CN113588297A - Hydraulic steering engine fault detection method and device based on self-adaptive noise elimination - Google Patents
Hydraulic steering engine fault detection method and device based on self-adaptive noise elimination Download PDFInfo
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Abstract
The invention discloses a hydraulic steering engine fault detection method and device based on self-adaptive noise elimination, which comprises the following steps: the method comprises the steps of collecting a plurality of groups of vibration signals of a hydraulic steering engine comprising a hydraulic steering engine oil inlet and a hydraulic steering engine body, and preprocessing the vibration signals to obtain a vibration signal sequence { x }i(j) J is the j-th group vibration signal sequence; extracting a fault characteristic signal from the vibration signal sequence through a self-adaptive noise removal algorithm, and acquiring a frequency spectrogram according to the fault characteristic signal; and detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the frequency spectrum diagram, and if so, judging that the hydraulic steering engine has a fault. The fault detection method and the fault detection device can be used for diagnosing the rotor fault of the vehicle hydraulic steering engine and have important practicability and engineering value.
Description
Technical Field
The invention belongs to the technical field of state monitoring and fault diagnosis, and relates to a hydraulic steering engine fault detection method and device based on self-adaptive noise elimination.
Background
The front axle steering gear of the chassis of the vehicle is one of the most important components in the vehicle composition and is important to the running safety of the automobile, so parts of the automobile steering gear are all called safety parts. If a vehicle fails during traveling and the traveling direction of the vehicle cannot be controlled as intended by the driver, a vehicle safety accident may be caused, resulting in loss of life and property. Therefore, the method has important significance for detecting the state and diagnosing early faults of the front axle steering engine of the chassis of the vehicle.
The main failure source of a set of vehicle chassis front axle steering engine is a hydraulic steering engine and an oil pump, wherein the failure component of the hydraulic steering engine comprises: relief valves, check valves, diverter valves, machinery, and seals. The oil pump is actually operated by a vane pump and a gear pump. Because the hydraulic steering gear of the front axle steering gear of the vehicle chassis has a complex composition structure, prior knowledge corresponding to fault characteristics is relatively deficient. In the actual vibration test environment for the steering engine, the measurement signal is often interfered by noise such as oil pressure impact, and the extraction of the fault characteristic signal is more difficult. For a long time, under the condition of poor fault characteristic priori knowledge and noise interference such as external high oil pressure impact, the fault detection of the hydraulic steering engine of the vehicle is difficult to extract fault characteristic signals and detect faults directly according to the information of vibration measurement signals.
Disclosure of Invention
In order to achieve the purpose, the technical scheme of the invention is as follows:
a hydraulic steering engine fault detection method based on self-adaptive noise elimination comprises the following steps:
the method comprises the steps of collecting a plurality of groups of vibration signals of a hydraulic steering engine comprising a hydraulic steering engine oil inlet and a hydraulic steering engine body, and preprocessing the vibration signals to obtain a vibration signal sequence { x }i(j) J is the jth vibration signal sequence, i is the ith signal in the vibration signal sequences;
extracting a fault characteristic signal from the vibration signal sequence through a self-adaptive noise removal algorithm, and acquiring a frequency spectrogram according to the fault characteristic signal;
and detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the frequency spectrum diagram, and if so, judging that the hydraulic steering engine has a fault.
Optionally, the acquiring multiple sets of vibration signals of the hydraulic steering gear including the hydraulic steering gear oil inlet and the hydraulic steering gear body includes:
synchronously acquiring vibration acceleration signals of an oil inlet of the hydraulic steering engine and vibration acceleration signals of the hydraulic steering engine body in three coordinate axis directions of a space rectangular coordinate system by using an acceleration sensor to obtain vibration acceleration data;
and preprocessing the vibration acceleration data to obtain a vibration signal sequence.
