CN112431753B - Multiple quantitative diagnosis method for shoe loosening fault of axial plunger pump - Google Patents

Abstract

The invention belongs to the technical field of fault diagnosis, and particularly relates to a multiple quantitative diagnosis method for a shoe loosening fault of an axial plunger pump, which comprises the following steps: s1. Acquiring an axial vibration signal a and a sound signal s of an axial plunger pump, and preprocessing the axial vibration signal a and the sound signal s; s2, constructing a two-channel third-order tensor signal T through the preprocessed axial vibration signal a and the preprocessed sound signal S; s3, carrying out self-adaptive decomposition on the dual-channel third-order tensor signal T, and carrying out reconstruction to screen out the optimal component signal a_o(ii) a S4, according to the optimal component signal a_oAnd judging whether the axial plunger pump has a shoe loosening fault or not. According to the multiple quantitative diagnosis method for the shoe loosening fault of the axial plunger pump, the size of the fault is accurately calculated on the basis of the diagnosis of the shoe loosening fault, production safety accidents caused by the shoe loosening fault are avoided, and the production cost can be saved.

Description

Multiple quantitative diagnosis method for shoe loosening fault of axial plunger pump

Technical Field

The invention belongs to the technical field of fault diagnosis, and particularly relates to a multiple quantitative diagnosis method for a shoe loosening fault of an axial plunger pump.

Background

The axial plunger pump has the characteristics of compact structure, high efficiency, high pressure and the like, and is widely applied to important industrial equipment such as environment-friendly water treatment, loaders, aircraft engines, ship engines and the like. Due to factors such as working environment, operating conditions and structural form, the axial plunger pump inevitably generates abrasion faults of different degrees in the use process. If the abrasion fault develops to a certain extent, serious production safety accidents can be caused, and economic loss and even casualties can be caused. Therefore, the online monitoring and fault diagnosis technology for the axial plunger pump is significant. The loose-boot fault is one of typical fault modes of the axial plunger pump, and the existing fault diagnosis technology based on the spectrum analysis can only qualitatively diagnose whether the loose-boot fault exists or not, but cannot quantitatively diagnose the degree of the loose-boot fault and the size of the fault. Moreover, these qualitative diagnostic techniques based on spectrum analysis are also susceptible to noise interference, differences in operating conditions, and other factors, which may lead to misdiagnosis or missed diagnosis.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the fault diagnosis technology based on the spectrum analysis can only qualitatively diagnose whether the loose boot fault exists, but cannot quantitatively diagnose the degree of the fault and the size of the fault. Moreover, the qualitative diagnosis technologies based on the spectrum analysis are also easily affected by noise interference, working condition difference and other factors to cause the technical problems of misdiagnosis or missed diagnosis. The invention aims to provide a quantitative diagnosis method for boot loosening faults of an axial plunger pump, which is used for accurately calculating the sizes of the faults on the basis of the diagnosis of the boot loosening faults and aims to provide basic support for online monitoring and operation and maintenance management of the axial plunger pump, so that production safety accidents caused by the boot loosening faults can be avoided, and a large amount of operation and maintenance cost can be saved.

The technical scheme adopted by the invention for solving the technical problems is as follows: a multiple quantitative diagnosis method for shoe loosening faults of an axial plunger pump comprises the following steps:

s1, acquiring an axial vibration signal a and a sound signal S of the axial plunger pump, and preprocessing the axial vibration signal a and the sound signal S;

s2, constructing a two-channel third-order tensor signal T through the preprocessed axial vibration signal a and the preprocessed sound signal S;

s3, carrying out self-adaptive decomposition on the dual-channel third-order tensor signal T, and carrying out reconstruction to screen out the optimal component signal a_o；

S4, according to the optimal component signal a_oAnd judging whether the axial plunger pump has a shoe loosening fault or not.

Preferably, in step S1, the axial vibration signal a and the sound signal S are preprocessed into an axial vibration signal matrix a and a sound signal matrix S by a period segmentation method.

