CN115270843A - Fault diagnosis method and device for floating platform reciprocating compressor - Google Patents
Fault diagnosis method and device for floating platform reciprocating compressor Download PDFInfo
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Abstract
The invention discloses a fault diagnosis method and a fault diagnosis device for a floating platform reciprocating compressor, which comprises the steps of decomposing a vibration signal to be processed to obtain an IMF component; screening certain orders of IMFs containing obvious impact characteristics through indexes; filtering the screened IMF components respectively to filter non-impact components; reconstructing the IMF component after the adaptive variable-scale morphological filtering to obtain a noise reduction signal; amplifying a sudden change component in the noise reduction signal; calculating the envelope of the abrupt change component, and performing smooth filtering on the envelope by using a filter; carrying out peak detection to extract the phase characteristics of impact components in the signals; and acquiring the phase characteristics of the impact. The diagnostic device includes: the system comprises an acquisition module, a processing module, an extraction module and a diagnosis module, wherein a reciprocating compressor fault characteristic knowledge base is established, and fault diagnosis is carried out according to vibration signal impact characteristics captured by the extraction module. And provides support and guarantee for the predictive maintenance of the reciprocating compressor of the ocean platform in the future.
Description
Technical Field
The invention relates to the field of fault diagnosis of floating platform reciprocating compressors, in particular to a method and a device for extracting and diagnosing a fault of a floating platform reciprocating compressor by vibration signal impact characteristics.
Background
The large reciprocating piston type compressor is used as a power source for compressing and conveying media, is widely applied to the fields of petroleum, chemical engineering, refrigeration and the like, is also one of key equipment of an ocean platform, and is important for safe, reliable and stable long-period operation. When some faults of the reciprocating compressor occur, the increase of the impact frequency or the movement of the impact phase position monitored by the vibration sensor at the structures such as the middle body, the valve cover, the crankcase and the like can be often caused, and the faults can be diagnosed initially by monitoring the impact frequency and the impact phase position in a period in real time. For example, when a small head tile has a wear failure, additional impact can occur near the piston reversing dead center; when the air inlet and exhaust valves have faults such as spring failure, valve plate fracture and the like, the opening and seating of the air inlet and exhaust valves are advanced or delayed, so that acceleration impact phases at the valve cover and the cylinder body are moved; as the large head shoe wears, two impacts per cycle are generated in the crankcase speed signal. By monitoring the number, peak and phase of impacts to the body, valve or crankcase of the compressor during a cycle, a preliminary diagnosis of a fault can be made.
The body acceleration signal in the reciprocating compressor comprises a harmonic signal related to the rotation period of the crank, an impact signal related to the running state of equipment and an environmental noise signal. In order to accurately extract the impulse component in the signal, it is necessary to suppress harmonic components in the signal in addition to noise reduction of the signal. The conventional linear filter is used for filtering harmonic components in a signal, and sufficient priori knowledge is required to be provided, so that the frequencies of all harmonics can be accurately predicted, and an effective notch filter can be applied to suppress the harmonic components. In addition, once the frequency of the harmonic coincides with the frequency component contained in the impact signal, the impact feature is lost, which is contrary to the target for accurately extracting the impact feature.
Disclosure of Invention
The invention aims to solve the technical problem of a fault diagnosis method and a fault diagnosis device for a floating platform reciprocating compressor, which are used for extracting the impact characteristics of a vibration signal of the floating platform reciprocating compressor and realizing fault diagnosis.
In order to solve the above technical problem, the present invention provides a fault diagnosis device for a floating platform reciprocating compressor, comprising:
the acquisition module is used for acquiring vibration data of the floating platform reciprocating compressor and acquiring and transmitting the vibration data to an upper computer for processing;
the processing module is connected with the acquisition module and is used for processing the acquired vibration signals through EEMD-adaptive variable-scale morphological filtering;
and the extraction module is connected with the processing module and used for carrying out peak detection to extract the phase characteristics of the impact components in the signals and capturing the parameter characteristics of impact.
The diagnosis and evaluation module is connected with the extraction module, establishes a fault characteristic knowledge base of the reciprocating compressor, compares the fault characteristic knowledge base according to the vibration signal impact characteristics captured by the extraction module, performs fault diagnosis and outputs a diagnosis result;
and the visualization module is connected with the diagnosis evaluation module and the acquisition module and is used for visualizing the diagnosis result and the acquired data. Meanwhile, webpage interactive access is carried out through the remote terminal to realize remote data processing and calling.
