the extracted time series features with the preset control signal fault feature library, the control signal abnormality type is obtained, and combined with the operating status of the diesel generator set, the dynamic response characteristics of the control system are analyzed, such as signal response time, overshoot, etc., to obtain the dynamic stability of the control system. Finally, the control signal fault trend is obtained through comprehensive feature matching. The control signal fault trend can be used for the health monitoring of the control system of the diesel generator set, providing data support for abnormal signal detection and fault prevention, ensuring the stability and response accuracy of the control system, and improving the overall reliability of the diesel generator set.

[0032] In step S15, it is necessary to perform multivariate collaborative analysis based on the load change data in combination with the ambient temperature data and the humidity information data to obtain a load impact factor.

[0033] In one implementation, performing multivariate collaborative analysis based on the load change data in combination with the ambient temperature data and the humidity information data to obtain a load impact factor includes: Based on the load change data, feature extraction is performed to obtain a load change feature vector including load size, change rate and periodic characteristics; based on the load change feature vector, a finite element analysis method is used to perform a mechanical stress distribution analysis to obtain the risk of mechanical stress exceeding the limit; based on the load change feature vector, in combination with a tribological model, a mechanical wear rate analysis is performed under different load conditions to obtain the trend of mechanical wear aggravation; based on the load change feature vector, a modal analysis method is used to perform a wavelet analysis on the mutation signal to extract transient vibration features to obtain the risk of mechanical resonance; based on the risk of mechanical stress exceeding the limit, the trend of mechanical wear aggravation, the mechanical resonance risk, the ambient temperature data and the humidity information data, a support vector machine algorithm is used to perform a multivariate collaborative analysis to obtain the load influencing factor.

[0034] It should be noted that the load influence factor represents the degree to which the mechanical and electrical performance of the diesel generator set is affected by environmental factors under different load conditions. The acquisition of the load influence factor is based on multivariate collaborative analysis, which comprehensively considers the dynamic influence of the load change characteristic vector, the risk of mechanical stress overrun, the trend of mechanical wear aggravation, the risk of mechanical resonance, the ambient temperature data and the humidity information data. The load change characteristic vector is obtained by the feature extraction method, and the risk of mechanical stress overrun, the trend of mechanical wear aggravation and the risk of mechanical resonance are evaluated respectively in combination with finite element analysis, tribological modeling and modal analysis. At the same time, the ambient temperature data and the humidity information data are used in combination with the machine learning algorithm for multivariate analysis to calculate the comprehensive load influence factor. The load influence factor is used to evaluate the reliability of the diesel generator set in actual operation, identify potential mechanical and electrical failure risks in advance, thereby optimizing the load management strategy and improving the stability and service life of the unit operation.

[0035] In step S16, it is necessary to perform correlation analysis and establish a comprehensive fault prediction model based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor to obtain a fault prevention strategy.

[0036] In one implementation, the correlation analysis is performed based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor, and a comprehensive fault prediction model is established to obtain a fault prevention strategy, including: According to the mechanical failure trend, the electrical system failure trend, the control signal failure trend and the load influence factor, multi-source data fusion technology is used to perform correlation impact analysis to obtain a multi-dimensional correlation matrix; according to the multi-dimensional correlation matrix, a deep neural network algorithm is used to perform training and modeling to obtain a fault probability prediction model; according to the fault probability prediction model, combined with a preset maintenance cost and risk consequence weight table, model parameters are dynamically optimized to obtain a comprehensive fault prediction model; the operating data of the diesel generator set is input into the comprehensive fault prediction model to obtain a fault prevention strategy.