Further, in step S2, the axial vibration signal matrix a and the sound signal matrix S are configured as a two-channel third order tensor signal T by a stacking method, wherein the two-channel third order tensor signal T includes: first, second and third stages.

Further, step S3 further includes:

s31, transposing the second order and the third order of the dual-channel third order tensor signal T;

s32, horizontally unfolding the front slice of the double-channel third-order tensor signal T to obtain a matrix M;

s33, performing singular value decomposition on the matrix M to obtain a left singular matrix U, a core matrix G and a right singular matrix V;

s34, stacking out core tensor R according to the core matrix G^kAnd according to the core tensor R^kCalculating the intermediate tensor C by the left singular matrix U^k；

S35, stacking the right singular tensor P according to the right singular matrix V, and stacking the right singular tensor P and the middle tensor C according to the right singular tensor P^kCalculating a reconstruction tensor T^k(ii) a S36, reconstructing tensor T^kMultiple component signals a developed into axial vibration signals a^k；

S37, calculating each component signal a^kScreening out the optimal component signal a by the envelope spectrum_o。

Further, in step S4, the optimum component signal a is plotted_oAnd setting the rotation period of the main shaft of the axial plunger pump to be T_rWith T_rFor periodic optimization of the component signal a_oThe time domain oscillogram is segmented, the peak value in each segment is extracted, the time interval between every 2 adjacent peak values is measured, and the average value T of the time interval is calculated_pIf | T is satisfied_p-T_r|>And 5%, judging that the axial plunger pump has no shoe loosening fault, otherwise, judging that the axial plunger pump has the shoe loosening fault.

Further, the multiple quantitative diagnosis method for the shoe loosening fault of the axial plunger pump further comprises the following steps:

s5, according to T_rAnd judging the degree of the shoe loosening fault by the peak value of the impact waveform of the waveform diagram in the period and the number of the impact waveforms.

Further, the step S5 includes:

s51, calculating the optimal component signal a_oRoot mean square value of_aThe maximum amplitude of the waveform is recorded as a peak value, and the peak value of the waveform is more than 2R_aRecord as impulse waveform if 1 complete T_rJudging the fault as a slight fault if only 1 impact waveform exists in the period;

s52, if 1 complete T _r2 impact waveforms exist in the period, and the distance between the 2 impact waveforms is set to be T_aIf | T is satisfied_a-T_r/N_p|<5%, and no 3 rd peak value between the 2 impact waveforms is larger than R_aIf the waveform is a medium fault, judging the medium fault;

s53, if 1 complete T _r2 impact waveforms exist in the period, and the distance between the 2 impact waveforms is set to be T_aIf | T is satisfied_a-T_r/N_p|<5%, and more than 1 impact waveform exists between the 2 impact waveforms, judging the fault as a severe fault;

wherein N is_pThe number of plungers of the axial plunger pump.

Preferably, if it is determined in step S5 that the fault is a serious fault, the process proceeds to step S6,

s6, dividing 1 complete T_rMarking 2 impact waveforms in a period as a first impact waveform and a second impact waveform respectively, marking the maximum amplitude of the impact waveform between the first impact waveform and the second impact waveform as a peak point, marking the 1 st extreme point of the first impact waveform as a starting point, and marking the time difference between the measured starting point and the peak point as T_dCalculating the width L of the shoe loosening fault_d：

Wherein, R is the revolution radius of the plunger around the shaft, and delta is the inclination angle of the swash plate.

The invention has the beneficial effects that the multiple quantitative diagnosis method for the shoe loosening fault of the axial plunger pump has the following specific effects: the invention integrates the axial vibration signal a and the sound signal s to construct a double channelBased on the proposed self-adaptive decomposition algorithm of the two-channel third-order tensor signal T, the three-order tensor signal T removes noise interference in an original signal, and simultaneously fully utilizes the inherent coupling relation between the axial vibration signal a and fault characteristic information in a sound signal s to extract an optimal component signal a_oBy applying to the optimum component signal a_oThe characteristic excavation is carried out, triple diagnosis criteria of the shoe loosening fault of the axial plunger pump are provided, whether the axial plunger pump has the shoe loosening fault or not can be diagnosed through the first double diagnosis, different fault degrees of the axial plunger pump can be diagnosed through the second double diagnosis, and different processing modes can be arranged subsequently. For example: if the fault is judged to be slight, the axial plunger pump can continue to operate; if the fault is determined to be serious, the replacement of the fault part needs to be considered. And the width of the shoe loosening fault during severe fault can be further judged through third triple diagnosis, when the width is larger than a certain width value, parts must be replaced, otherwise, the machine is damaged, and production safety accidents are caused.

