Real-time online diagnosis method for engine valve clearance abnormal fault based on DREKF

Abstract

The application aims to provide a real-time online diagnosis method for engine valve clearance abnormal failure based on DREKF, belonging to the field of engine diagnosis. The method comprises the following steps: real-time acquisition of engine cylinder head vibration signal, establishment and simplification of cylinder head vibration signal mathematical model and determination of undetermined parameters; selection of appropriate state variables to construct cylinder head vibration signal state space equation and verification of its observability; design of double-rate extended Kalman filter optimal estimation method, input of vibration signal into DREKF, and obtaining of cylinder head vibration signal estimated value; simultaneous calculation of process derivative based on DREKF estimated vibration signal and derivative threshold value, and realization of online real-time fault diagnosis of the engine valve clearance according to the valve clearance state judgment criterion. The application establishes a new nonlinear state space model of engine cylinder head vibration signal, is suitable for engine cylinder head vibration signal observation under different speeds and different loads, and realizes online real-time diagnosis of the valve clearance state.

Description

Technical Field

[0001] The present invention relates to an engine diagnosis method, in particular to a valve clearance abnormality fault diagnosis method. Background Art

[0002] The cylinder head vibration signal of an engine contains a wealth of information about the operating conditions of various engine components, such as changes in combustion pressure within the cylinder, valve opening and closing, and friction between the piston and cylinder liner. Real-time analysis and processing of cylinder head vibration signals can be used for engine fault diagnosis, combustion status assessment, and other applications. Abnormal valve clearance is a typical engine failure. If not promptly diagnosed and repaired, it can lead to reduced mechanical efficiency and power, impacting the safe operation of equipment and systems, and even endangering the personal safety of operators. Therefore, research on abnormal valve clearance fault diagnosis is crucial for ensuring safe and efficient engine operation.

[0003] The paper "Diagnosis Method for Abnormal Valve Clearance in Diesel Engines Based on Complex Morlet Transform and Improved AlexNet Neural Network" (Journal of Beijing University of Chemical Technology, 2021) diagnoses abnormal valve clearance faults in TBD234V12 diesel engines. The paper uses a complex Morlet wavelet basis to perform a continuous wavelet transform on the cylinder head vibration signal. The resulting time-frequency graph is compressed and normalized before being input into the AlexNet neural network. The AlexNet neural network architecture is improved, and the optimal parameters are found through parameter adjustment, resulting in a fault diagnosis model with an accuracy of 98.91%. The method used in this paper requires high computational resources; the diagnostic method is data-driven and highly data-dependent; and the model has poor interpretability. The neural network acts like a "black box," making it difficult to understand the internal decision-making process and feature extraction mechanism. The document "Application of Improved Variational Modal Decomposition in Diagnosis of Abnormal Valve Clearance Faults in Engines" (Journal of Beijing University of Chemical Technology, 2021) diagnoses abnormal valve clearance faults in TBD234V12 diesel engines and proposes an improved VMD method, which optimizes the number of modes and penalty factors based on the principle of minimizing the correlation coefficient of signals in adjacent frequency bands and maximizing the power spectrum entropy of high-frequency noise, and verifies its effectiveness through simulation signals. Based on this, a fault diagnosis method was constructed to form a complete diagnostic process. Through valve clearance fault experiments under different working conditions, it was finally verified that this method has a higher recognition rate than the traditional VMD method in fault identification, with an average recognition rate of 86.9%. The VMD method used in this document takes a long time to calculate, and lacks universality for parameters of different diesel engines and different working conditions. It is also data-driven, and the large demand for computing resources leads to complex calculations and limited real-time performance, which affects the effective application of this method in a wider range of scenarios and real-time monitoring. Summary of the Invention

[0004] The object of the present invention is to provide a real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF, which is applicable to observation of engine cylinder head vibration signals at different speeds and different loads.

[0005] The object of the present invention is achieved like this:

[0006] The present invention provides a real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF, which is characterized by comprising the following steps:

[0007] (1) According to the characteristics of the engine cylinder head vibration signal, a nonlinear mathematical model of the engine cylinder head vibration signal is established and simplified;

[0008] (2) Vibration signal parameter identification: The impact frequency and attenuation damping parameters of the target cylinder head vibration signal are identified based on the Laplace wavelet correlation filter algorithm;

[0009] (3) Perform initial parameter identification on the cylinder head vibration signal to select state variables, and construct a dual-rate nonlinear state space model based on the state variables. Discretize the nonlinear state space model and verify the observability of the model.

[0010] (4) Taking process noise and measurement noise into account in the model, and linearizing the model to obtain the linear discrete system state space equation, based on the dual-rate extended Kalman filter algorithm, the optimal estimation method of the engine cylinder head vibration signal is designed;

[0011] (5) The basic parameters of the DREKF algorithm are identified and set. Then, the process derivative obtained by estimating the cylinder head vibration signal based on DREKF in step (3) is used as the monitoring target, the specific parameters and thresholds of the fault diagnosis are defined, and the judgment criteria of the valve clearance status are set.