Optionally, the extracting a fault feature signal from the vibration signal sequence by using an adaptive noise removal algorithm, and acquiring a spectrogram according to the fault feature signal includes:
(1) setting parameters of an adaptive filter;
(2) for vibration signal sequence { xi(j) Applying a time delay delta to obtain a delayed vibration signal sequence xΔi(j)}；
(3) Will be described inDelayed vibration signal sequence xΔi(j) Inputting the signal sequence to a self-adaptive filter for filtering to obtain an output signal sequence xouti(j)}；
(4) The vibration signal sequence { xi(j) With the output signal sequence { x }outi(j) Get the difference sequence { x }subi(j)}；
(5) Differencing the sequence { xsubi(j) Inputting the signal into an adaptive filter as a reference signal, and updating the weight Wj of the adaptive filter based on a least square algorithm;
(6) repeating the steps 1 to 5(n- (delta + L)) times, and extracting the difference sequence { x ] of the last timesubi(j) Where n is the vibration signal sequence { x }i(j) Total length of the element;
(7) with the last differencing sequence xsubi(j) And drawing a time domain graph, and carrying out Fourier transform on the time domain graph and a region corresponding to the delayed data of the difference sequence to obtain a spectrogram.
Optionally, the updating of the adaptive filter weights W based on a least squares algorithmjThe method comprises the following steps:
wherein μ is the learning rate;
εkfor output error at kth iteration, i.e. taking value equal to xsubi(k)；
E [. cndot. ] is the sequence mean operator.
Optionally, detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the spectrogram, and if so, determining that the hydraulic steering engine has a fault, including:
extracting a corresponding frequency band from the spectrogram according to the frequency conversion of the hydraulic steering engine or the multiple frequency rate range of the frequency conversion;
if the frequency conversion and the frequency multiplication exist in the extracted frequency band, the hydraulic steering engine is judged to be in fault.
Optionally, before extracting the corresponding frequency band from the spectrogram, a margin is added to a frequency conversion range according to the hydraulic steering engine or a multiple frequency ratio range of the frequency conversion.
Optionally, the corresponding frequency band is within a range of 0-0.6 Hz.
The invention also provides a hydraulic steering engine fault detection device based on self-adaptive noise removal, which comprises the following components:
the vibration signal sequence acquisition module is used for acquiring a plurality of groups of vibration signals of the hydraulic steering engine including a hydraulic steering engine oil inlet and a hydraulic steering engine body and preprocessing the vibration signals to obtain a vibration signal sequence { xi(j) J is the jth vibration signal sequence, i is the ith signal in the vibration signal sequences;
the frequency spectrum image acquisition module is used for extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm and acquiring a frequency spectrum image according to the fault characteristic signals;
and the fault judgment module is used for detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the spectrogram, and judging that the hydraulic steering engine has faults if the frequency conversion and the frequency multiplication exist.
The invention has the following advantages and outstanding technical effects: the invention analyzes the characteristics of the hydraulic steering machine fault characteristic signal and the hydraulic impact signal from the instantaneous impact and periodicity angle of the signal based on the phenomenon that the vibration measurement signal is strongly interfered by the hydraulic impact noise in the actual vibration test environment of the hydraulic steering machine of the front axle of the actual vehicle, separates the two signals by combining the self-adaptive noise clearing signal processing method, further analyzes the extracted fault special diagnosis signal based on the fault rotor dynamics theory, and diagnoses the faults of unbalance, misalignment, collision, and the like possibly existing in each rotor in the steering machine. The whole set of the provided technical route can diagnose the rotor fault of the vehicle hydraulic steering machine, effectively fills the theoretical and technical blank of the hydraulic steering machine fault characteristic signal extraction and fault detection method, and has important practicability and engineering value.
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The present invention will become more apparent by describing in detail embodiments thereof with reference to the attached drawings, in which various embodiments are illustrated for illustrative purposes, and should by no means be construed as limiting the scope of the embodiments. Additionally, various features of different disclosed embodiments may be combined to form additional embodiments, which are part of this disclosure, wherein:
FIG. 1 is a flow chart of a method for diagnosing a hydraulic steering engine fault with adaptive noise cleaning per se provided by the present invention;
FIG. 2 is a time domain diagram of a mixed signal composed of preprocessed oil pressure impact noise and hydraulic steering gear fault characteristic signal white noise;
FIG. 3 is a time domain diagram of a fault signature signal obtained after an original mixed signal is subjected to self-adaptive noise cleaning;
FIG. 4 is a frequency domain diagram of a fault signature obtained after an original mixed signal is subjected to self-adaptive noise removal processing;
Detailed Description
The embodiments of the present invention will be described below with reference to the accompanying drawings. Those of ordinary skill in the art will recognize that the described embodiments can be modified in various different ways, or combinations thereof, without departing from the spirit and scope of the present invention. Accordingly, the drawings and description are illustrative in nature and not intended to limit the scope of the claims. Furthermore, in the present description, the drawings are not to scale and like reference numerals refer to like parts.