[0037] It should be noted that the acquisition of the multi-dimensional correlation matrix involves data preprocessing, feature extraction, multi-source data fusion technology and correlation impact analysis; a multi-dimensional correlation matrix is ​​generated to analyze the mutual influence relationship between various fault trends. The fault prevention strategy is a decision data set that includes operating parameter adjustment suggestions, maintenance priority ranking and resource allocation plan, which is generated through multi-dimensional fault correlation analysis and cost risk optimization modeling. Specifically, the fault prevention strategy includes dynamic adjustment instructions for the operating parameters of the diesel generator set, such as adjusting the generator output power range, optimizing the working intensity of the cooling system, adjusting the fuel supply rate, etc., and also includes maintenance measures. Recommendations, such as determining whether the maintenance priority is to shut down for maintenance immediately or observe operation, list spare parts replacement lists such as bearing models, adjust lubrication cycles, etc., and also include resource allocation plans, such as formulating human resource scheduling plans such as maintenance team division of labor, equipment deployment plans such as standby unit activation time and budget allocation ratio. The generation of these strategies relies on the comprehensive analysis of multi-source data, such as inputting mechanical failure trends such as bearing wear reaching 80%, electrical system failure trends such as insulation aging rate of 0.5% per month, control signal failure trends such as response overshoot of 15%, load influencing factors such as mechanical stress overlimit risk level of level 3, and real-time operation data such as current load rate of 85%. By mapping these data into feature vectors and using the fault evolution knowledge graph to establish association rules, such as increased bearing wear will lead to increased vibration and thus increase the risk of loose electrical connections, combined with maintenance costs such as the cost of replacing bearings of 5,000 yuan and risk consequence weights such as downtime losses of 50,000 yuan per hour, the optimal strategy is calculated using a deep neural network. For example, when the comprehensive fault prediction model outputs a bearing failure probability of 92% and a loose electrical connector probability of 65%, the generated fault prevention strategy may include immediately reducing the load to 70% to slow down the bearing wear rate, arranging the replacement of bearings during low-load hours at night within three days to reduce maintenance costs, and simultaneously checking and reinforcing electrical connectors to prevent cascading failures, and assigning two maintenance personnel and spare bearing inventory number B203. These strategies are pushed to the operation and maintenance terminal in the form of an instruction list, and automatically generate work order numbers and execution schedules. By quantifying the fault evolution path and economic constraints, closed-loop control from fault prediction to preventive execution is achieved, ultimately achieving a balance between risk minimization and optimization of operation and maintenance costs, and realizing global dynamic fault prevention of diesel generator sets.

[0038] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0039] The following describes the working process of the present invention using a common scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0040] A large data center is equipped with a diesel generator set as a backup power source to ensure that the server can continue to operate stably when the main power grid fails. The diesel generator set needs to have high reliability and long-term stable operation capabilities. Therefore, during its operation, real-time monitoring and prevention of potential failures are the key to ensuring system reliability.

[0041] In the application process of the present invention, the operation data of the diesel generator set is first obtained, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data. These data are collected by multiple sensors and monitoring equipment, and recorded and analyzed in real time by a computer system.

[0042] After acquiring the data, the mechanical vibration data is first processed, and the high-frequency features are extracted using wavelet transform to analyze the frequency spectrum changes of the vibration signal. By matching with the preset mechanical fault feature library, possible mechanical problems such as bearing wear, gear failure or looseness are identified. At the same time, the resonant frequency of the mechanical structure is calculated, and the relationship between the vibration characteristics and the wear state of the mechanical components is analyzed to further evaluate the risk of mechanical looseness. By matching the comprehensive features of the above analysis results, the mechanical failure trend of the diesel generator set is obtained.

[0043] At the same time, the electrical parameter data of the diesel generator set is subjected to Fourier transform analysis to extract the harmonic components, and combined with the preset electrical fault feature library, the potential fault types of the electrical system are identified. For example, if the analysis finds that the content of a specific harmonic increases abnormally, it may indicate that there is a problem of local short circuit or insulation aging in the generator winding. In addition, the aging rate of the insulation material is analyzed in combination with the ambient temperature data and humidity information data, and based on this, possible electrical faults in the future are predicted. According to the potential fault types and insulation aging trends of the electrical system, comprehensive feature matching is performed in combination with the preset electrical fault feature library to obtain the fault trend of the electrical system.

[0044] In terms of control signals, the system uses time series analysis methods to decompose the timing changes of control signals and extract the timing change rules of control signals. By matching the fault waveform features in the preset control signal fault feature library, the control signal abnormality type is obtained, such as signal delay, pulse loss or noise interference. At the same time, combined with the operating status of the diesel generator set, the response time and overshoot of the control signal are analyzed to obtain the dynamic stability of the control system. Finally, based on the comprehensive analysis of the control signal timing change rules, control signal abnormality types and control system dynamic stability, the control signal fault trend is obtained.

