Engine ignition fault diagnosis method, device and equipment

Abstract

The invention provides an engine ignition fault diagnosis method, device and equipment, belongs to the technical field of computer information processing, and solves the problems that the existing engine ignition fault diagnosis process depends on manual inspection and is long in time consumption, high in cost and low in diagnosis accuracy and timeliness. The method comprises the steps that at least one kind of sound wave signal data generated in the engine ignition process is obtained; preprocessing the at least one kind of sound wave signal data to obtain preprocessed sound wave data; performing feature extraction processing on the preprocessed sound wave data to obtain feature data; and carrying out identification and diagnosis processing on the feature data to obtain engine ignition fault diagnosis data. According to the scheme, the sound wave signal is used as a diagnosis basis, and real-time monitoring and accurate diagnosis of the working state of the engine ignition system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information processing, and in particular to an engine ignition fault diagnosis method, device and equipment. Background Art

[0002] As the main power source for general-purpose small aircraft, the stability of the ignition system of piston aircraft engines is directly related to the overall performance and operational safety of the engine. During long-term use, the ignition system is easily affected by various factors such as the environment, material aging, and improper use, leading to ignition failures. Traditional ignition fault diagnosis methods mostly rely on manual inspection, disassembly inspection, and static testing, which are not only time-consuming and costly, but also difficult to capture dynamic changes in the ignition process, thus affecting the accuracy and timeliness of the diagnosis. Summary of the invention

[0003] The present invention provides an engine ignition fault diagnosis method, device and equipment, which solves the problems that the current engine ignition fault diagnosis process relies on manual inspection, is time-consuming, costly, and has low diagnostic accuracy and timeliness.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] An embodiment of the present invention provides an engine ignition fault diagnosis method, comprising:

[0006] Acquiring at least one acoustic signal data generated during engine ignition;

[0007] Preprocessing the at least one acoustic wave signal data to obtain preprocessed acoustic wave data;

[0008] Performing feature extraction processing on the preprocessed sound wave data to obtain feature data;

[0009] The characteristic data is identified and diagnosed to obtain engine ignition fault diagnosis data.

[0010] Optionally, the acquiring of sound wave signal data includes:

[0011] At least one type of acoustic wave signal data generated during the engine ignition process is acquired through a microphone array or an acoustic wave sensor installed at the position of the engine's ignition component.

[0012] Optionally, preprocessing the sound wave signal data to obtain preprocessed sound wave data includes:

[0013] Performing filtering processing on the at least one sound wave signal data to obtain sound wave data after filtering processing;

[0014] The filtered sound wave data is subjected to denoising to obtain pre-processed sound wave data.

[0015] Optionally, performing feature extraction processing on the preprocessed sound wave data to obtain feature data includes:

[0016] Performing feature parameter extraction processing on the preprocessed sound wave data to obtain key feature parameters of the ignition state;

[0017] Performing statistical analysis on key characteristic parameters of the ignition state to obtain at least one of a mean value, a variance and a high-order statistic of the characteristic parameters;

[0018] Normalize at least one of the mean, variance and high-order statistics of the characteristic parameter to obtain characteristic data.

[0019] Optionally, identification and diagnosis processing is performed on the characteristic data to obtain engine ignition fault diagnosis data, including:

[0020] The characteristic data is input into a fault diagnosis model for processing to obtain engine ignition fault diagnosis data; the fault diagnosis model maps the input characteristic data through a nonlinear activation function to obtain an intermediate result; and the intermediate result is subjected to nonlinear transformation processing to obtain engine ignition fault diagnosis data.

[0021] Optionally, the fault diagnosis model maps the input feature data through a nonlinear activation function to obtain an intermediate result; and performs nonlinear transformation processing on the intermediate result to obtain engine ignition fault diagnosis data, including:

[0022] The fault diagnosis model is implemented by a nonlinear activation function:

[0023] y=f·(W·x+b)

[0024] Map the input feature data to obtain the intermediate result;

[0025] pass:

[0026] z=σ(y)

[0027] Performing nonlinear transformation processing on the intermediate results to obtain engine ignition fault diagnosis data;

[0028] Among them, W is the weight matrix, x is the input vector, b is the bias term, f is the activation function, y is the intermediate result, σ is the nonlinear transformation function, and z is the engine ignition fault diagnosis data.