Before any embodiments of the invention are explained in detail, it is to be understood that the concepts disclosed herein are not limited in their application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The concepts illustrated in these embodiments can be practiced or carried out in various ways. Specific phrases and terms used herein are for convenience of description and should not be construed as limiting.
In the invention, the periodic characteristic signal separated by the self-adaptive noise clearing method and the hydraulic steering engine fault characteristic signal of the front axle steering engine of the vehicle chassis have similar characteristics, the impact noise signal removed and the oil pressure impact signal have similar characteristics, and the judgment is based on the following facts or phenomena:
(1) in the vibration measurement process of the hydraulic steering engine of the front axle steering engine of the vehicle chassis, according to the comparison of the vibration signal of the vibration measuring point of the oil inlet and the vibration signal of the vibration signal measuring point of the hydraulic steering engine body, the vibration signal at the oil inlet and the impact characteristics of the hydraulic steering engine body in all directions can be obviously found. This shows that the oil pressure impact noise is dominant in the vibration signal of the hydraulic steering gear body, and the signal-to-noise ratio of the actually measured vibration signal of the hydraulic steering gear body is reduced.
(2) In the structural composition of a hydraulic steering gear of a vehicle steering gear, a large number of rotating mechanisms such as ball screws, ball bearings and the like exist. In an actual operation state, the steering engine may have various fault modes such as rotor misalignment fault, unbalance fault, crack fault, rubbing fault and the like. According to the rotor dynamics theory, in the early stage of the fault, the fault may cause the frequency spectrum of the fault characteristic signal to be in frequency conversion and frequency multiplication with the hydraulic steering engine. The frequency conversion refers to the frequency corresponding to the rotating speed of the hydraulic steering engine, and the frequency multiplication is integral multiple of the frequency conversion. Since the energy contained in the failure signature signal is low at the initial stage of the failure, the impulse is low and the periodicity is high. Due to the interference of oil pressure impact at the oil inlet and other noises, the fault characteristic frequency of the hydraulic steering engine can be submerged in the noises, and the extraction of the fault characteristic signal is difficult.
The technical solutions in the embodiments will be described specifically, clearly and completely with reference to the accompanying drawings in the embodiments.
As shown in FIG. 1, the invention discloses a hydraulic steering engine fault diagnosis method of self-adaptive noise elimination algorithm, which comprises the following steps:
the method comprises the following steps: collecting a vibration signal sequence of a hydraulic steering gear of a front axle steering gear of a vehicle chassis and preprocessing the signal sequence;
the method comprises the following steps that (1) vibration acceleration signals of an oil inlet of a hydraulic steering engine of a front axle steering engine of a vehicle chassis and vibration acceleration signals of an engine body of the hydraulic steering engine in three directions are synchronously acquired by using a corresponding acceleration sensor according to set timing sampling parameters;
(2) obtaining vibration acceleration data of an oil inlet and three groups of hydraulic steering engine bodies through sorting;
(3) and preprocessing the four groups of collected and sorted vibration acceleration data to obtain four groups of vibration signal sequences.
The vibration acceleration signal of the hydraulic steering engine of the front axle steering engine of the vehicle chassis can be measured by a vibration acceleration sensor. A discrete vibration acceleration signal is obtained by sampling the vibration acceleration and this signal is further pre-processed. The main pretreatment step is the de-trending treatment. The method can remove the components with strong tendency in the signal through signal preprocessing to obtain the main fluctuation components of the signal, and the processed vibration signal sequence is recorded as { x }i(j)}。
Step two: extracting fault characteristic signals of the vibration signal sequence through a self-adaptive noise removal algorithm (SANC);
the method comprises the following specific steps:
(1) according to fault prior knowledge and test conditions of a hydraulic steering gear of a front axle steering gear of a vehicle chassis, selecting the order L of an adaptive filter and the initial weight W of the adaptive filter0And the time delay length Δ of the vibration signal sequence; the hyper-parameters to be selected in advance are mainly the order L of the adaptive filter, the delay time length Δ and the learning rate μ. The value of L is generally larger, so that obvious correlation difference between the oil pressure impact noise and the fault characteristics of the hydraulic steering engine can be presented; on the other hand, considering that the length of the mixed sequence is limited, L cannot be too large, so that it can be ensured that the iterative algorithm has enough iteration times to adjust the mixed signal to satisfy the convergence criterion. For the order L, the minimum order needs to correspond to one period of the frequency interval, and in order to obtain better filtering effect, the order should be more than twice of the minimum order. In general, any test signal can be treated as a random signal component and a true signalThe combination of signal components is fixed and the time delay length delta needs to be longer than the correlation length of the random component in the signal. The selection of the learning rate mu cannot take too large value, otherwise the algorithm may not meet the convergence criterion; meanwhile, if the value of μ is too small, the computational complexity may be too high. The order L, the delay time Δ, and the learning rate μ are set to conventional techniques, and will not be described in detail here.