[0012] The present invention may also include:

[0013] 1. Step (1) The process of establishing the nonlinear mathematical model of the engine cylinder head vibration signal is as follows:

[0014] The cylinder head vibration signal y is approximated as the sum of multiple Laplace wavelet functions:

[0015]

[0016] Where a i is the amplitude of the vibration signal generated by the excitation; ω i is the impact frequency generated by the excitation; i is the attenuation damping generated by the excitation;

[0017] Extract the seating impact component from the cylinder head vibration signal and ignore other vibration components and interference. The vibration signal is simplified as follows:

[0018]

[0019] Wherein, σ is defined as the attenuation damping coefficient; ω is the impact frequency generated by the valve seating impact.

[0020] 2. The process of constructing the dual-rate nonlinear state space model in step (3) is as follows:

[0021] Select cylinder head vibration signal ae σωt sinωt and valve seating impact frequency ω are used as two state variables, and ae is introduced σωt cosωt, then the three state variables are:

[0022]

[0023] The valve seating impact frequency is approximately constant over a period of time, so The update frequency of the cylinder head vibration signal and the impact frequency are inconsistent. The fast sampling vibration signal x1 is defined to be sampled and updated in real time at Δt, and the slow sampling impact frequency x3 is sampled and updated at time interval Δt. f After FFT update, define α as the ratio of fast sampling to slow sampling frequency, that is, the inverse of the time interval, and it is an integer:

[0024]

[0025] The relationship between the three state variables is:

[0026]

[0027] The dual-rate nonlinear continuous-time state space equation of the engine cylinder head vibration signal is obtained as follows:

[0028]

[0029] Where y a is the cylinder head vibration signal measurement output of the system; y a&f It is the dual measurement output of cylinder head vibration signal and valve seating impact frequency; f =j·Δt f (j=1, 2, ...) is the feedback moment of the seating impact frequency;

[0030] Assuming the sampling step length is Δt, the dual-rate nonlinear continuous-time state space equation of the engine cylinder head vibration signal is discretized. The state variable x is k-1 The derivative at time is approximately:

[0031]

[0032] t k, t k-1 Substitute k and k-1, and the above formula becomes:

[0033] x(k)=x(k-1)+Δt·f(x(k-1))

[0034] at this time,

[0035]

[0036] The discrete state space model is expressed as:

[0037]

[0038] in,

[0039] According to the dual-rate nonlinear continuous-time state-space equation of the engine cylinder head vibration signal, the Jacobian matrix A is obtained as:

[0040]

[0041] According to the observability matrix calculation formula R o =[C CA CA 2 ] T , the rank R of the observability matrix is ​​obtained by calculating o|a =[C1 C1A C1A 2 ] T =3, R o|a&f =[C2 C2A C2A 2 ] T =3, which proves that the state space equation of the nonlinear continuous system shown in the above formula is observable.

[0042] 3. Step (4) The process of designing the optimal estimation method for the engine cylinder head vibration signal is as follows:

[0043] Considering the process noise w(k) and measurement noise v(k), the formula can be Written as:

[0044]

[0045] Among them, w(k), v1(k) and v2(k) are all zero-mean and independent Gaussian white noise, and the covariance matrices are Q(k), R1(k) and R2(k), respectively, that is:

[0046] E(w(k))=0,

[0047] E(v1(k))=0,

[0048] E(v2(k))=0, According to the Kalman filter algorithm, let the estimated value of x(k) be A priori estimates and the posterior estimate First, the pair Perform linearization and obtain the posterior estimate of the state variable x(k) at the k-1 moment at the sampling time k exist At w(k-1)0=0, perform Taylor series expansion on x(k)=g(x(k-1),w(k-1)), omit the second-order and higher terms, and obtain the approximate linear expression of the state equation:

[0049]

[0050] Among them, A(k-1) and M(k-1) are x(k) in The Jacobian matrix at , we get:

[0051]

[0052] M(k-1) is a constant value:

[0053]

[0054] The optimal estimation method of cylinder head vibration signal based on dual-rate extended Kalman filter includes two updating processes: time update and measurement update. Error covariance matrix P(0) + After obtaining the noise covariance matrices Q, R1, and R2, the following time update process and measurement update process are used for calculation. The system state and covariance matrix are updated repeatedly to finally obtain the optimal estimation result of the cylinder head vibration signal. The specific steps are as follows.