[0045] In addition, the system processes the load change data and performs multivariate collaborative analysis in combination with the ambient temperature data and humidity information data. First, the load size, change rate and periodic characteristics are extracted from the load change data to obtain the load change characteristic vector, and the finite element analysis method is used based on the load change characteristic vector to calculate the mechanical stress distribution and identify the risk of mechanical stress exceeding the limit. At the same time, combined with the tribological model, the mechanical wear rate under different load conditions is evaluated to obtain the trend of mechanical wear aggravation. In addition, modal analysis is performed on the mutation signal to extract transient vibration characteristics to identify the risk of mechanical resonance. Finally, in combination with the risk of mechanical stress exceeding the limit, the trend of mechanical wear aggravation, the risk of mechanical resonance, the ambient temperature data and the humidity information data, the system uses the support vector machine algorithm to perform multivariate collaborative analysis to obtain the load influencing factor.

[0046] After obtaining the mechanical failure trend, electrical system failure trend, control signal failure trend and load influencing factors, all data are analyzed for correlation and a comprehensive fault prediction model is established. Multi-source data fusion technology is used to generate a multi-dimensional correlation matrix to analyze the mutual influence relationship between various fault trends. Based on this matrix, the system uses a deep neural network algorithm for training and modeling to establish a fault probability prediction model, and dynamically optimizes the model parameters in combination with the preset maintenance cost and risk consequence weight table to generate a comprehensive fault prediction model. The diesel generator set operation data is input into the comprehensive fault prediction model to obtain a fault prevention strategy.

[0047] In specific applications, the operating data of the diesel generator set is input in real time, and the probability of failure under the current state is calculated through a comprehensive fault prediction model. When the probability of failure exceeds the preset threshold, the system automatically generates a fault prevention strategy, such as adjusting load distribution, optimizing control signals, replacing worn parts in advance, or adding insulation protection measures, so as to minimize the risk of failure of the diesel generator set.

[0048] Through the method of the present invention, the diesel generator set can monitor its own status in real time during operation and predict possible failures in advance, thereby improving the reliability and service life of the equipment. In scenarios such as data centers, hospitals, and industrial parks that have high reliability requirements for power supply, the application of this method can effectively reduce the risk of business interruption caused by generator failure and ensure the stable operation of the power system.

[0049] In summary, the present invention discloses a real-time perception method for preventing diesel generator set faults, including obtaining diesel generator set operating data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; based on the mechanical vibration data, using wavelet transform to extract high-frequency features to obtain mechanical fault trends; based on the diesel generator set operating data, using Fourier transform to perform trend analysis to obtain electrical system fault trends; based on the control signal data, performing time series analysis to extract time series change rules to obtain control signal fault trends; based on the load change data, combining the ambient temperature data with the humidity information data to perform multivariate collaborative analysis to obtain load influencing factors; based on the diesel generator set operating data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factors, performing correlation analysis and establishing a comprehensive fault prediction model to obtain a fault prevention strategy.

[0050] The present invention realizes comprehensive perception of the operating status of the diesel generator set by constructing a multi-source data fusion analysis system, and constructs an accurate fault prediction model based on feature extraction and trend analysis of different types of data. First, the mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data of the diesel generator set are collected to ensure the comprehensiveness of the monitoring data. Then, the high-frequency features of the mechanical vibration data are extracted by wavelet transform to identify potential mechanical fault trends; the electrical parameter data is analyzed in the frequency domain by Fourier transform to reveal the electrical system fault trends that may exist in the electrical system; the control signal data is processed by time series analysis method to extract the time series change law, so as to identify the abnormal trend of the control system; combined with load change data, ambient temperature and humidity information, multivariate collaborative analysis is carried out to obtain load influencing factors. Based on the above analysis results, correlation analysis is carried out to establish a comprehensive fault prediction model to fuse multi-source data, improve the accuracy of fault prevention, and obtain a fault prevention strategy. The present invention can adaptively adjust the early warning mechanism and maintenance measures according to the different operating states of the diesel generator set, making up for the limitations of the prior art in fault prevention, thereby realizing the global dynamic fault prevention of the diesel generator set.