(2) As shown in FIG. 1, for the vibration signal sequence { x ] acquired and preprocessed in step onei(j) Applying a time delay of magnitude Δ, resulting in a sequence of delayed vibration signals { x }Δi(j) In which z is-ΔRepresenting the application of a time delay of magnitude delta.
(3) Delayed vibration signal sequence x subjected to time delay processingΔi(j) The input signal is filtered in an adaptive filter H (z) to obtain an output signal sequence { x }outi(j)}。
(4) Will initially vibrate the signal sequence xi(j) And the output signal sequence xouti(j) Making difference to obtain difference sequence { x }subi(j)}。
(5) The obtained difference sequence { xsubi(j) Inputting the signal into the adaptive filter as a reference signal, and updating the weight W of the adaptive filter based on a least square algorithmj。
The updating process is shown as the following formula:
wherein mu is a learning rate, the value of mu cannot be too large to avoid divergence, and cannot be too small to avoid causing too long training time; epsilonkFor output error at kth iteration, i.e. taking value equal to xsubi(k)；E[·]Is the sequence mean operator.
(6) Repeating the steps (n- (delta + L)) from 1 to 5 times, and extracting the difference sequence { x ] of the last timesubi(j) }; wherein n is the total length of the original signal.
(7) For the last extracted sequence { x }subi(j) And making a time domain graph, and performing Fourier transform on the delayed part of the difference making sequence to obtain a Fourier transform sequence X (-) and a corresponding spectrogram.
Step three: and performing low-pass filtering on the frequency domain based on the obtained spectrogram, and observing whether the low-frequency band of the frequency spectrum has frequency conversion or the frequency conversion and the frequency multiplication of the frequency spectrum, wherein the frequency conversion or the frequency conversion and the frequency multiplication are used as criteria for judging whether the hydraulic steering engine has faults or not. If the rotating frequency or the rotating frequency and the frequency multiplication thereof exist, the hydraulic steering engine of the front axle steering engine of the vehicle chassis may have initial faults such as unbalanced rotor components, misalignment, friction and the like.
The first embodiment is as follows: fault feature extraction simulation analysis of oil impact noise and fault feature mixed signal
The mixed signal is composed of an oil pressure impact signal, a fault characteristic signal, a white noise signal and a trend noise signal, and each component of the mixed signal can be expressed as: 1) oil impact noise: y is150(mod (p, T) ═ 0), T ═ 1/0.30, p ═ 0: N-1, N ═ 32768; 2) fault signature signal: y is2＝sin(w2t)，w2＝2πf2，t＝n(1/Fs)，F s2000 Hz; 3) white noise signal: y is3＝50y30，y30N (0, 1); 4) trending noise: y is4T. In the above formulas, p represents the number of sampling points corresponding to the current signal acquisition time, N represents the total number of sampling points, T represents the current signal acquisition time, T represents the period of oil pressure shock, f represents the period of oil pressure shock2Representing the fault signature frequency, N (0,1) is a standard normal distribution.
The operation condition of the hydraulic steering engine is as follows: speed of 8rpm (frequency of rotation f)2＝0.133Hz)。
Data acquisition parameters: the sampling frequency Fs of the vibration accelerometer of the hydraulic steering engine is 2000Hz, and the data acquisition length N of the hydraulic steering engine is 32768.
Based on the above technical implementation and simulation parameters, the three hyper-parameters involved in self-adaptive noise cleaning, namely the order L, the delay time Δ and the learning rate μ of the adaptive filter, can be respectively determined as: l is 20000; Δ 14999; μ ═ 0.1.