[0055] Time update phase:

[0056] Using nonlinear models Calculate a priori estimates

[0057]

[0058] Calculate the Jacobian matrices A(k-1) and M(k-1) of the state variable x;

[0059] Calculate the prior estimate covariance matrix P(k) - :

[0060] P(k) - =A(k-1)P(k-1) + A(k-1) T+M(k-1)Q(k-1)M(k-1) T

[0061] Measurement update phase:

[0062] ① When k=1,2,...and k≠k f When the measurement update system only updates the cylinder head vibration signal feedback value y a (k), the posterior estimate of the third state variable shock frequency and the corresponding error prior remain unchanged, that is:

[0063]

[0064] P (3,3) (k) + =P (3,3) (k-1) +

[0065] The first two state variables are measured and updated using the cylinder head vibration measurement signal. The corresponding measurement update matrix is ​​C a =[1 0], the measurement covariance matrix is ​​R a , define the state variable x at this stage a =[x1 x2] T , the corresponding prior error is recorded as P a (k) - is P(k) - The upper left 2*2 matrix block;

[0066] Calculate the Kalman filter gain K a (k):

[0067] K a (k) = P a (k) - C a (k) T [C a (k)P a (k) - C a (k) T +R a (k)] -1

[0068] Feedback Correction Estimation System:

[0069]

[0070] Calculate the posterior estimation error covariance matrix P a (k) + :

[0071] P a (k)+ =(IK a (k)C a (k))P a (k) - (IK a (k)C a (k)) T +K a (k)R a (k)K a (k) T

[0072] At this point, the estimated output of the system is:

[0073]

[0074] ② When k=1,2,... and k=k f When the measurement update system updates the cylinder head vibration signal and the seating impact frequency feedback value at the same time, the corresponding prior error is recorded as P f (k) - and P(k) - Equal, and the corresponding measurement output feedback y f (k), measurement update matrix C f , measurement covariance matrix R f They are:

[0075]

[0076] Calculate the Kalman filter gain K f (k), feedback correction estimation system, calculation of posterior estimation error covariance matrix P f (k) + :

[0077] K f (k) = P f (k) - C f T [C f P f (k) - C f T +R f (k)] -1

[0078]

[0079] P f (k) + =(IK f (k)C f (k))P f (k)- (IK f (k)C f (k)) T +K f (k)R f (k)K f (k) T At this point, the estimated output of the system is:

[0080]

[0081] From the estimated value The estimated output of the system is circulated, and the system state, Kalman filter gain and covariance matrix are continuously updated to achieve real-time estimation of the engine cylinder head vibration signal.

[0082] 4. The process of step (5) is:

[0083] choose As the target value for fault diagnosis and monitoring. It can be seen that:

[0084]

[0085] The maximum absolute value of the vibration signal derivative in the healthy state is used as the calculation index, which is defined as:

[0086]

[0087] The threshold of fault diagnosis is defined as the derivative threshold Der th The criteria for determining the healthy status of the engine valve clearance are shown in the following table:

[0088]

[0089] The advantages of the present invention are:

[0090] 1. This paper proposes a new mathematical model for cylinder head vibration signals based on their characteristics. Based on this model, a new nonlinear state-space model for cylinder head vibration signals is established, using the cylinder head vibration signal, its correlation function, and the valve seating impact frequency as state variables.

[0091] 2. This paper discretizes a nonlinear state-space model and verifies its observability. To address measurement noise and model uncertainty issues in engineering applications, this paper incorporates both measurement and process noise into the model, linearizing it and improving its accuracy. This model is suitable for observing engine cylinder head vibration signals at varying speeds and loads.

[0092] 3. In view of the inconsistency between the update frequency of the cylinder head vibration signal and the valve seating impact frequency, the present invention proposes a real-time estimation method for the cylinder head vibration signal based on a dual-rate extended Kalman filter. By iteratively correcting the cylinder head vibration signal and the impact frequency, the real-time estimation of the engine cylinder head vibration signal is achieved.

[0093] 4. Based on the characteristics of engine valve clearance faults, this paper proposes a new online, real-time diagnosis method for valve clearance status. Based on the extended Kalman filter algorithm, a new method for obtaining the process derivative of the vibration signal is proposed. A fault diagnosis derivative threshold is set based on the process derivative, establishing a criterion for diagnosing abnormal engine valve clearance faults. This method enables online, real-time diagnosis of valve clearance status. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 is a flow chart of the present invention;

[0095] Figure 2 The test signal of the cylinder head vibration with normal valve clearance in a single cycle with a speed of 1500 rpm and a load of 20 kW;

[0096] Figure 3 The following is the identification result of the cylinder head vibration signal under normal valve clearance condition based on Laplace wavelet correlation filtering method at 1500rpm and 20kW;

[0097] Figure 4 The error bar graphs of 10 sets of identification results of cylinder head vibration signals under normal valve clearance conditions based on Laplace wavelet correlation filtering are shown at 1500rpm and 20kW.