[0051] Reference Figure 2 The second embodiment of the present invention provides a real-time sensing diesel generator set fault prevention system, comprising: A data acquisition module is used to acquire the operating data of the diesel generator set, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; A mechanical inspection module, used to extract high-frequency features using wavelet transform according to the mechanical vibration data to obtain mechanical failure trends; An electrical analysis module, used to perform trend analysis using Fourier transform based on the operating data of the diesel generator set to obtain the fault trend of the electrical system; A control diagnosis module, used to perform time series analysis based on the control signal data, extract the time series variation law, and obtain the control signal fault trend; A load analysis module, used to perform multivariate collaborative analysis based on the load change data in combination with the ambient temperature data and the humidity information data to obtain a load impact factor; The result output module is used to perform correlation analysis and establish a comprehensive fault prediction model based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor to obtain a fault prevention strategy.

[0052] It should be noted that the real-time perception diesel generator set fault prevention device provided in an embodiment of the present invention is used to execute all the process steps of the real-time perception diesel generator set fault prevention method in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0053] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a real-time sensing diesel generator set fault prevention program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the real-time sensing diesel generator set fault prevention method are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, such as the load analysis module.

[0054] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0055] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0056] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0057] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0058] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0059] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0060] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time sensing diesel generator set fault prevention method, characterized in that: Executed by a computer, including: Obtain diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; According to the mechanical vibration data, high-frequency features are extracted by wavelet transform to obtain mechanical failure trends; Based on the diesel generator set operation data, Fourier transform is used to perform trend analysis to obtain the electrical system fault trend; Perform time series analysis based on the control signal data, extract the time series variation law, and obtain the control signal failure trend; According to the load change data, a multivariate collaborative analysis is performed in combination with the ambient temperature data and the humidity information data to obtain a load influencing factor; According to the diesel generator set operation data, the mechanical failure trend, the electrical system failure trend, the control signal failure trend and the load influencing factor, a correlation analysis is performed and a comprehensive failure prediction model is established to obtain a failure prevention strategy.

2. The real-time sensing diesel generator set fault prevention method according to claim 1 is characterized in that: The acquisition of diesel generator set operation data, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data, includes: Obtaining original mechanical vibration data, original electrical parameter data, original control signal data, original load change data, original ambient temperature data and original humidity information data; The original operation data of the diesel generator set is defined to include the original mechanical vibration data, the original electrical parameter data, the original control signal data, the original load change data, the original ambient temperature data and the original humidity information data; Perform integrity analysis based on the original operating data of the diesel generator set, and use an interpolation algorithm to fill in missing values ​​to obtain complete operating data of the diesel generator set; According to the complete operation data of the diesel generator set, data format standardization processing is performed to obtain standardized operation data of the diesel generator set; The diesel generator set operation data includes mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data.

3. The real-time sensing diesel generator set fault prevention method according to claim 1 is characterized in that: The method of extracting high-frequency features by wavelet transform according to the mechanical vibration data to obtain the mechanical failure trend includes: According to the mechanical vibration data, wavelet transform is used to extract features to obtain high-frequency feature components; According to the high-frequency characteristic component, combined with a preset mechanical fault characteristic library, frequency characteristic matching of mechanical fault types is performed to obtain potential mechanical fault types; According to the mechanical vibration data, the resonance frequency of each component of the mechanical structure is calculated, and the corresponding relationship between the mechanical wear and the vibration spectrum is analyzed to obtain the degree of mechanical wear; Calculating the acceleration characteristics of the mechanical looseness fault according to the mechanical vibration data, performing acceleration time domain signal analysis, and obtaining the mechanical looseness risk; According to the potential mechanical failure type, the mechanical wear degree and the mechanical looseness risk, combined with a preset mechanical failure feature library, a comprehensive feature matching of mechanical failure trends is performed to obtain a mechanical failure trend.