Can be firstly based onThe operating condition of the hydraulic steering engine determines that the fault characteristic frequency of the hydraulic steering engine is close to the rotating frequency of the hydraulic steering engine. Considering its rotation speed of 8rpm, its corresponding rotation frequency is f2The frequency conversion can be properly enlarged by a certain integral multiple to obtain the corresponding frequency doubling (f) at 0.133Hz2' -0.266 Hz) and frequency tripling (f)2"═ 0.40Hz), and a small amount of margin is added appropriately, so that the fault characteristic frequency can be preliminarily determined to be in the range of 0-0.6 Hz. The mixed signal is preprocessed to remove the trend noise y when analyzing the mixed signal4The time domain signal of the preprocessed original signal is obtained by interference as shown in fig. 2, wherein the vertical axis is amplitude and the horizontal axis is time. It can be seen from fig. 2 that the fault signature in the hybrid signal has been completely drowned out in oil surges and other noise. Then, the self-adaptive noise removal processing is performed on the mixed signal, and the separated signal is shown in fig. 3. It can be seen that there is a significant periodicity in the portion of the signal after the time delay; the further delayed portion is extracted and subjected to fast fourier transform and spectrography as shown in fig. 4, where the vertical axis is amplitude and the horizontal axis is frequency. Based on the spectrogram, it can be found that the energy of the signal is more obviously concentrated in the frequency band range (0-0.6 Hz) corresponding to the fault characteristic frequency and is converted in the frequency (f)20.133Hz) was captured. As shown in table 1, comparing the signal-to-noise ratios before and after the adaptive noise removal, it can be found that the signal-to-noise ratio of the mixed signal after the adaptive noise removal process is improved, that is, the fault characteristic signal of the hydraulic steering engine is effectively extracted. Based on the extracted fault characteristic signal, the frequency conversion of the fault characteristic signal can be captured through frequency spectrum analysis, and the fault of the hydraulic steering engine can be stably detected.
TABLE 1
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A hydraulic steering engine fault detection method based on self-adaptive noise elimination is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the steps of collecting a plurality of groups of vibration signals of the hydraulic steering engine, including a hydraulic steering engine oil inlet and a hydraulic steering engine body, and preprocessing the vibration signals to obtain a vibration signal sequence { x }i(j) J is the jth vibration signal sequence, i is the ith signal in the vibration signal sequences;
extracting a fault characteristic signal from the vibration signal sequence through a self-adaptive noise removal algorithm, and acquiring a frequency spectrogram according to the fault characteristic signal;
and detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the frequency spectrum diagram, and if so, judging that the hydraulic steering engine has a fault.
2. The adaptive noise-cleaning-based hydraulic steering engine fault detection method according to claim 1, characterized in that: gather hydraulic steering machine's multiunit vibration signal including hydraulic steering machine oil liquid entry and hydraulic steering machine organism, include:
synchronously acquiring vibration acceleration signals of an oil inlet of the hydraulic steering engine and vibration acceleration signals of the hydraulic steering engine body in three coordinate axis directions of a space rectangular coordinate system by using an acceleration sensor to obtain vibration acceleration data;
and preprocessing the vibration acceleration data to obtain a vibration signal sequence.
3. The hydraulic steering engine fault detection method based on the adaptive noise elimination as claimed in claim 1, characterized in that:
the method for extracting the fault characteristic signal from the vibration signal sequence through the self-adaptive noise removal algorithm and acquiring the spectrogram according to the fault characteristic signal comprises the following steps:
(1) setting parameters of an adaptive filter;
(2) for vibration signal sequence { xi(j) Applying a time delay delta to obtain a delayed vibration signal sequence xΔi(j)}；
(3) The delayed vibration signal sequence xΔi(j) Inputting the signal sequence to a self-adaptive filter for filtering to obtain an output signal sequence xouti(j)}；
(4) The vibration signal sequence { xi(j) With the output signal sequence { x }outi(j) Get the difference sequence { x }subi(j)}；
(5) Differencing the sequence { xsubi(j) Inputting the signal into an adaptive filter as a reference signal, and updating the weight Wj of the adaptive filter based on a least square algorithm;
(6) repeating (n- (delta + L)) times steps 1 to 5, and extracting the difference sequence { x ] of the last timesubi(j) Where n is the vibration signal sequence { x }i(j) Total length of the element;
(7) with the last differencing sequence xsubi(j) And drawing a time domain graph, and carrying out Fourier transform on the time domain graph and a region corresponding to the delayed data of the difference sequence to obtain a spectrogram.