[0098] Figure 5 Flowchart for estimating engine cylinder head vibration signal based on DREKF;

[0099] Figure 6 This is the 1s cylinder head vibration test signal with normal valve clearance under 1500rpm and 20kW operating conditions;

[0100] Figure 7 Estimation of cylinder head vibration signal based on DREKF at 1500rpm and 20kW;

[0101] Figure 8 Schematic diagram of the process derivative and derivative threshold value obtained by estimating the cylinder head vibration signal with normal valve clearance based on DREKF under the conditions of 1500 rpm and 20 kW;

[0102] Figure 9 This is the 1s cylinder head vibration test signal of abnormal valve clearance under 1500rpm and 20kW operating conditions;

[0103] Figure 10 Schematic diagram of the process derivative of the cylinder head vibration signal with abnormal valve clearance obtained based on DREKF under the working conditions of 1500rpm and 20kW;

[0104] Figure 11 This is a schematic diagram of the process derivative splicing of normal and abnormal valve clearance at 1500rpm and 20kW. DETAILED DESCRIPTION

[0105] The present invention will be described in more detail below with reference to the accompanying drawings:

[0106] Example:

[0107] Combine Figure 1-11 The present invention includes: acquiring the engine cylinder head vibration signal in real time, establishing and simplifying the mathematical model of the cylinder head vibration signal and determining the undetermined parameters; selecting appropriate state variables to construct the cylinder head vibration signal state space equation and verifying its observability; designing a dual-rate extended Kalman filter optimal estimation method, and inputting the vibration signal into the DREKF to obtain the cylinder head vibration signal estimation value; at the same time, calculating the process derivative and derivative threshold of the vibration signal based on the DREKF estimation, and accurately determining the valve clearance state according to the valve clearance state, so as to realize the online real-time fault diagnosis of the valve clearance of the engine.

[0108] The flow chart of real-time online diagnosis of abnormal engine valve clearance fault based on DREKF is as follows Figure 1 As shown, the following are the main steps of the present invention.

[0109] Step 1: According to the characteristics of the engine cylinder head vibration signal, a nonlinear mathematical model of the engine cylinder head vibration signal is established and simplified.

[0110] Step 2: Vibration signal parameter identification: Based on the Laplace wavelet correlation filter algorithm, the impact frequency, attenuation damping and other parameters of the target cylinder head vibration signal are identified to obtain a more accurate mathematical model and state space equation.

[0111] Step 3: Select appropriate state variables and construct a dual-rate nonlinear state-space model based on the state variables. Then, discretize the nonlinear state-space model and verify its observability.

[0112] Step 4: Incorporate process noise and measurement noise into the model and linearize the model to obtain the linear discrete system state space equation. Then, based on the dual-rate extended Kalman filter algorithm, design an optimal estimation method for the engine cylinder head vibration signal.

[0113] Step 5: A real-time online diagnosis method for abnormal valve clearance faults is proposed. The basic parameters of the DREKF algorithm are initially set. Then, using the process derivatives obtained from the DREKF-based estimation of the cylinder head vibration signal in Step 4 as monitoring target characteristic parameters, specific fault diagnosis parameters and thresholds are defined, and criteria for determining valve clearance status are set.

[0114] The following details are provided:

[0115] Step 1: According to the characteristics of the engine cylinder head vibration signal, a nonlinear mathematical model of the engine cylinder head vibration signal is established and simplified.

[0116] The vibration signal of the engine cylinder head is very complex, mainly including combustion impact, valve seating impact, valve throttling impact and reciprocating inertia force impact of piston connecting rod assembly. Figure 2 This is a single-cycle cylinder head vibration signal collected at 1500 rpm and 20 kW. Based on the characteristics of the vibration signal generated by the impact force acting on the engine cylinder head, the cylinder head vibration signal y can be approximated as the sum of multiple Laplace wavelet functions:

[0117]

[0118] Where a i is the amplitude of the vibration signal generated by the excitation; ω i is the impact frequency generated by the excitation; i is the attenuation damping generated by the excitation.

[0119] Among all the impacts in the cylinder head vibration signal, the valve seating impact is the main component, and when the valve clearance is abnormal, the valve seating impact becomes the component with the most significant change. In order to diagnose the abnormal valve clearance fault, it is first necessary to extract the seating impact component in the cylinder head vibration signal, ignoring other vibration components and interference. The vibration signal can then be simplified as follows:

[0120]

[0121] Wherein, σ is defined as the attenuation damping coefficient; ω is the impact frequency generated by the valve seating impact.

[0122] Step 2: Based on the Laplace wavelet correlation filtering algorithm, the impact frequency, attenuation damping and other parameters of the target monitored cylinder head vibration signal are identified.

[0123] Due to the similarity between the Laplace wavelet function and the vibration impact signal in the time-frequency domain, the present invention uses the Laplace wavelet correlation filtering method to perform parameter identification. The cylinder head vibration signal and the Laplace wavelet basis function are combined as the inner product, and the similarity between the cylinder head vibration signal and the Laplace wavelet at each moment is calculated, which is represented by the correlation coefficient k. y :

[0124]

[0125] Where y(t) is the vibration signal; ψ y (t) is the Laplace wavelet function, as shown in formula (2).

[0126] Based on Laplace wavelet correlation filtering algorithm Figure 2 The vibration signal shown in the figure is used for parameter identification, and the identification results are shown in the figure below. Figure 3 shown.