[0029] Optionally, the engine ignition fault diagnosis method further includes:

[0030] An engine ignition fault diagnosis report is output, wherein the engine ignition fault diagnosis report includes at least one of a sound wave waveform diagram, a spectrum analysis diagram, and a fault diagnosis result.

[0031] The embodiment of the present invention further provides an engine ignition fault diagnosis device, comprising:

[0032] An acquisition module, used to acquire at least one type of acoustic wave signal data generated during the engine ignition process;

[0033] A processing module, used to preprocess the at least one acoustic wave signal data to obtain preprocessed acoustic wave data; perform feature extraction processing on the preprocessed acoustic wave data to obtain feature data;

[0034] The generating module is used to perform identification and diagnosis processing on the characteristic data to obtain engine ignition fault diagnosis data.

[0035] An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the above method when executed by the processor.

[0036] An embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method.

[0037] The technical solution of the present invention includes at least the following effects:

[0038] The above-mentioned scheme of the present invention obtains at least one kind of sound wave signal data generated during the engine ignition process; pre-processes at least one kind of sound wave signal data to obtain pre-processed sound wave data; performs feature extraction processing on the pre-processed sound wave data to obtain feature data; performs identification and diagnosis processing on the feature data to obtain engine ignition fault diagnosis data; the technical scheme provided by the present invention provides a piston aircraft engine ignition fault diagnosis method based on audio signal analysis, which monitors and diagnoses the fault of the engine ignition system in real time and accurately through non-invasive means, not only realizing real-time monitoring and accurate diagnosis of the working state of the ignition system, but also having a fault warning function, providing a strong technical guarantee for the safe operation and efficient maintenance of the aircraft engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of an engine ignition fault diagnosis method provided by an embodiment of the present invention;

[0040] Figure 2 is a structural diagram of an engine ignition fault diagnosis device provided by an embodiment of the present invention;

[0041] Figure 3It is a schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0043] like Figure 1 As shown, an embodiment of the present invention provides an engine ignition fault diagnosis method, comprising:

[0044] Step 11, obtaining at least one acoustic wave signal data generated during the engine ignition process;

[0045] Step 12, preprocessing the at least one acoustic wave signal data to obtain preprocessed acoustic wave data;

[0046] Step 13, performing feature extraction processing on the pre-processed sound wave data to obtain feature data;

[0047] Step 14, performing identification and diagnosis processing on the characteristic data to obtain engine ignition fault diagnosis data.

[0048] In this embodiment, in order to obtain fault diagnosis data, the sound wave signal data is first collected; the sound wave signal data includes multiple types, and at least one of them needs to be collected; a special sound wave sensor, such as a high-sensitivity microphone array, is used in the collection process to capture the sound wave signal generated during the engine ignition process; the microphone or sensor needs to be installed near the spark plug to ensure that the sound wave generated at the moment of ignition can be accurately captured; the sound wave signal collection system has a high signal-to-noise ratio and a wide bandwidth to capture various sound wave components from low frequency to high frequency; at the same time, the collection system has real-time transmission capability to transmit the collected sound wave signal to the subsequent processing unit in real time; the frequency range of the high-sensitivity microphone array should match the expected sound wave signal;

[0049] After obtaining the sound wave signal data, it is necessary to preprocess the sound wave signal data; the preprocessing operation includes filtering and denoising the sound wave signal to eliminate interference factors such as environmental noise and mechanical vibration. The filtering process can use digital filters, such as low-pass filters, band-pass filters, etc., and select the appropriate filter type and parameters according to the characteristics of the sound wave signal. The denoising process can use advanced denoising techniques such as wavelet transform and empirical mode decomposition to further improve the signal quality;

[0050] After the acoustic signal data is preprocessed, information that can reflect the essential characteristics of the signal is extracted from it; specifically, based on the physical characteristics of the ignition process, key characteristic parameters that can characterize the ignition state are extracted, such as the acoustic peak value at the ignition moment, the specific frequency components of the acoustic spectrum, the distribution of acoustic energy, etc. These characteristic parameters will serve as input for subsequent pattern recognition;

[0051] The extracted feature parameters are input into the trained pattern recognition model, which is built based on machine learning algorithms such as support vector machines, neural networks, random forests, etc. The model contains a preset fault mode library, which stores the acoustic feature parameter ranges and pattern features corresponding to different fault types; the model compares and analyzes the input feature parameters, and determines whether the current ignition state is normal or has a certain fault by calculating indicators such as similarity or distance measurement. If a fault exists, the model will further determine the fault type, location and severity, and generate an engine ignition fault diagnosis report;