The other technical scheme is as follows: a method for diagnosing the fault of a reciprocating compressor of a floating platform is characterized by comprising the following steps:
s01: decomposing a vibration signal to be processed to obtain a plurality of IMF components;
s02: screening out certain orders of IMFs containing obvious impact characteristics from a plurality of IMF components through indexes;
s03: respectively carrying out self-adaptive variable-scale morphological filtering on the screened IMF components to filter out non-impact components;
s04: reconstructing the IMF component after the adaptive variable-scale morphological filtering to obtain a noise reduction signal;
s05: amplifying amplitude-frequency sudden change components in the noise reduction signal;
s06: calculating the envelope of the abrupt change component, and performing smooth filtering on the envelope by using a smoothing filter;
s07: carrying out peak phase detection to extract the phase characteristics of the impact components in the signals;
s08: after the phase characteristics of the impact are obtained, capturing the parameter characteristics to further carry out fault diagnosis on the reciprocating compressor;
s09: and visualizing the diagnosis result and the acquired data, and remotely processing and calling the data.
Further, the step S02 uses a kurtosis-correlation index, which includes:
first, kurtosis index:
for discrete signals x (i) = { xi| i =1,2, \8230;, n }, where n is the number of signal points, kurtosis K is defined as:
in the formula
Is the mean of x (i) and is defined as:
respectively calculating kurtosis values of all orders of components, and rejecting components with kurtosis values smaller than 5.0 to realize the first-step screening of IMF components;
step two, correlation indexes are as follows:
calculating the correlation coefficient of each IMF component and the original function, and assuming that there are two continuous time domain signals x (t), y (t), the correlation coefficient R (xy) is defined as:
and selecting IMF with the correlation coefficient larger than 10% to realize the second step of screening.
Further: in step S03, the adaptive variable-scale morphological filtering includes:
for a discrete signal f (N) with a total number of points N, where the argument N =0,1, \ 8230, N-1 and a sequence of structural elements g (M) with a total number of points M, where the argument M =0,1, \ 8230, M-1, N < M, a dilation operator is defined
And erosion operator
Comprises the following steps:
wherein,
denotes the expansion of f (n) with respect to g (m),
denotes the corrosion of f (n) with respect to g (m).
The morphology open operator (°) and morphology closed operator (·) are defined by a sequential combination of dilation and erosion operators:
wherein (f DEG g) (n) represents a morphological opening operator of f (n) with respect to g (m), and (f · g) (n) represents a morphological closing operator of f (n) with respect to g (m).
Further, morphology on-Off (OC) and morphology off-on (CO) operators are defined by sequential combinations of morphology on operators and morphology off operators:
OC[f(n)]＝(f°g·g)(n)
CO[f(n)]＝(f·g°g)(n)
wherein OC [ f (n) ] represents the form open/close operator of f (n), and CO [ f (n) ] represents the form open/close operator of f (n).
A cascade morphological filter Γ [ f (n) ] formed by combining a morphological open-close operator and a morphological closed-open operator to f (n):
further, the filter structure element uses a flat structure with a height of 0, and a structure element sequence g (N, m, k) (N =0,1, \8230; N-1) is defined for a discrete signal f (N) (N =0,1, \8230; N-1, m =0,1, \8230; k-1), where N is the number of signal points and k is the structure element width of the nth sampling point, and the width k is determined as follows:
(1) The phase and amplitude of each point of the signal are respectively recorded as
x(i)＝{xi| i =1,2, \8230 |, N }, where N is a number of signal points;
(2) Calculating all local extreme points of the signal, and recording the phase and amplitude of the local extreme points as phi (j) = { phij∣j＝1,2,…,M}、 y(j)＝{yj-j =1, 2.·, M }, where M is the number of extreme points;
(3) Carrying out linear normalization on amplitude of extreme point of signal ynorm(j)：
Wherein, yjAnd the j-th extreme point amplitude is represented, min { y (j) } is the minimum value of the extreme point sequence y (j), and max { y (j) } is the maximum value of the extreme point sequence y (j).
(4) Calculating a signal waveform scale s:
(5) Defining a non-linear mapping
Fuzzifying the boundary of impact and non-impact components, changing the amplitude distribution of normalized local extreme value points, and determining the width k (i) = { k) = of the structural element corresponding to each point in the signali| i =1, 2.., N } is determined by:
wherein the symbol
Represents rounding up, mapping
Is defined as:
wherein a is a variable parameter for controlling the bending degree of the mapping curve, is used for adjusting the amplitude distribution of extreme points, and recommends a value range a epsilon [2,8]. And k (i) is taken as the element width, and the signals are subjected to cascade morphological filtering, so that the effective suppression of non-impact components is realized.
Further, step S05, amplifying the mutation component in the noise reduction signal by using Teager energy operator:
specifically, for a discrete signal x (i) with a total point number of n (i =1,2, \8230;, n), a three-point symmetric differential energy operator is constructed and smoothed, and an energy operator Ψ [ x (i) ] is defined as:
further, the step 06: and (4) solving a Hilbert envelope of the mutation component, and performing smooth filtering on an envelope line by using a Savitzky-Golay filter.