[0127] Correlation coefficient k y The amplitude and damping of the Laplace wavelet corresponding to the maximum value of is a and σ in formula (2).

[0128] The error bar graph of the identification results obtained by identifying 10 sets of single-cycle cylinder head vibration signals under 1500rpm and 20kw working conditions based on the Laplace wavelet correlation filtering algorithm is shown in the figure below: Figure 4 shown.

[0129] according to Figure 3 and Figure 4 The identification result diagram shows that the identification results of the cylinder head vibration signal are a=500、ξ=0.14. Calculation shows that σ = -0.14.

[0130] Then, formula (2) can be written as:

[0131] y=500·e -0.14·ωt sinωt (4)

[0132] Step 3: Select appropriate state variables and construct a dual-rate nonlinear state-space model based on the state variables. Then, discretize the nonlinear state-space model and verify its observability.

[0133] When the valve clearance is abnormal, the cylinder head vibration signal and the valve seat impact frequency will change. σωt Sinωt and valve seating impact frequency ω are used as two state variables. In order to estimate the cylinder head vibration signal more accurately, ae is introduced here. σωt cosωt.

[0134] The three state variables are:

[0135]

[0136] Considering that the valve seating impact frequency needs to be obtained based on FFT and is not updated for a period of time, that is, it is approximately constant for a period of time, we have In addition, since the update frequency of the cylinder head vibration signal and the impact frequency are inconsistent, the fast sampling vibration signal x1 is defined to be sampled and updated in real time at Δt, and the slow sampling impact frequency x3 is defined to be sampled and updated at a longer time interval Δt f Updated after FFT.

[0137] The sampling frequency in this embodiment is fs=51200 Hz, so Δt=1 / fs=1.953125×10 -5 s, setting, the impact frequency is updated every 0.2 seconds, then Δt f = 0.2s. Here, α is defined as the ratio of the fast sampling frequency to the slow sampling frequency, that is, the inverse of the time interval, and is an integer:

[0138]

[0139] Then, the relationship between the three state variables is:

[0140]

[0141] The dual-rate nonlinear continuous-time state space equation of the engine cylinder head vibration signal is obtained as follows:

[0142]

[0143] Where y a is the cylinder head vibration signal measurement output of the system; y f It is the dual measurement output of cylinder head vibration signal and valve seating impact frequency; f =j·Δt f (j=1, 2, ...) is the feedback moment of the seating impact frequency.

[0144] Assume that the sampling step is Δt, and discretize the continuous state space equation of Equation (8), then the state variable x at t k-1 The derivative at time can be approximated as:

[0145]

[0146] t k , t k-1 Substituting k and k-1, formula (9) is transformed into:

[0147] x(k)=x(k-1)+Δt·f(x(k-1)) (10)

[0148] at this time,

[0149]

[0150] Then the discrete state space model can be expressed as:

[0151]

[0152] Where, C1=[1 0 0],

[0153] Since Equation (8) is a nonlinear model, it is linearized by deriving the Jacobian matrix, and then its observability is judged according to the rank of the observability matrix. According to Equation (8), the Jacobian matrix A can be obtained as:

[0154]

[0155] According to the observability matrix calculation formula R o =[C CA CA 2 ] T , the rank R of the observability matrix is ​​obtained by calculating o|a =[C1 C1A C1A 2 ] T =3, R o|a&f =[C2 C2A C2A 2 ] T =3, which proves that the state space equation of the nonlinear continuous system shown in formula (8) is observable.

[0156] Step 4: Incorporate process noise and measurement noise into the model and linearize the model to obtain the linear discrete system state space equation. Then, based on the dual-rate extended Kalman filter algorithm, design an optimal estimation method for the engine cylinder head vibration signal.

[0157] Considering the process noise w(k) and measurement noise v(k), Equation (8) can be written as:

[0158]

[0159] Among them, w(k), v1(k) and v2(k) are all zero-mean and independent Gaussian white noise, and their covariance matrices are Q(k), R1(k) and R2(k) respectively. That is:

[0160]

[0161] According to the Kalman filter algorithm, let the estimated value of x(k) be A priori estimates and the posterior estimate First, linearize Equation (14). At sampling time k, according to the Kalman filter algorithm, the posterior estimate of the state variable x(k) at time k-1 can be obtained: exist At w(k-1)0=0, perform Taylor series expansion on x(k)=g(x(k-1),w(k-1)), omit the second-order and higher terms, and obtain the approximate linear expression of the state equation:

[0162]

[0163] Among them, A(k-1) and M(k-1) are x(k) in The Jacobian matrix at , according to formula (14), is obtained as follows:

[0164]

[0165] M(k-1) is a constant value:

[0166]

[0167] The flow chart of estimating engine cylinder head vibration signal based on DREKF is as follows Figure 5 As shown in the figure. The optimal estimation method of cylinder head vibration signal based on DREKF includes two update processes: time update and measurement update. Error covariance matrix P(0) + After obtaining the noise covariance matrix Q, R1, and R2, the calculation is performed according to the following time update process and measurement update process. The system state and covariance matrix are updated repeatedly, and the optimal estimation result of the cylinder head vibration signal is finally obtained.