Drawings

The invention is further illustrated with reference to the following figures and examples.

FIG. 1 is a flow chart of the operation of the multiple quantitative diagnosis method for shoe loosening fault of axial plunger pump of the present invention;

FIG. 2 is a time domain waveform diagram of an axial plunger pump axial vibration signal a and an acoustic signal s according to the multiple quantitative diagnosis method for the shoe loosening fault of the axial plunger pump of the present invention;

FIG. 3 is a schematic diagram illustrating the criteria for a minor fault in the multiple quantitative diagnosis method for shoe loosening fault of the axial plunger pump according to the present invention;

FIG. 4 is a schematic diagram illustrating a medium fault criterion of the multiple quantitative diagnosis method for the shoe loosening fault of the axial plunger pump according to the present invention;

fig. 5 is a schematic diagram illustrating the criterion of severe fault in the multiple quantitative diagnosis method for shoe loosening fault of the axial plunger pump.

Reference numerals:

1. first shock waveform, 2, second shock waveform, 3, peak point, 4, start point.

Detailed Description

The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.

As shown in fig. 1 to 5, which are preferred embodiments of the present invention, a method for diagnosing loosening shoe failure of an axial plunger pump in multiple quantification modes includes the following specific steps:

s1, acquiring an axial vibration signal a and a sound signal S of the axial plunger pump, and preprocessing the axial vibration signal a and the sound signal S;

referring to fig. 1, which is a working flow chart of the present invention, firstly, a vibration acceleration sensor and a sound sensor are used to obtain an axial vibration signal a and a sound signal s of an axial plunger pump, as shown in fig. 2, a sampling frequency is F_sNumber of samples N =100000 at =5kHz, using a respectively_iAnd s_iThe ith element representing the axial vibration signal a and the sound signal s; respectively converting the axial vibration signal a and the sound signal S from a vector into a matrix A and a matrix S, A by using a period segmentation method_m,nAnd S_m,nThe elements in the mth row and nth column of matrix a and matrix S respectively are expressed by the following conversion formula:

(1)

wherein floor () represents a downward integer to the number in brackets, the number of rows N of matrix A and matrix S_r=floor(T_rF_s／N_p) Wherein, T_r=40ms is the axial plunger pump spindle rotation period, N_p=9 is the number of plungers of the axial plunger pump;

s2, constructing a two-channel third-order tensor signal T through the preprocessed axial vibration signal a and the preprocessed sound signal S;

by using pilesThe folding method constructs the matrixes A and S into two-channel third-order tensor signals T and I₁、I₂And I₃Representing the dimensions of order 1,2 and 3 of T, respectively_i1,i2,i3Any element of the two-channel third-order tensor signal T is represented, I is more than or equal to 1 and less than or equal to I1 and less than or equal to I₁，1≤i2≤I₂，1≤i3≤I₃Wherein I1=1,2, … I₁，i2=1,2,…I₂，i3=1,2,…I₃Then, the following conversion formula is shown:

(2)

wherein, T_:,:,1And T_:,:,2The 1 st and 2 nd frontal slices, respectively, representing T;

s3, carrying out self-adaptive decomposition on the dual-channel third-order tensor signal T, and carrying out reconstruction to screen out the optimal component signal a_o；

Transposing the 2 nd order and the 3 rd order of the double-channel third-order tensor signal T, and horizontally unfolding the front slice to obtain a matrix M, wherein the matrix M is expressed by a formula:

(3)

wherein the content of the first and second substances,

and

and

the 1 st, 2 nd and I th side slices representing T, respectively₂Slicing the lateral surface; performing singular value decomposition on the matrix M to obtain a left singular matrix U, a core matrix G and a right singular matrix V; by G_kK-th diagonal element, K =1,2, …, K, representing the core matrix G；K=min[I₁,I₂I₃]Min denotes taking the minimum value, I₂I₃Is represented by₂And I₃The product of (a); constructing k matrices G by using the following formula^k：

(4)

Wherein the content of the first and second substances,

representing a number of lines I₁The number of columns is I₂I₃A set of matrices.