[0052] In addition, the method can also include a result display and feedback process, that is, the fault diagnosis results are intuitively displayed to the user through a display system; the display system can be a computer screen, a touch screen or a mobile device interface, etc., and the display content includes sound wave waveforms, spectrum analysis diagrams, fault diagnosis reports, etc.; users can quickly locate the fault location based on the report content and take corresponding maintenance or replacement measures; when the model predicts a possible fault trend in the future, it will send an early warning signal to the user in advance so that the user can take preventive maintenance measures in time. The early warning signal can be notified by status code, sound or light.

[0053] Compared with the existing technology, this technical solution has the following advantages:

[0054] (1) The piston aircraft engine ignition fault diagnosis method based on audio signal analysis in the present invention is implemented by non-invasive means, thereby reducing the risk and cost in the diagnosis process.

[0055] (2) The present invention has real-time and accuracy, and can collect and analyze sound wave signals in real time, thereby improving the timeliness and accuracy of fault diagnosis.

[0056] (3) The present invention is comprehensive and integrated, and can comprehensively evaluate the performance differences and fault conditions of the controller, wiring harness and spark plug, provide comprehensive data support for optimizing engine performance, and more precisely monitor the ignition status of a single cylinder, so as to promptly detect cylinders with abnormal combustion.

[0057] (4) The present invention has the advantages of intelligence and automation, and uses machine learning algorithms to perform pattern recognition and fault diagnosis, realize the intelligence and automation of the diagnostic process, and improve diagnostic efficiency and accuracy;

[0058] In an optional embodiment of the present invention, step 11 may include:

[0059] Step 111, obtaining at least one type of sound wave signal data generated during the engine ignition process by means of a microphone array or a sound wave sensor installed at the position of the engine's ignition component.

[0060] In this embodiment, the microphone array is a device composed of multiple microphones arranged in a certain pattern, which is used to capture and record sound wave signals; each microphone can independently receive sound waves and convert them into electrical signals; the sound wave sensor is a device used to detect sound wave signals and convert them into measurable signals; different from the microphone array, the sound wave sensor may be smaller and lighter, and suitable for different application scenarios;

[0061] Whether using a microphone array or an acoustic wave sensor, the specific steps for obtaining acoustic wave signal data generally include: selecting a suitable microphone array or acoustic wave sensor according to the application scenario and requirements, and arranging it in a suitable position. In this embodiment, a high-sensitivity microphone array needs to be installed near the engine spark plug; connecting the device to a data acquisition system or computer, and performing necessary configuration and calibration. Start the data acquisition software or program to start recording the acoustic wave signal data received by the microphone array or acoustic wave sensor.

[0062] In an optional embodiment of the present invention, step 12 may include:

[0063] Step 121, filtering the sound wave signal data to obtain sound wave data after filtering;

[0064] Step 122, performing denoising on the sound wave data after the filtering process to obtain pre-processed sound wave data.

[0065] In this embodiment, the main purpose of filtering is to remove unnecessary frequency components from the original sound wave signal data, thereby retaining information useful for subsequent analysis; in the sound wave signal processing, filtering can reduce noise interference and improve the quality of the signal; first, according to the characteristics of the sound wave signal and the frequency range to be processed, a suitable filter type is selected, such as a low-pass filter or a band-pass filter; after filtering, the noise and interference components in the sound wave signal data will be effectively removed, retaining the frequency components useful for subsequent analysis, and improving the accuracy and reliability of the sound wave data;

[0066] Although filtering has removed some of the noise, in some cases, there may still be residual noise or interference; denoising is intended to further reduce these noise components to improve the clarity and readability of the acoustic data; denoising can use denoising techniques such as wavelet transform and empirical mode decomposition, among which wavelet transform denoising removes noise by decomposing and reconstructing the signal in the wavelet domain; after denoising, the noise components in the acoustic data will be further reduced, and the clarity and readability of the signal will be significantly improved; providing more accurate and reliable data support for subsequent analysis and diagnosis.

[0067] In an optional embodiment of the present invention, step 13 may include:

[0068] Step 131, extracting characteristic parameters of the ignition state from the preprocessed sound wave data to obtain key characteristic parameters of the ignition state;

[0069] Step 132, performing statistical analysis on the key characteristic parameters of the ignition state to obtain at least one of a mean value, a variance and a high-order statistic of the characteristic parameters;

[0070] Step 133, normalize at least one of the mean, variance and high-order statistics of the characteristic parameter to obtain characteristic data.