[0168] The specific steps are as follows:

[0169] Time update phase:

[0170] Calculate the prior estimate using the nonlinear model (14)

[0171]

[0172] Compute the Jacobian matrices A(k-1) and M(k-1) of the state variable x.

[0173] Calculate the prior estimate covariance matrix P(k) - :

[0174] P(k) - =A(k-1)P(k-1) + A(k-1)T +M(k-1)Q(k-1)M(k-1) T (20)

[0175] Measurement update phase:

[0176] ① When k=1,2,...and k≠k f The measurement update system only updates the cylinder head vibration signal feedback value y a (k), the posterior estimate of the third state variable shock frequency and the corresponding error prior remain unchanged, that is:

[0177]

[0178] The first two state variables are measured and updated using the cylinder head vibration measurement signal. The corresponding measurement update matrix is ​​C a =

[10] , the measurement covariance matrix is ​​R a Define the state variable x at this stage a =[x1 x2] T , the corresponding prior error is recorded as P a (k) - is P(k) - The upper left 2*2 matrix block.

[0179] Calculate the Kalman filter gain K a (k):

[0180] K a (k) = P a (k) - C a (k) T [C a (k)P a (k) - C a (k) T +R a (k)] -1 (twenty two)

[0181] Feedback Correction Estimation System:

[0182]

[0183] Calculate the posterior estimation error covariance matrix P a (k) + :

[0184] P a (k) + =(IK a (k)C a (k))P a (k)- (IK a (k)C a (k)) T +K a (k)R a (k)K a (k) T (twenty four)

[0185] At this point, the estimated output of the system is:

[0186]

[0187] ② When k=1,2,... and k=k f When , the measurement update system updates the cylinder head vibration signal and the seating impact frequency feedback value at the same time. The corresponding prior error is recorded as P f (k) - and P(k) - Equal. And the corresponding measurement output feedback y f (k), measurement update matrix C f , measurement covariance matrix R f They are:

[0188]

[0189] Calculate the Kalman filter gain K f (k), feedback correction estimation system, calculation of posterior estimation error covariance matrix P f (k) + :

[0190] K f (k) = P f (k) - C f T [C f P f (k) - C f T +R f (k)] -1 (27)

[0191]

[0192] P f (k) + =(IK f (k)C f (k))P f (k) - (IK f (k)C f (k)) T +Kf (k)R f (k)K f (k) T (29) At this time, the estimated output of the system is:

[0193]

[0194] According to equations (19) to (30), the system state, Kalman filter gain and covariance matrix are continuously updated to achieve real-time estimation of the engine cylinder head vibration signal.

[0195] Step 5: Initially set the basic parameters of the DREKF algorithm. Then, based on the process derivative obtained by estimating the cylinder head vibration signal based on DREKF in step 4 as the monitoring target characteristic parameter, define the specific parameters and thresholds for fault diagnosis, and set the judgment criteria for the valve clearance status.

[0196] Set the initial value of the state variable Initial value of error covariance P(0) + , and the initial values ​​of Q, R1, and R2. The selection of Q, R1, and R2 mainly considers the influence of process noise and measurement noise on the filtering effect, and can be selected according to actual conditions. Figure 4 The identification results show that the seating impact amplitude and frequency are 500 and 5710.3 respectively. Therefore, the present invention is set as follows:

[0197]

[0198] In order to verify the estimation effect of the DREKF proposed in the present invention on the cylinder head vibration signal, the cylinder head vibration signal of the normal valve clearance state, 1500rpm, 20kw working condition, one second is selected, such as Figure 6 As shown. Figure 6 The cylinder head vibration signal samples shown in the figure are subjected to real-time optimal estimation to obtain the cylinder head vibration signal estimation value as shown in the figure. Figure 7 shown.

[0199] from Figure 6 、 Figure 7 The comparison shows that the DREKF designed based on the present invention can accurately estimate the cylinder head vibration signal. This proves the accuracy of the cylinder head vibration signal state space equation proposed in this paper and the effectiveness of using DREKF to estimate the cylinder head vibration signal.

[0200] The definitions of characteristic parameters and thresholds in step 5 and the criteria for determining valve clearance status are detailed below:

[0201] When an abnormal valve clearance fault occurs in the engine, it is directly manifested as a change in the rate of change of the cylinder head vibration signal, that is, the derivative of the cylinder head vibration signal changes. However, the cylinder head vibration signal contains the influence of measurement noise. Directly taking the derivative of the vibration signal will amplify the noise. Since y = x1, we choose As the target value for fault diagnosis and monitoring. From formula (7), we can know:

[0202]

[0203] The maximum absolute value of the derivative of the vibration signal process under normal valve clearance is used as the calculation index, which is defined here as:

[0204]

[0205] The threshold of fault diagnosis is defined as the derivative threshold Der th As shown in formula (34), the engine valve clearance state judgment criteria are shown in Table 1.