In particular, for a continuous-time signal x (t), the Hilbert transform thereof
Is defined as:
structure analysis signal
The Hilbert envelope of the signal x (t) is then:
dividing the envelope filtered by the Savitzky-Golay smoothing filter into n small sections of section number in an angular domain diagram, calculating the average amplitude of each small section signal, and arranging the small sections into a group of series l [ i [ [ i ]]I =0,1, \8230;, n-1, calculating the mean of the sequence
And using 3-sigma rule to remove the small segment containing obvious impact characteristic and the rest m small segment number series
A relatively flat component in the signal is characterized. Let a number series r [ j ]]Has an average value of
The difference between the maximum value and the minimum value is Δ r, and the weighting coefficient a is used to adjust the threshold value, so that the impact detection threshold value can be defined as
Further, the number of segments n =72, i.e. a small segment every 5 °.
The invention has the technical effects that: the embodiment of the application provides a method for obtaining a plurality of intrinsic mode functions by carrying out ensemble empirical mode decomposition on a vibration signal, utilizing a self-adaptive variable-scale morphological filter to configure a proper structural element width for each signal point, and reversely utilizing the excellent performance of a cascade type morphological filter on pulse suppression to retain an impact component in IMF and suppress a non-impact component.
The method and the device for extracting and diagnosing the fault of the vibration signal impact characteristic of the floating platform reciprocating compressor based on the EEMD-adaptive variable-scale morphological filter are characterized in that harmonic components in IMFs to be reconstructed of various orders are suppressed by using a mathematical morphological filtering method, and compared with the method for performing morphological filtering on noise reduction signals after EEMD decomposition and reconstruction, the method and the device have a better harmonic suppression effect, and can be used for accurately extracting the impact characteristic of the vibration signal of the floating platform reciprocating compressor and realizing fault diagnosis.
Drawings
FIG. 1 is a flow chart of a method for diagnosing a vibration signal impact characteristic extraction fault of a floating platform reciprocating compressor;
FIG. 2 is a schematic structural diagram of a vibration signal impact characteristic extraction fault diagnosis device of a floating platform reciprocating compressor;
FIGS. 3 a-3 d are graphs simulating reciprocating compressor surge, harmonic, noise and their mixed vibration signals;
FIG. 4 is a reconstructed noise reduction signal after adaptive variable-scale morphological filtering of a simulated reciprocating compressor;
FIG. 5 is a graph of simulated reciprocating compressor impact threshold calculation and peak detection results;
FIG. 6 shows the body acceleration signal and the Crankshaft End (CE) and cylinder Head End (HE) dynamic pressures in a real reciprocating compressor;
FIG. 7 is a signal of body acceleration in a real reciprocating compressor;
FIG. 8 is a reconstructed noise reduction signal after adaptive variable-scale morphological filtering of a real reciprocating compressor;
FIG. 9 is the real reciprocating compressor Teager energy operator filtering result;
fig. 10 shows the real reciprocating compressor surge threshold calculation and peak detection results.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Based on this, the embodiment of the present application proposes a method for obtaining a plurality of Intrinsic Mode Functions (IMFs) by performing Ensemble Empirical Mode Decomposition (EEMD) on a vibration signal, and using an Adaptive Variable Scale Morphological Filter (AVSMF), to configure a suitable structural element width for each signal point, and reversely using the excellent performance of the cascaded Morphological Filter in pulse suppression, so as to retain the impulse component in the IMF and suppress the non-impulse component, and finally, using an Adaptive impulse peak detection method to automatically determine the phase characteristics of the impulse component in the signal, which can effectively extract the impulse characteristic in the signal, provide a recognition basis for diagnosing the reciprocating compressor failure, and provide support and guarantee for predictive maintenance of the reciprocating compressor on an ocean platform.
The method and the device for extracting and diagnosing the fault of the vibration signal impact characteristic of the floating platform reciprocating compressor based on the EEMD-adaptive variable-scale morphological filter are characterized in that a mathematical morphological filtering method is used for inhibiting harmonic components in IMFs to be reconstructed in each order, and compared with the morphological filtering of noise reduction signals after EEMD decomposition reconstruction, the method and the device have a better harmonic inhibition effect, and realize accurate impact characteristic extraction and fault diagnosis of the vibration signal of the floating platform reciprocating compressor.
Fig. 1 is a flowchart of a method for diagnosing an impact characteristic extraction fault of a floating platform reciprocating compressor vibration signal according to an embodiment of the present application. Referring to fig. 1, the method for extracting and diagnosing the fault of the floating platform reciprocating compressor vibration signal impact characteristic provided in the embodiment of the present application specifically includes:
step 1, EEMD decomposition is carried out on the vibration signal to be processed to obtain a plurality of IMFs.
Specifically, the EEMD decomposition performs multiple EMD decompositions by introducing white gaussian noise to the original signal, and averages the multiple decomposed IMF components to obtain the final IMF.