[0071] In this embodiment, the characteristic parameter extraction of the engine ignition state refers to extracting key information directly related to the engine ignition process from the sound wave data. These characteristic parameters include:

[0072] (1) Amplitude characteristics: The amplitude change of the acoustic signal during engine ignition can reflect the change of engine ignition intensity;

[0073] (2) Frequency characteristics: The specific frequency components of the acoustic signal generated by the engine ignition, which are related to the specific operating state or failure mode of the engine;

[0074] (3) Duration characteristics: The duration of the engine ignition acoustic wave signal can reflect the stability of the engine ignition process;

[0075] (4) Phase characteristics: The phase information of the acoustic signal contains important clues about the engine ignition timing;

[0076] After obtaining the key characteristic parameters, statistical analysis is required to obtain more in-depth information. The specific process includes:

[0077] (1) Mean calculation: Calculate the average value of each feature parameter to understand its overall performance on the entire dataset;

[0078] (2) Variance calculation: Calculate the variance of the characteristic parameters to assess the dispersion or volatility of the data;

[0079] (3) Calculation of higher-order statistics: such as skewness and kurtosis. These statistics can provide information about the shape of the data distribution and help identify outliers or nonlinear relationships.

[0080] Normalization is to scale the data to a specific range (such as 0 to 1 or -1 to 1) to eliminate the deviation between different features due to different dimensions or value ranges; the mean, variance and high-order statistics of the feature parameters calculated in step 132 are normalized; the purpose of normalization is to ensure that all features have the same weight in subsequent analysis and models to eliminate the impact of dimensions, ensure the comparability and effectiveness of feature data, and thus improve the accuracy and stability of the model; after normalization, the final feature data is obtained, which can be used for subsequent machine learning model training, fault diagnosis or performance evaluation tasks.

[0081] In an optional embodiment of the present invention, step 14 may include:

[0082] Step 141, input the characteristic data into the fault diagnosis model for processing to obtain engine ignition fault diagnosis data; the fault diagnosis model maps the input characteristic data through a nonlinear activation function to obtain an intermediate result; and nonlinearly transforms the intermediate result to obtain engine ignition fault diagnosis data.

[0083] In this embodiment, the feature data is input into a fault diagnosis model based on deep learning for processing to obtain a fault diagnosis output; the fault diagnosis model adopts a multi-layer perceptron architecture, and efficiently maps the input features through a nonlinear activation function to obtain an intermediate result, and further performs a nonlinear transformation on the intermediate result to obtain the final engine ignition fault diagnosis data;

[0084] Wherein, the fault diagnosis model is implemented by a nonlinear activation function:

[0085] y=f·(W·x+b)

[0086] Process the input feature data to obtain intermediate results;

[0087] pass:

[0088] z=σ(y)

[0089] Performing nonlinear transformation processing on the intermediate results to obtain engine ignition fault diagnosis data;

[0090] Among them, W is the weight matrix, x is the input vector, b is the bias term, f is the activation function, y is the intermediate result, σ is the nonlinear transformation function, and z is the engine ignition fault diagnosis data.

[0091] A specific embodiment of an engine ignition fault diagnosis method provided by an embodiment of the present invention is:

[0092] Step 1, obtaining at least one acoustic wave signal data generated during the engine ignition process;

[0093] Install a high-sensitivity microphone array near the engine spark plug to capture the sound wave signal generated during the engine ignition process;

[0094] Step 2, preprocessing the at least one acoustic wave signal data to obtain preprocessed acoustic wave data;

[0095] After obtaining the sound wave signal data, the sound wave signal data is filtered and denoised in turn; the filter type can be selected as a low-pass filter or a band-pass filter; the denoising process can use advanced denoising techniques such as wavelet transform and empirical mode decomposition; after the above processing, the noise component in the sound wave data will be further reduced, and the clarity and readability of the signal will be significantly improved.

[0096] Step 3, performing feature extraction processing on the pre-processed sound wave data to obtain feature data;

[0097] Specifically, the feature extraction process of the pre-processed sound wave data includes:

[0098] (1) Time domain feature extraction: Calculate the statistical features of the acoustic signal, such as mean, variance, peak, skewness, and kurtosis, to capture the basic properties of the signal;

[0099] (2) Frequency domain feature extraction: The time domain signal is converted into a frequency domain signal through fast Fourier transform, and the spectrum features are extracted, including the main frequency, frequency band energy, and frequency distribution. These features can reflect the abnormal patterns in the ignition process.