[0206] |Der th |=110%·Der max (34) Table 1 Engine valve clearance status judgment criteria

[0207]

[0208] The DREKF designed by the present invention is used to Figure 6 The cylinder head vibration signal samples are used for real-time optimal estimation, and the process derivative and derivative threshold Der of the cylinder head vibration signal with normal valve clearance can be obtained. th like Figure 8 As shown. The maximum absolute value of the process derivative is calculated to be 1.5247*10 6 , the derivative threshold is 1.6771*10 6 The valve clearance state judgment criteria set in Table 1 are shown in Table 2.

[0209] Table 2 Criteria for judging the status of engine valve clearance

[0210]

[0211] In order to verify the effectiveness of the valve clearance state judgment criterion proposed in the present invention, the cylinder head vibration signal with abnormal valve clearance under the same working conditions of 1500 rpm and 20 kW is used as a sample to perform DREKF estimation and real-time monitoring.

[0212] The cylinder head vibration signal sample of abnormal valve clearance under 1500rpm and 20kw working conditions is as follows: Figure 9 As shown. Applying the DREKF designed in this patent to perform real-time optimal estimation, the process derivative of valve clearance abnormality is obtained as follows Figure 10 shown.

[0213] The process derivatives and derivative thresholds obtained by DREKF for cylinder head vibration signal samples with normal and abnormal valve clearance are spliced ​​together to obtain a comparison chart of process derivatives of normal and abnormal valve clearance. Figure 11 shown.

[0214] Figure 11 In the figure, the solid line represents the process derivatives of normal and abnormal valve clearance, and the dotted line represents the derivative threshold value calculated under normal valve clearance. The first 1 second of the process derivatives is the process derivative of normal valve clearance calculated based on DREKF, and the last 1 second is the process derivative of abnormal valve clearance calculated based on DREKF. Figure 11 It can be seen that the process derivative under the abnormal valve clearance state obviously exceeds the derivative threshold range, which proves the effectiveness of the valve clearance state judgment criterion set in the present invention.

Claims

1. A real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF is characterized by: The steps include: (1) According to the characteristics of the engine cylinder head vibration signal, a nonlinear mathematical model of the engine cylinder head vibration signal is established and simplified; (2) Vibration signal parameter identification: The impact frequency and attenuation damping parameters of the target cylinder head vibration signal are identified based on the Laplace wavelet correlation filter algorithm; (3) Perform initial parameter identification on the cylinder head vibration signal to select state variables, and construct a dual-rate nonlinear state space model based on the state variables. Discretize the nonlinear state space model and verify the observability of the model. The process of constructing a dual-rate nonlinear state-space model is: Select cylinder head vibration signal ae σωt sinωt and valve seating impact frequency ω are used as two state variables, and ae is introduced σωt cosωt, then the three state variables are: The valve seating impact frequency is approximately constant over a period of time, so The update frequency of the cylinder head vibration signal and the impact frequency are inconsistent. The fast sampling vibration signal x1 is defined to be sampled and updated in real time at Δt, and the slow sampling impact frequency x3 is sampled and updated at time interval Δt. f After FFT update, define α as the ratio of fast sampling to slow sampling frequency, that is, the inverse of the time interval, and it is an integer: The relationship between the three state variables is: The dual-rate nonlinear continuous-time state space equation of the engine cylinder head vibration signal is obtained as follows: Where y a is the cylinder head vibration signal measurement output of the system; y a&f It is the dual measurement output of cylinder head vibration signal and valve seating impact frequency; f =j·Δt f (j=1, 2, ...) is the feedback moment of the seating impact frequency; (4) Taking process noise and measurement noise into account in the model, and linearizing the model to obtain the linear discrete system state space equation, based on the dual-rate extended Kalman filter algorithm, the optimal estimation method of the engine cylinder head vibration signal is designed; (5) The basic parameters of the DREKF algorithm are identified and set. Then, the process derivative obtained by estimating the cylinder head vibration signal based on DREKF in step (3) is used as the monitoring target, the specific parameters and thresholds of the fault diagnosis are defined, and the judgment criteria of the valve clearance status are set.

2. The real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF according to claim 1 is characterized by: The process of establishing the nonlinear mathematical model of the engine cylinder head vibration signal in step (1) is as follows: The cylinder head vibration signal y is approximated as the sum of multiple Laplace wavelet functions: Where a i is the amplitude of the vibration signal generated by the excitation; ω i is the impact frequency generated by the excitation; i is the attenuation damping generated by the excitation; Extract the seating impact component from the cylinder head vibration signal and ignore other vibration components and interference. The vibration signal is simplified as follows: Wherein, σ is defined as the attenuation damping coefficient; ω is the impact frequency generated by the valve seating impact.