Each IMF needs to satisfy the following two conditions:
(1) In the whole signal, the difference between the number of extreme points of the signal and the number of zero-crossing points of the signal is less than or equal to 1;
(2) At any time, an upper envelope formed by the local maximum point and a lower envelope formed by the local minimum point are locally symmetrical with respect to the time axis.
The main steps of EMD decomposition are as follows:
(1) Finding the upper and lower extreme points of the vibration signal, and drawing an upper envelope line and a lower envelope line;
(2) Calculating the mean value of the upper envelope line and the lower envelope line to obtain a mean envelope line;
(3) Subtracting the mean envelope curve from the original vibration signal to obtain a middle signal;
(4) And (3) judging whether the intermediate signal meets two conditions of IMF, if so, marking the intermediate signal as an IMF component, and if not, repeating the steps (1) to (4) on the intermediate signal.
And 2, screening out IMF components of certain order containing obvious impact characteristics from the IMF components through kurtosis-correlation indexes.
Specifically, in the first step, the kurtosis index principle:
the kurtosis index is a time domain statistics dimensionless index and is commonly used for detecting impact characteristics in signals. For discrete signal x (i) = { xi| i =1,2, \8230;, n }, where n is the number of signal points, kurtosis K is defined as:
in the formula
Is the mean of x (i) and is defined as:
and respectively calculating kurtosis values of all orders of components, and rejecting the components with the kurtosis values smaller than 5.0 to realize the first step of screening the IMF components.
Step two, correlation indexes are as follows:
when performing EEMD decomposition, the number of IMF components obtained by decomposition is larger than the actual components of the original signal due to the influence of calculation errors, edge effects, and other factors, and these extra IMF components are called "pseudo components". When the signal is reconstructed, new noise is introduced by including these pseudo components, and therefore these pseudo components must be removed.
Calculating the correlation coefficient of each IMF component and the original function, and assuming that there are two continuous time domain signals x (t), y (t), and the correlation number R (xy) is defined as:
and selecting IMF with the correlation coefficient more than 10% to realize the second step of screening.
And 3, respectively carrying out self-adaptive variable-scale morphological filtering on the selected IMF components, filtering out non-impact components and improving the signal-to-noise ratio of the impact signals.
For a discrete signal f (N) with a total number of points N, where the argument N =0,1, \8230, N-1 and a sequence of structural elements g (M) with a total number of points M, where the argument M =0,1, \8230, M-1, N < M, an inflation operator is defined
And the corrosion operator is
Wherein,
denotes the expansion of f (n) with respect to g (m),
denotes the corrosion of f (n) with respect to g (m);
the morphology open operator (°) and morphology closed operator (·) are defined by a sequential combination of dilation and erosion operators:
wherein (f ° g) (n) represents the morphology opening operator of f (n) with respect to g (m) and the morphology closing operator of f (n) with respect to g (m);
further, morphology on-Off (OC) and morphology off-on (CO) operators are defined by sequential combinations of morphology on, morphology off operators:
OC[f(n)]＝(f°g·g)(n)
CO[f(n)]＝(f·g°g)(n)
wherein OC [ f (n) ] represents the morphology open/close operator of f (n), and CO [ f (n) ] represents the morphology open/close operator of f (n).
Wherein the morphology on-off operator suppresses positive pulses in the signal, eliminating sharp "peaks" in the signal, and the morphology off-on operator suppresses negative pulses in the signal, filling in valleys in the signal. In order to remove both positive and negative bi-directional pulses in a signal, a cascaded morphological filter Γ f (n) may be defined that is a combination of a morphological on-off operator and a morphological off-on operator to form f (n).
The traditional morphological filter uses a structural element with fixed width, while the morphological filter proposed by the method uses a structural element with width that is adaptively changed according to the amplitude of the local extreme point of each peak (valley) in the signal. Wherein the structural element uses a flat structure with a height of 0, and a structure element sequence g (N, m, k) (N =0,1, \8230;, N-1) is defined for a discrete signal f (N) (N =0,1, \8230;, N-1 m =0,1, \8230;, k-1), wherein N is the number of signal points, k is the width of the structural element at the nth sampling point, and the width k is determined as follows:
(1) Calculating the phase and amplitude of all local extreme points in the signal, and respectively recording as phi (j) = { phi (j) = phij∣j＝1,2,…,M}、 y(j)＝{yj-j =1,2, \8230 \ M }, where M is the number of local extremum points;
(2) Calculating all local extreme points of the signal, and recording the phase and amplitude as phi (j) = { phi (j) =j∣j＝1,2,…,M}、 y(j)＝{yj∣j＝1,2, \ 8230, M }, wherein M is the number of extreme points;
(3) Carrying out linear normalization on amplitude of local extreme point of signal ynorm(j)：
Wherein, yjAnd representing the amplitude of the jth extreme point, wherein min { y (j) } is the minimum value of the extreme point sequence y (j), and max { y (j) } is the maximum value of the extreme point sequence y (j).