4. The real-time sensing diesel generator set fault prevention method according to claim 1 is characterized in that: The method of performing trend analysis based on the diesel generator set operation data by using Fourier transform to obtain the electrical system fault trend includes: According to the electrical parameter data, Fourier transform is used to analyze and obtain harmonic components; According to the harmonic components, combined with a preset electrical fault feature library, electrical fault type harmonic feature matching is performed to obtain potential fault types of the electrical system; Perform insulation aging rate analysis based on the ambient temperature data and the humidity information data to obtain an insulation aging trend; According to the insulation aging trend and the potential fault type of the electrical system, combined with a preset electrical fault feature library, electrical fault trend comprehensive feature matching is performed to obtain the electrical fault trend.

5. The real-time sensing diesel generator set fault prevention method according to claim 1 is characterized in that: The method of performing time series analysis based on the control signal data, extracting the time series variation rule, and obtaining the control signal fault trend includes: According to the control signal data, time series decomposition is used to perform feature extraction to obtain the time series variation law of the control signal; According to the control signal timing variation rule, matching is performed in combination with the fault waveform features in the preset control signal fault feature library to obtain the control signal abnormality type; According to the abnormal type of the control signal, combined with the operating state of the control system, the response time and overshoot of the control signal are analyzed to obtain the dynamic stability of the control system; According to the control signal timing variation rule, the control signal abnormality type and the control system dynamic stability, combined with a preset control signal fault feature library, control signal fault trend comprehensive feature matching is performed to obtain the control signal fault trend.

6. The real-time sensing diesel generator set fault prevention method according to claim 1 is characterized in that: The multivariate collaborative analysis is performed based on the load change data in combination with the ambient temperature data and the humidity information data to obtain the load impact factor, including: Extracting features based on the load change data to obtain a load change feature vector including load size, change rate and periodicity features; According to the load change characteristic vector, a finite element analysis method is used to perform a mechanical stress distribution analysis to obtain a mechanical stress over-limit risk; According to the load variation characteristic vector, combined with the tribology model, the mechanical wear rate under different load conditions is analyzed to obtain the mechanical wear aggravation trend; According to the load change characteristic vector, a modal analysis method is used to perform wavelet analysis on the mutation signal, extract transient vibration characteristics, and obtain the mechanical resonance risk; Based on the risk of mechanical stress exceeding the limit, the tendency of mechanical wear aggravation, the risk of mechanical resonance, the ambient temperature data and the humidity information data, a support vector machine algorithm is used to perform multivariate collaborative analysis to obtain load influencing factors.

7. The real-time sensing diesel generator set fault prevention method according to claim 1 is characterized in that: The method of performing correlation analysis and establishing a comprehensive fault prediction model based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor to obtain a fault prevention strategy includes: According to the mechanical failure trend, the electrical system failure trend, the control signal failure trend and the load impact factor, a correlation impact analysis is performed using multi-source data fusion technology to obtain a multi-dimensional correlation matrix; According to the multi-dimensional correlation matrix, a deep neural network algorithm is used to train and model a fault probability prediction model; According to the fault probability prediction model, combined with a preset maintenance cost and risk consequence weight table, the model parameters are dynamically optimized to obtain a comprehensive fault prediction model; The operating data of the diesel generator set is input into a comprehensive fault prediction model to obtain a fault prevention strategy.

8. A real-time sensing diesel generator set fault prevention system, characterized in that: include: A data acquisition module is used to acquire the operating data of the diesel generator set, including mechanical vibration data, electrical parameter data, control signal data, load change data, ambient temperature data and humidity information data; A mechanical inspection module, used to extract high-frequency features using wavelet transform according to the mechanical vibration data to obtain mechanical failure trends; An electrical analysis module, used to perform trend analysis using Fourier transform based on the operating data of the diesel generator set to obtain the fault trend of the electrical system; A control diagnosis module, used to perform time series analysis based on the control signal data, extract the time series variation law, and obtain the control signal fault trend; A load analysis module, used to perform multivariate collaborative analysis based on the load change data in combination with the ambient temperature data and the humidity information data to obtain a load impact factor; The result output module is used to perform correlation analysis and establish a comprehensive fault prediction model based on the diesel generator set operation data, the mechanical fault trend, the electrical system fault trend, the control signal fault trend and the load influencing factor to obtain a fault prevention strategy.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for preventing diesel generator set faults with real-time perception as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the real-time perception diesel generator set fault prevention method as described in any one of claims 1 to 7.

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