[0100] (3) Time-frequency domain feature extraction: Use short-time Fourier transform or wavelet transform to obtain the joint features of time and frequency and capture the changes of the signal in time and frequency;

[0101] (4) Feature fusion: The features extracted from the time domain, frequency domain, and time-frequency domain are fused to form a high-dimensional feature vector for subsequent model input.

[0102] Step 4: performing identification and diagnosis processing on the characteristic data to obtain engine ignition fault diagnosis data.

[0103] Specifically, the process of identifying and diagnosing the feature data includes:

[0104] (1) Model selection: Select a suitable machine learning or deep learning model, such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), or long short-term memory network (LSTM). The choice of model should be based on the nature of the feature data and the fault type.

[0105] (2) Model training: Use the labeled training data set to train the selected model; the training data should contain the acoustic wave characteristics under normal and fault conditions so that the model can learn the characteristic distribution of different states; use methods such as cross-validation to optimize model parameters and improve the generalization ability of the model;

[0106] (3) Fault diagnosis: The new acoustic wave feature data is input into the trained model, and the model outputs the fault diagnosis results, including the fault type and its confidence level. By setting a threshold, it is determined whether a fault exists and the corresponding fault diagnosis report is output.

[0107] The engine ignition fault diagnosis method proposed in the present invention provides a piston aircraft engine ignition fault diagnosis method based on audio signal analysis. It monitors and diagnoses the faults of the engine ignition system in real time and accurately through non-invasive means. It not only realizes real-time monitoring and accurate diagnosis of the working status of the ignition system, but also has a fault warning function, which provides a strong technical guarantee for the safe operation and efficient maintenance of aircraft engines.

[0108] like Figure 2 As shown, the embodiment of the present invention further provides an engine ignition fault diagnosis device 20, comprising:

[0109] An acquisition module 21, used to acquire at least one type of acoustic wave signal data generated during the engine ignition process;

[0110] The processing module 22 is used to preprocess the at least one acoustic wave signal data to obtain preprocessed acoustic wave data; perform feature extraction processing on the preprocessed acoustic wave data to obtain feature data;

[0111] The generating module 23 is used to perform identification and diagnosis processing on the characteristic data to obtain engine ignition fault diagnosis data.

[0112] Optionally, the acquisition module 21 includes:

[0113] At least one type of acoustic wave signal data generated during the engine ignition process is acquired through a microphone array or an acoustic wave sensor installed at the position of the engine's ignition component.

[0114] Optionally, the processing module 22 includes:

[0115] Performing filtering processing on the at least one sound wave signal data to obtain sound wave data after filtering processing;

[0116] The filtered sound wave data is subjected to denoising to obtain pre-processed sound wave data.

[0117] Optionally, the processing module 22 further includes:

[0118] Performing feature parameter extraction processing on the preprocessed sound wave data to obtain key feature parameters of the ignition state;

[0119] Performing statistical analysis on key characteristic parameters of the ignition state to obtain at least one of a mean value, a variance and a high-order statistic of the characteristic parameters;

[0120] Normalize at least one of the mean, variance and high-order statistics of the characteristic parameter to obtain characteristic data.

[0121] Optionally, the generating module 23 includes:

[0122] The characteristic data is input into a fault diagnosis model for processing to obtain engine ignition fault diagnosis data; the fault diagnosis model maps the input characteristic data through a nonlinear activation function to obtain an intermediate result; and the intermediate result is subjected to nonlinear transformation processing to obtain engine ignition fault diagnosis data.

[0123] Optionally, the fault diagnosis model maps the input feature data through a nonlinear activation function to obtain an intermediate result; and performs nonlinear transformation processing on the intermediate result to obtain engine ignition fault diagnosis data, including:

[0124] The fault diagnosis model is implemented by a nonlinear activation function:

[0125] y=f·(W·x+b)

[0126] Map the input feature data to obtain the intermediate result;

[0127] pass:

[0128] z=σ(y)

[0129] Performing nonlinear transformation processing on the intermediate results to obtain engine ignition fault diagnosis data;

[0130] Among them, W is the weight matrix, x is the input vector, b is the bias term, f is the activation function, y is the intermediate result, σ is the nonlinear transformation function, and z is the engine ignition fault diagnosis data.