(4) Calculating a signal waveform scale s:
(5) Defining a non-linear mapping
Fuzzifying the boundary of impact and non-impact components, and changing the amplitude distribution of the normalized local extreme value points, so that the width k (i) = { k) = of the structural element corresponding to each point in the signali| i =1,2, \8230 |, N } is determined by:
wherein the symbols
Represents rounding up, mapping
Is defined as:
wherein a is a variable parameter for controlling the bending degree of the mapping curve and is used for adjusting the amplitude distribution of the extreme points, and the recommended value range a belongs to [2,8]. And k (i) is taken as the element width, and the signals are subjected to cascade morphological filtering, so that the effective suppression of non-impact components is realized.
And 4, reconstructing the IMF component after the adaptive variable-scale morphological filtering to obtain a noise reduction signal.
And 5, amplifying amplitude-frequency mutation components in the noise reduction signals by using a Teager energy operator.
Specifically, for a discrete signal x (i) with a total number of points n (i =1,2, \8230;, n), a three-point symmetric differential energy operator is constructed and smoothed, and the energy operator Ψ [ x (i) ] is defined as:
and 6, solving a Hilbert envelope of the mutation component, and performing smooth filtering on the envelope by using a Savitzky-Golay filter.
In particular, for a continuous-time signal x (t), the Hilbert transform thereof
Is defined as:
structure analysis signal
The Hilbert envelope of the signal x (t) is then:
the Savitzky-Golay filter is a data flow smoothing and denoising method widely applied, and is a method for performing best fitting by a least square method through a moving window on the basis of a polynomial in a time domain.
Dividing the envelope filtered by the Savitzky-Golay smoothing filter into n small sections of section number in an angular domain diagram, calculating the average amplitude of each small section signal, and arranging the small sections into a group of series l [ i [ [ i ]]I =0,1, \ 8230;, n-1, the mean value of the sequence is calculated
Using 3-sigma rule to remove the small segment containing obvious impact characteristic and the rest m small segment sequence
A relatively flat component in the signal is characterized. Let a number series r [ j ]]Has an average value of
The difference between the maximum and minimum values is Δ r, and the weighting coefficient a is used to adjust the threshold, the impact detection threshold can be defined as
In the embodiment of the application, the number of segments n =72, namely, a small segment every 5 °.
And 7: and carrying out peak phase detection to extract the phase characteristics of the impact component in the signal.
Specifically, zero-crossing detection with a threshold as a zero point is performed on the smoothed envelope, so that all "peaks" higher than the threshold line can be extracted, namely, impact peaks. And eliminating false impact through the continuous angle of each impact peak in the angular domain graph, wherein the continuous angle is 5 degrees. And finally, determining the angle in the angle domain graph where the peak value of each impact peak is positioned as the phase characteristic of the impact.
And 8: and after the phase characteristics of the impact are obtained, capturing amplitude, energy and other characteristics of the impact to further carry out fault diagnosis on the reciprocating compressor.
Fig. 2 is a schematic structural diagram of a floating platform reciprocating compressor vibration signal impact characteristic extraction fault diagnosis device, and referring to fig. 2, the floating platform reciprocating compressor vibration signal impact characteristic extraction fault diagnosis device provided in the embodiment of the present application specifically includes:
and the acquisition module is used for acquiring vibration data of the floating platform reciprocating compressor and acquiring and transmitting the vibration data to the upper computer for processing.
The acquisition module specifically comprises: the key phase sensor is used for acquiring key phase pulse signals of impact positions of a reciprocating compressor cylinder valve cover, a middle body, a crankshaft and the like, and the time interval of one working cycle of the compressor can be acquired through setting of a trigger value. The multi-point vibration sensor can capture vibration signals of a plurality of key positions of the compressor. The signal conditioning and collecting device comprises: the data acquisition hardware uses an NI 9263 sound and vibration voltage input module and an NI Compact RIO 9047 case matched with the NI 9263 module to perform data bottom acquisition and TCP/IP network data transmission. The method comprises the steps of firstly reading data from an FIFO (first in first out) and then transmitting the data to an upper computer through a TCP (transmission control protocol), wherein the TCP supports the transmission of the data to a plurality of clients, namely supports the transmission to a plurality of upper computers.
The acquisition module can provide required original vibration signal data for the subsequent processing module and provide a multi-source fault diagnosis basis for the subsequent diagnosis module.
And the processing module is connected with the acquisition module, and comprises storage and processing of acquired vibration signal data, and the acquired vibration signals are processed through EEMD-adaptive strain scale morphological filtering.
And the extraction module is connected with the processing module and is used for carrying out peak detection to extract the phase characteristics of the impact components in the signals and capturing the amplitude, the energy and other characteristics of the impact.