Matrix G is arranged from left to right and from top to bottom^kIs divided into₁A 1₂×I₃Each sub-matrix is erected into a side slice, and core tensors R are stacked in a left-to-right order^k. Calculating the intermediate tensor

In which the operation sign

Representing a tensor modal product; the matrix V is divided into I in the order from left to right and from top to bottom₁A 1₂×I₃Each submatrix is taken as a side slice, a right singular tensor P is stacked, and a reconstruction tensor is calculated

(ii) a Extracting a reconstructed tensor T ^k1 st front section T of^k _:,:,1The order matrix M^k=T^k _:,:,1(ii) a Will matrix M^kIs expanded into component signals a in an end-to-end manner from the 1 st column to the last 1 st column^k(ii) a Calculating each component signal a^kEnvelope spectrum e of^k(f_n) Wherein f is_nIs an independent variable, e^k() An envelope spectrum magnitude function; let f_n=N_p/T_rThe amplitude of the envelope spectrum of

The optimum component signal a is extracted by the following formula_o：

(5)

Wherein the content of the first and second substances,

expression calculationkAn

The maximum value of (a) is,

representation finding

Corresponding tokTaking the value of (A);

s4, according to the optimal component signal a_oJudging whether the axial plunger pump has a shoe loosening fault or not;

plotting the optimal component signal a_oAnd setting the rotation period of the main shaft of the axial plunger pump to be T_rWith T_rFor periodic optimization of the component signal a_oThe time domain oscillogram is segmented, the peak value in each segment is extracted, the time interval between every 2 adjacent peak values is measured, and the average value T of the time interval is calculated_pIf | T is satisfied_p-T_r|>And 5%, judging that the axial plunger pump has no shoe loosening fault, otherwise, judging that the axial plunger pump has the shoe loosening fault.

S5, according to T_rJudging the degree of the shoe loosening fault by the peak value of the impact waveform of the waveform diagram in the period and the quantity of the impact waveforms;

calculating an optimal component signal a_oOf the frequency spectrum ofValue off _maxThen, define the impulse waveform as: optimum component signal a_oThe time domain waveform takes the step point as the starting point and the oscillation frequencyf >4f _maxThe high frequency oscillation of (1) attenuates the waveform.

If it is determined in step S4 that there is a boot loosening failure, the optimal component signal a is calculated_oRoot mean square value of_aAnd in the optimum component signal a_oIs marked with T in the time domain waveform diagram_rFor a periodically occurring impulse waveform, for 1 complete T_rThe waveform in the cycle is judged as follows: s51, calculating the optimal component signal a_oRoot mean square value of_aThe maximum amplitude of the waveform is recorded as a peak value, and the peak value of the waveform is more than 2R_aRecord as impulse waveform if 1 complete T_rIf only 1 impact waveform exists in the period, the fault is judged to be a slight fault, as shown in fig. 3; s52, if 1 complete T_r2 impact waveforms exist in the period, and the distance between the 2 impact waveforms is set to be T_aIf | T is satisfied_a-T_r/N_p|<5%, and no 3 rd peak value between the 2 impact waveforms is larger than R_aThe waveform of (2) is determined to be a medium fault, as shown in fig. 4; s53, if 1 complete T_r2 impact waveforms exist in the period, and the distance between the 2 impact waveforms is set to be T_aIf | T is satisfied_a-T_r/N_p|<If more than 1 impact waveform exists between the 2 impact waveforms, the fault is determined to be a severe fault, as shown in fig. 5; wherein N is_pThe number of plungers of the axial plunger pump.