[0131] Optionally, the engine ignition fault diagnosis device 20 further includes:

[0132] The display module 24 is used to output an engine ignition fault diagnosis report, wherein the engine ignition fault diagnosis report includes at least one of a sound wave waveform diagram, a spectrum analysis diagram, and a fault diagnosis result.

[0133] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0134] like Figure 3 As shown, the embodiment of the present invention further provides a computing device 30, including a processor 31, a memory 32, a program or instruction stored in the memory 32 and executable on the processor 31, and when the program or instruction is executed by the processor 31, each process of the above-mentioned engine ignition fault diagnosis method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. It should be noted that the computing device in the embodiment of the present invention includes the above-mentioned mobile electronic device and non-mobile electronic device.

[0135] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0140] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0141] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0142] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code for implementing a method or device. That is to say, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0143] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for diagnosing engine ignition faults, characterized in that: include: Acquiring at least one acoustic signal data generated during engine ignition; Preprocessing the at least one acoustic wave signal data to obtain preprocessed acoustic wave data; Performing feature extraction processing on the preprocessed sound wave data to obtain feature data; The characteristic data is identified and diagnosed to obtain engine ignition fault diagnosis data.

2. The engine ignition fault diagnosis method according to claim 1, characterized in that: The step of obtaining at least one type of acoustic signal data generated during the engine ignition process includes: At least one type of acoustic wave signal data generated during the engine ignition process is acquired through a microphone array or an acoustic wave sensor installed at the position of the engine's ignition component.

3. The engine ignition fault diagnosis method according to claim 1, characterized in that: Preprocessing the at least one acoustic wave signal data to obtain preprocessed acoustic wave data includes: Performing filtering processing on the at least one sound wave signal data to obtain sound wave data after filtering processing; The filtered sound wave data is subjected to denoising to obtain pre-processed sound wave data.

4. The engine ignition fault diagnosis method according to claim 1, characterized in that: Performing feature extraction processing on the preprocessed sound wave data to obtain feature data includes: Performing feature parameter extraction processing on the preprocessed sound wave data to obtain key feature parameters of the ignition state; Performing statistical analysis on key characteristic parameters of the ignition state to obtain at least one of a mean value, a variance and a high-order statistic of the characteristic parameters; Normalize at least one of the mean, variance and high-order statistics of the characteristic parameter to obtain characteristic data.

5. The engine ignition fault diagnosis method according to claim 1, characterized in that: The characteristic data is identified and diagnosed to obtain engine ignition fault diagnosis data, including: The characteristic data is input into a fault diagnosis model for processing to obtain engine ignition fault diagnosis data; the fault diagnosis model maps the input characteristic data through a nonlinear activation function to obtain an intermediate result; and the intermediate result is subjected to nonlinear transformation processing to obtain engine ignition fault diagnosis data.

6. The engine ignition fault diagnosis method according to claim 1, characterized in that: The fault diagnosis model maps the input feature data through a nonlinear activation function to obtain an intermediate result; The intermediate result is subjected to nonlinear transformation processing to obtain engine ignition fault diagnosis data, including: The fault diagnosis model is implemented by a nonlinear activation function: y=f·(W·x+b) Map the input feature data to obtain the intermediate result; pass: z=σ(y) Performing nonlinear transformation processing on the intermediate results to obtain engine ignition fault diagnosis data; Among them, W is the weight matrix, x is the input vector, b is the bias term, f is the activation function, y is the intermediate result, σ is the nonlinear transformation function, and z is the engine ignition fault diagnosis data.

7. The engine ignition fault diagnosis method according to claim 1, characterized in that: Also includes: An engine ignition fault diagnosis report is output, wherein the engine ignition fault diagnosis report includes at least one of a sound wave waveform diagram, a spectrum analysis diagram, and a fault diagnosis result.

8. An engine ignition fault diagnosis device, characterized in that: include: An acquisition module, used to acquire at least one type of acoustic wave signal data generated during the engine ignition process; A processing module, used to preprocess the at least one acoustic wave signal data to obtain preprocessed acoustic wave data; perform feature extraction processing on the preprocessed acoustic wave data to obtain feature data; The generating module is used to perform identification and diagnosis processing on the characteristic data to obtain engine ignition fault diagnosis data.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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