The diagnosis and evaluation module is connected with the extraction module, establishes a reciprocating compressor fault characteristic knowledge base and carries out fault diagnosis according to the vibration signal impact characteristics captured by the extraction module:
specifically, the extraction module is used for extracting impact characteristics according to data continuously collected in real time on site, comparing the multi-source characteristic values with a fault characteristic knowledge base of the reciprocating compressor and outputting diagnosis results. The time interval of the diagnosis output result can be set, namely the diagnosis interval of the working cycle of the compressor is set in a user-defined mode.
And the visualization module is connected with the diagnosis evaluation module and the acquisition module and is used for visualizing the diagnosis result and the acquired data. Meanwhile, webpage interactive access is carried out through the remote terminal to realize remote data processing and calling.
Specifically, the module is implanted into an actual three-dimensional model of the compressor, and the acquired measurement result is visualized in real time and the diagnosis fault position is highlighted. Meanwhile, aiming at the characteristics that a reciprocating compressor of an offshore platform and the like need to be remotely monitored and store a large amount of data, the diagnosis module based on webpage access is arranged locally, and webpage interactive access is carried out through a remote terminal, so that the problem of bandwidth introduced by returning of big data is avoided. And a remote data downloading port (downloading on demand) is provided so that an expert at the client side can perform deep analysis and remote monitoring and diagnosis of the compressor system can be realized.
The floating platform reciprocating compressor vibration signal impact characteristic extraction fault diagnosis device can realize acquisition of vibration signals of an acquisition module in each compressor working cycle, vibration signal impact phase characteristic values are acquired through a processing module and an extraction module, the characteristic values are matched with a knowledge base in a diagnosis and evaluation module to give a compressor state diagnosis result, and the result and a monitoring acquisition numerical value are displayed through a visualization module. The whole device has the functions of remotely acquiring data and monitoring the state of the compressor, and the safe and stable operation of the reciprocating compressor is ensured.
The first embodiment is as follows:
establishing a continuous time domain model of a body acceleration signal in the reciprocating compressor as follows:
in the formula, τi、AiFor the time of occurrence of the ith impact and its amplitude, fniFor a certain natural frequency of the body-sensor system in the compressor, ζ is the corresponding system damping ratio, fjIs in a signalFrequency component of frequency multiplication, 1 (t-tau)i) Is a unit step function, and p (t) is a noise signal.
FIG. 3 is a graph showing simulated impact, harmonic, noise and mixed vibration signals of the reciprocating compressor, referring to FIG. 3, setting the rotating speed of the reciprocating compressor to 900RPM, and the sampling rate f of the acceleration sensorsAnd =51200Hz, and 3 impact signals of different types are assumed to exist in the signal and respectively represent high-frequency high-amplitude impact, low-frequency low-amplitude impact and high-frequency low-amplitude impact. Their amplitude Ai160, 90, respectively, system natural frequency fni3000, 1500 and 3000 respectively, damping ratio zetai0.1, respectively, the phase τ at which the impact occursiThe amplitudes are respectively 20 and 10, the frequencies are respectively 90Hz and 180Hz, the phases are set to be the same, refer to a graph 3 (b), and finally a Gaussian white noise reference graph 3 (c) with the signal-to-noise ratio of-3 dB is introduced. The resulting synthesized simulation signal is referred to fig. 3 (d).
Since it takes some time for the shock to reach a peak value from the onset of occurrence, the actual shock peak phase slightly lags behind the phase at which the shock occurs.
The phase of the 3 shock peaks in this example is shown in table 1.
TABLE 1 simulation impact phase
The method comprises the following steps of performing impact characteristic extraction on the simulation mixed vibration signal of the reciprocating compressor:
EEMD decomposition is carried out on the mixed signal to obtain a plurality of orders of IMF;
calculating the kurtosis of each IMF and the correlation coefficient of the IMF and the original signal, and finally screening 2, 3 and 4 IMFs;
performing self-adaptive variable-scale morphological filtering on the screened IMF, and reserving an impact component in the filtered signal, wherein a non-impact component is inhibited;
reconstructing the filtered signal to obtain a noise reduction signal, wherein both the harmonic signal and the noise signal of the original signal are well suppressed, and amplifying the mutation component of the signal by using a Teager energy operator, referring to FIG. 4;
carrying out envelope smoothing to obtain a final signal to be detected;
and (4) performing adaptive calculation on the impact threshold and performing peak detection to obtain an impact phase, see fig. 5, and the detection result is shown in table 2.
TABLE 2 impact test results
According to the impact phase detection result, whether high-amplitude impact or low-amplitude impact or high-frequency impact or low-frequency impact can be accurately detected, and the phase error of the impact peak value is less than 0.5 degree.