If it is determined in step S5 that the fault is a serious fault, the judgment of the size of the loose boot fault is continued, and S6 and S6 are executed to count 1 complete T_rMarking 2 impact waveforms in a period as a first impact waveform 1 and a second impact waveform 2 respectively, marking the maximum amplitude of the impact waveform between the first impact waveform 1 and the second impact waveform 2 as a peak point 3, marking the 1 st extreme point of the first impact waveform 1 as a starting point 4, and marking the time difference between the measured starting point 4 and the peak point 3 as T_dAs shown in FIG. 5, T may be extracted quantitatively in the example given in FIG. 5_d=3msCalculating the width L of the loose boot fault by the following formula_d：

(6)

Where R =46mm is the revolution radius of the plunger around the shaft, and δ =14 ° is the inclination angle of the swash plate, the shoe loosening failure width L in the example shown in fig. 5 can be calculated by the equation (6)_d=1.75 mm. In this embodiment, when L is_d>At 2mm, the faulty component must be replaced, otherwise it will lead to the destruction of the machine.

The invention integrates the axial vibration signal a and the sound signal s to construct a double-channel third-order tensor signal T, and extracts the optimal component signal a by fully utilizing the inherent coupling relation between the axial vibration signal a and the fault characteristic information in the sound signal s while removing the noise interference in the original signal based on the proposed self-adaptive decomposition algorithm of the double-channel third-order tensor signal T_oBy applying to the optimum component signal a_oThe characteristic excavation is carried out, triple diagnosis criteria of the shoe loosening fault of the axial plunger pump are provided, whether the axial plunger pump has the shoe loosening fault or not can be diagnosed through the first double diagnosis, different fault degrees of the axial plunger pump can be diagnosed through the second double diagnosis, and different processing modes can be arranged subsequently. For example: if the fault is judged to be slight, the axial plunger pump can continue to operate; if the fault is determined to be serious, the replacement of the fault part needs to be considered. And the width of the shoe loosening fault during severe fault can be further judged through third triple diagnosis, when the width is larger than a certain width value, parts must be replaced, otherwise, the machine is damaged, and production safety accidents are caused.

In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (1)

1. A multiple quantitative diagnosis method for shoe loosening faults of an axial plunger pump is characterized by comprising the following steps:

s1, acquiring an axial vibration signal a and a sound signal S of the axial plunger pump, and preprocessing the axial vibration signal a and the sound signal S;

s2, constructing a two-channel third-order tensor signal T through the preprocessed axial vibration signal a and the preprocessed sound signal S;

s3, carrying out self-adaptive decomposition on the dual-channel third-order tensor signal T, and carrying out reconstruction to screen out the optimal component signal a_o；

S4, according to the optimal component signal a_oJudging whether the axial plunger pump has a shoe loosening fault or not;

in step S1, preprocessing the axial vibration signal a and the sound signal S into an axial vibration signal matrix a and a sound signal matrix S by a period segmentation method;

in step S2, the axial vibration signal matrix a and the sound signal matrix S are constructed as a two-channel third order tensor signal T using a stacking method, wherein the two-channel third order tensor signal T includes: a first, second and third stage;

step S3 further includes: s31, transposing the second order and the third order of the dual-channel third order tensor signal T;

s32, horizontally unfolding the front slice of the double-channel third-order tensor signal T to obtain a matrix M;

s33, performing singular value decomposition on the matrix M to obtain a left singular matrix U, a core matrix G and a right singular matrix V;

s34, stacking out core tensor R according to the core matrix G^kAnd according to the core tensor R^kCalculating the intermediate tensor C by the left singular matrix U^k；

S35, stacking the right singular tensor P according to the right singular matrix V,and from the right singular tensor P and the intermediate tensor C^kCalculating a reconstruction tensor T^k；

S36, reconstructing tensor T^kMultiple component signals a developed into axial vibration signals a^k；

S37, calculating each component signal a^kScreening out the optimal component signal a by the envelope spectrum_o。

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