4. The hydraulic steering engine fault detection method based on adaptive noise cleaning according to claim 3, characterized in that: updating the adaptive filter weight W based on the least square algorithmjThe method comprises the following steps:
wherein μ is the learning rate;
εkfor output error at kth iteration, i.e. taking value equal to xsubi(k)；
E [. cndot. ] is the sequence mean operator.
5. The hydraulic steering engine fault detection method based on the adaptive noise elimination as claimed in claim 1, characterized in that: detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the spectrogram, and if so, judging that the hydraulic steering engine has a fault, wherein the method comprises the following steps:
extracting a corresponding frequency band from the spectrogram according to the frequency conversion of the hydraulic steering engine or the multiple frequency rate range of the frequency conversion;
if the frequency conversion and the frequency multiplication exist in the extracted frequency band, the hydraulic steering engine is judged to be in fault.
6. The hydraulic steering engine fault detection method based on the adaptive noise elimination as claimed in claim 5, characterized in that:
before extracting the corresponding frequency band from the spectrogram, increasing a margin for the frequency conversion of the hydraulic steering engine or the frequency conversion multiple frequency ratio range.
7. The hydraulic steering engine fault detection method based on the adaptive noise elimination as claimed in claim 5, characterized in that: the corresponding frequency band is within the range of 0-0.6 Hz.
8. A hydraulic steering gear fault detection device based on self-adaptive noise elimination is characterized by comprising:
the vibration signal sequence acquisition module is used for acquiring a plurality of groups of vibration signals of the hydraulic steering engine including a hydraulic steering engine oil inlet and a hydraulic steering engine body and preprocessing the vibration signals to obtain a vibration signal sequence { xi(j) J is the jth vibration signal sequence, i is the ith signal in the vibration signal sequences;
the frequency spectrum image acquisition module is used for extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm and acquiring a frequency spectrum image according to the fault characteristic signals;
and the fault judgment module is used for detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering engine exist in the spectrogram, and judging that the hydraulic steering engine has faults if the frequency conversion and the frequency multiplication exist.
9. The adaptive noise-cleaning-based hydraulic steering engine fault detection device of claim 8, wherein: gather hydraulic steering machine's multiunit vibration signal including hydraulic steering machine oil liquid entry and hydraulic steering machine organism, include:
synchronously acquiring vibration acceleration signals of an oil inlet of the hydraulic steering engine and vibration acceleration signals of the hydraulic steering engine body in three coordinate axis directions of a space rectangular coordinate system by using an acceleration sensor to obtain vibration acceleration data;
and preprocessing the vibration acceleration data to obtain a vibration signal sequence.
10. The hydraulic steering engine fault detection device based on adaptive noise elimination according to claim 8, characterized in that:
the method for extracting the fault characteristic signal from the vibration signal sequence through the self-adaptive noise removal algorithm and acquiring the spectrogram according to the fault characteristic signal comprises the following steps:
(1) setting parameters of an adaptive filter;
(2) for vibration signal sequence { xi(j) Applying a time delay delta to obtain a delayed vibration signal sequence xΔi(j)}；
(3) The delayed vibration signal sequence xΔi(j) Inputting the signal sequence to a self-adaptive filter for filtering to obtain an output signal sequence xouti(j)}；
(4) The vibration signal sequence { xi(j) With the output signal sequence { x }outi(j) Get the difference sequence { x }subi(j)}；
(5) Differencing the sequence { xsubi(j) Inputting the signal into an adaptive filter as a reference signal, and updating the weight Wj of the adaptive filter based on a least square algorithm;
(6) repeating (n- (delta + L)) times steps 1 to 5, and extracting the difference sequence { x ] of the last timesubi(j) Where n is the vibration signal sequence { x }i(j) Total length of the element;
(7) with the last differencing sequence xsubi(j) And drawing a time domain graph, and carrying out Fourier transform on the time domain graph and a region corresponding to the delayed data of the difference sequence to obtain a spectrogram.
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