The second embodiment:
for example: the compressor is provided with 2 first-stage low pressure cylinders and 2 second-stage high pressure cylinders which are symmetrically arranged at two ends, and an acceleration sensor with the model of PCB-EX603C01 is installed at the middle body of each cylinder and used for monitoring the vibration signal of the middle body. The used acquisition equipment is an NI Compact RIO 9047 reconfigurable embedded measurement and control system, and an NI 9263 sound and vibration input module is matched, so that high-speed acquisition of acceleration signals can be realized. The compressor operating speed in this example is 1200RPM, setting the sampling rate at 10240Hz. The phase position of the acceleration signal of a certain two cycles at the middle body of the #1 first-stage low-pressure cylinder and the cylinder head side (HE) and the crankshaft side (CE) dynamic gas pressure converted into an angular domain diagram is 0-720 degrees, which is shown in a reference figure 6, wherein the acceleration signal is independently taken out and is shown in a reference figure 7. The noise reduction signal obtained after the EEMD-adaptive variable-scale morphological noise reduction processing is shown in a reference figure 8, and it can be obviously seen that the non-impact component in the signal is obviously inhibited. After Teager energy operator filtering (refer to fig. 9) and envelope smoothing filtering, adaptive identification of an impact threshold is performed on the shock threshold, and finally, an impact phase is determined through peak phase detection, as shown in fig. 10, wherein a horizontal line is an impact detection threshold line, and a vertical line is a detection result of an impact peak. The impact peak phase detection results are shown in table 3.
TABLE 3 impact test results
Combining the impact peak phase detection result with the dynamic pressure in the HE and CE cylinders, it can be obviously seen that the impact 1 and impact 2 phase stabilities respectively represent the CE end exhaust valve seating and the HE end suction valve seating; the impact phases 3 and 5 are relatively stable, and represent the seating of an exhaust valve at the HE end and a suction valve at the CE end; and the impact 4 has larger phase fluctuation, and combined with the phenomenon that the dynamic pressure in the cylinder at the HE end is temporarily abnormally raised near 280 degrees in a single period, the valve plate and the valve seat are judged to generate continuous impact for many times due to the vibration generated when the exhaust valve at the HE end is seated, so that additional impact is introduced. In addition, it can be observed from fig. 11, 12 and 13 that there are slight impact components around 220 ° and 380 °, and in combination with significant large air pressure fluctuations in the gas dynamic pressures in the cylinders at the HE and CE ends near these two phases, it is comprehensively judged that the valve plate chattering is generated due to the mismatch between the exhaust valve and the exhaust pressure at the HE and CE ends, which results in the slight collision between the valve plate and the valve seat during the exhaust process and introduces additional impact.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or the change made by the person skilled in the art on the basis of the present invention are within the protection scope of the present invention. The protection scope of the invention is subject to the claims.
Claims (8)
1. A fault diagnostic device for a floating platform reciprocating compressor, comprising:
the acquisition module is used for acquiring vibration signal data of the floating platform reciprocating compressor and acquiring and transmitting the vibration signal data to an upper computer for processing;
the processing module is connected with the acquisition module and is used for processing the acquired vibration signals through EEMD-adaptive variable-scale morphological filtering;
the extraction module is connected with the processing module and used for carrying out peak detection to extract the phase characteristics of the impact components in the signals and capturing the parameter characteristics of the impact;
the diagnosis and evaluation module is connected with the extraction module, establishes a fault characteristic knowledge base of the reciprocating compressor, compares the fault characteristic knowledge base according to the vibration signal impact characteristics captured by the extraction module, and outputs a diagnosis result;
and the visualization module is connected with the diagnosis evaluation module and the acquisition module, visualizes the diagnosis result and the acquired data, and simultaneously carries out webpage interactive access through the remote terminal to realize remote data processing and calling.
2. A fault diagnosis method for a floating platform reciprocating compressor is characterized by comprising the following steps:
s01: decomposing a vibration signal to be processed to obtain a plurality of IMF components;
s02: screening out certain orders of IMFs containing obvious impact characteristics from a plurality of IMF components through indexes;
s03: respectively carrying out self-adaptive variable-scale morphological filtering on the screened IMF components to filter out non-impact components;
s04: reconstructing the IMF component after the adaptive variable-scale morphological filtering to obtain a noise reduction signal;
s05: amplifying amplitude-frequency mutation components in the noise reduction signal;
s06: calculating the envelope of the abrupt change component, and performing smooth filtering on the envelope by using a smoothing filter;
s07: carrying out peak phase detection to extract the phase characteristics of the impact components in the signals;
s08: after the impact phase characteristics are obtained, capturing parameter characteristics to further carry out fault diagnosis on the reciprocating compressor;
s09: and visualizing the diagnosis result and the acquired data, and remotely processing and calling the data.
3. The diagnostic method of claim 2, wherein: the step S02 uses a kurtosis-correlation index, including:
first, kurtosis index:
for discrete signal x (i) = { xi| i =1,2, \8230;, n }, where n is the number of signal points, kurtosis K is defined as:
in the formula
Is the mean of x (i) and is defined as:
respectively calculating kurtosis values of IMF components of all orders, and rejecting components with kurtosis values smaller than 5.0 to realize the first-step screening of the IMF components;
step two, correlation indexes are as follows:
calculating the correlation coefficient of each IMF component and the original function, and assuming that there are two continuous time domain signals x (t), y (t), the correlation coefficient R (xy) is defined as:
and selecting IMF with the correlation coefficient larger than 10% to realize the second step of screening.