3. The real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF according to claim 1 is characterized by: In step 3, the sampling step is set to Δt, and the dual-rate nonlinear continuous-time state space equation of the engine cylinder head vibration signal is discretized. The state variable x is k-1 The derivative at time is approximately: t k , t k-1 Substitute k and k-1, and the above formula becomes: x(k)=x(k-1)+Δt·f(x(k-1)) at this time, The discrete state space model is expressed as: Where, C1=[1 0 0], According to the dual-rate nonlinear continuous-time state-space equation of the engine cylinder head vibration signal, the Jacobian matrix A is obtained as: According to the observability matrix calculation formula R o =[C CA CA 2 ] T , the rank R of the observability matrix is ​​obtained by calculating o|a =[C1C1A C1A 2 ] T =3, R o|a&f =[C2 C2A C2A 2 ] T =3, which proves that the state space equation of the nonlinear continuous system shown in the above formula is observable.

4. The real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF according to claim 3 is characterized by: Step (4) The process of designing the optimal estimation method for the engine cylinder head vibration signal is as follows: Considering the process noise w(k) and measurement noise v(k), the formula can be Written as: Among them, w(k), v1(k) and v2(k) are all zero-mean and independent Gaussian white noise, and the covariance matrices are Q(k), R1(k) and R2(k), respectively, that is: E(w(k))=0, E(v1(k))=0, E(v2(k))=0, According to the Kalman filter algorithm, let the estimated value of x(k) be A priori estimates and the posterior estimate First, the pair Perform linearization and obtain the posterior estimate of the state variable x(k) at the k-1 moment at the sampling time k exist At w(k-1)0=0, perform Taylor series expansion on x(k)=g(x(k-1),w(k-1)), omit the second-order and higher terms, and obtain the approximate linear expression of the state equation: Among them, A(k-1) and M(k-1) are x(k) in The Jacobian matrix at , we get: M(k-1) is a constant value: The optimal estimation method of cylinder head vibration signal based on dual-rate extended Kalman filter includes two updating processes: time update and measurement update. Error covariance matrix P(0) + After obtaining the noise covariance matrix Q, R1, and R2, the following time update process and measurement update process are used for calculation. The system state and covariance matrix are updated repeatedly to finally obtain the optimal estimation result of the cylinder head vibration signal. The specific steps are as follows: Time update phase: Using nonlinear models Calculate a priori estimates Calculate the Jacobian matrices A(k-1) and M(k-1) of the state variable x; Calculate the prior estimate covariance matrix P(k) - : P(k) - =A(k-1)P(k-1) + A(k-1) T +M(k-1)Q(k-1)M(k-1) T Measurement update phase: ① When k=1,2,...and k≠k f When the measurement update system only updates the cylinder head vibration signal feedback value y a (k), the posterior estimate of the third state variable shock frequency and the corresponding error prior remain unchanged, that is: P (3,3) (k) + =P (3,3) (k-1) + The first two state variables are measured and updated using the cylinder head vibration measurement signal. The corresponding measurement update matrix is ​​C a =[10], the measurement covariance matrix is ​​R a , define the state variable x at this stage a =[x1 x2] T , the corresponding prior error is recorded as P a (k) - is P(k) - The upper left 2*2 matrix block; Calculate the Kalman filter gain K a (k): K a (k)=P a (k) - C a (k) T [C a (k)P a (k) - C a (k) T +R a (k)] -1 Feedback Correction Estimation System: Calculate the posterior estimation error covariance matrix P a (k) + : P a (k) + =(IK a (k)C a (k))P a (k) - (IK a (k)C a (k)) T +K a (k)R a (k)K a (k) T At this point, the estimated output of the system is: ② When k=1,2,... and k=k f When the measurement update system updates the cylinder head vibration signal and the seating impact frequency feedback value at the same time, the corresponding prior error is recorded as P f (k) - and P(k) - Equal, and the corresponding measurement output feedback y f (k), measurement update matrix C f , measurement covariance matrix R f They are: Calculate the Kalman filter gain K f (k), feedback correction estimation system, calculation of posterior estimation error covariance matrix P f (k) + : K f (k)=P f (k) - C f T [C f P f (k) - C f T +R f (k)] -1 P f (k) + =(IK f (k)C f (k))P f (k) - (IK f (k)C f (k)) T +K f (k)R f (k)K f (k) T At this point, the estimated output of the system is: From the estimated value The estimated output of the system is circulated, and the system state, Kalman filter gain and covariance matrix are continuously updated to achieve real-time estimation of the engine cylinder head vibration signal.

5. The real-time online diagnosis method for abnormal engine valve clearance fault based on DREKF according to claim 2 is characterized by: The process of step (5) is: choose As the target value of fault diagnosis monitoring, It can be seen that: The maximum absolute value of the vibration signal derivative in the healthy state is used as the calculation index, which is defined as: The threshold of fault diagnosis is defined as the derivative threshold Der th The criteria for determining the healthy status of the engine valve clearance are shown in the following table: 。

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