4. The diagnostic method of claim 2, wherein: in step S03, the adaptive variable-scale morphological filtering includes:
for a discrete signal f (N) with a total number of points N, where the argument N =0,1, \ 8230;, N-1 and the total number of pointsThe sequence of structural elements g (M) for M, where the argument M =0,1, \8230, M-1, N < M, defines the inflation operator
And erosion operator
Comprises the following steps:
wherein,
denotes the expansion of f (n) with respect to g (m),
denotes the corrosion of f (n) with respect to g (m);
defining a morphology opening operator by a sequential combination of an expansion operator and an erosion operator
And a morphological closing operator (·):
wherein,
represents a morphology opening operator of f (n) with respect to g (m), and (f.g) (n) represents a morphology closing operator of f (n) with respect to g (m);
further, a morphology open-close (OC) operator and a morphology close-open (CO) operator are defined by sequential combinations of the morphology open operator and the morphology close operator:
wherein OC [ f (n) ] represents the morphological on-off operator of f (n), and CO [ f (n) ] represents the morphological on-off operator of f (n);
a cascade morphological filter Γ [ f (n) ] formed by combining a morphological open-close operator and a morphological closed-open operator to f (n):
5. the diagnostic method of claim 4, wherein: the filter structure element uses a flat structure with a height of 0, and a structure element sequence g (N, m, k) (N =0,1, \8230;, N-1) is defined for a discrete signal f (N) (N =0,1, \8230;, N-1 m =0,1, \8230;, k-1), wherein N is the number of signal points, k is the width of the structure element at the nth sampling point, and the width k is determined as follows:
(1) The phase and amplitude of each point of the signal are recorded as
x(i)＝{xi| i =1,2, \8230 |, N }, where N is a number of signal points;
(2) Calculating the phase and amplitude of all local extreme points in the signal, and respectively recording as phi (j) = { phi (j) =j∣j＝1,2,…,M}、y(j)＝{yj-j =1,2, \8230 \ M }, where M is the number of local extremum points;
(3) Carrying out linear normalization on amplitude values of local extreme points of signals ynorm(j)：
Wherein, yjThe amplitude value of the jth extreme point is represented, min { y (j) } is the minimum value of the extreme point sequence y (j), and max { y (j) } is the maximum value of the extreme point sequence y (j);
(4) Calculating a signal waveform scale s:
(5) Defining a non-linear mapping
Fuzzifying the boundary of impact and non-impact components, changing the amplitude distribution of normalized local extreme points, and determining the width k (i) = { k) = of the structural element corresponding to each point in the signali| i =1,2, \8230 |, N } is determined by:
wherein the symbols
Represents rounding up, mapping
Is defined as:
in the formula, a is a variable parameter and is used for adjusting the amplitude distribution of extreme points, a recommended value range a belongs to [2,8], k (i) is used as the element width, and the cascade type morphological filtering is carried out on signals to realize the effective inhibition of non-impact components.
6. The diagnostic method of claim 2, wherein: step S05, amplifying amplitude-frequency mutation components in the noise reduction signals by using a Teager energy operator:
specifically, for a discrete signal x (i) with a total number of points n (i =1,2, \ 8230;, n), a three-point symmetric difference energy operator is constructed and smoothed, and an energy operator Ψ [ x (i) ] is defined as:
7. the diagnostic method of claim 2, wherein: the step 06: obtaining a Hilbert envelope of a mutation component, and performing smooth filtering on the envelope by using a Savitzky-Golay smoothing filter;
in particular, for a continuous-time signal x (t), the Hilbert transform thereof
Is defined as:
structure analytic signal
The Hilbert envelope of the signal x (t) is then:
dividing the envelope filtered by the Savitzky-Golay smoothing filter into n small sections of section number in an angular domain diagram, calculating the average amplitude of each small section signal, and arranging the small sections into a group of series l [ i [ [ i ]]I =0,1, \ 8230;, n-1, the mean value of the sequence is calculated
Using 3-sigma rule to remove the small segment containing obvious impact characteristic and the rest m small segment sequence
Characterizing the relatively stationary components of the signal, a series of r [ j ] is set]Has an average value of
The difference between the maximum and minimum values is Δ r, and the weighting factor a is used to adjust the threshold, then the impact detection threshold can be defined as
8. The diagnostic method of claim 7, wherein: the number of segments n =72, i.e. one segment every 5 °.
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| CN117450054B (en) * | 2023-11-22 | 2024-06-07 | 深圳沈鼓测控技术有限公司 | Reciprocating compressor state monitoring and protecting method based on dynamic angle domain |
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