CN113588297B - Hydraulic steering engine fault detection method and device based on self-adaptive noise removal - Google Patents
Hydraulic steering engine fault detection method and device based on self-adaptive noise removal Download PDFInfo
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Abstract
The invention discloses a hydraulic steering gear fault detection method and device based on self-adaptive noise removal, comprising the following steps: collecting a plurality of groups of vibration signals of a hydraulic steering engine, wherein the hydraulic steering engine comprises an engine oil inlet of the hydraulic steering engine and a hydraulic steering engine body, and preprocessing the vibration signals to obtain a vibration signal sequence { x i (j) }, wherein j is a j-th group of vibration signal sequence; extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm, and acquiring a spectrogram according to the fault characteristic signals; and detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering machine exist in the spectrogram, and judging the fault of the hydraulic steering machine if the frequency conversion and the frequency multiplication exist. The fault detection method and the fault detection device provided by the invention can diagnose the rotor fault of the hydraulic steering engine of the vehicle, 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 method and a device for detecting faults of a hydraulic steering gear based on self-adaptive noise removal.
Background
The front axle steering gear of the chassis of the vehicle is one of the most important components among the vehicle components, and is critical to the running safety of the vehicle, so that the parts of the steering gear of the vehicle are all called security elements. If the vehicle fails during running and the running direction of the vehicle cannot be controlled according to the wishes of a driver, vehicle safety accidents can be caused, and life and property losses can be caused. Therefore, the method has important significance in state detection and early fault diagnosis of the front axle steering engine of the vehicle chassis.
The main fault sources of the front axle steering machine of the chassis of the set of vehicles are a hydraulic steering machine and an oil pump, wherein the fault components of the hydraulic steering machine comprise: overflow valve, check valve, diverter valve, machinery and seal. The oil pump is actually operated by two types, namely a vane pump and a gear pump. Because the hydraulic steering gear of the front axle steering gear of the chassis of the vehicle has a complex composition structure, priori knowledge corresponding to fault characteristics is relatively deficient. In the vibration test environment for the steering engine, the measurement signals are often interfered by noise such as oil pressure impact, and the like, so that the extraction of fault characteristic signals is more difficult. For a long time, under the condition that fault feature priori knowledge is lack and noise interference such as external high oil pressure impact is caused, fault feature signal extraction and fault detection are difficult to directly carry out according to information of vibration measurement signals.
Disclosure of Invention
In order to achieve the above purpose, the technical scheme of the invention is as follows:
A hydraulic steering engine fault detection method based on self-adaptive noise removal comprises the following steps:
Collecting a plurality of groups of vibration signals of a hydraulic steering engine, wherein the hydraulic steering engine comprises an engine oil inlet of the hydraulic steering engine and a hydraulic steering engine body, and preprocessing the vibration signals to obtain a vibration signal sequence { x i (j) }, wherein j is a j-th group of vibration signal sequence, and i is an i-th signal in a group of vibration signal sequence;
Extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm, and acquiring a spectrogram according to the fault characteristic signals;
And detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering machine exist in the spectrogram, and judging the fault of the hydraulic steering machine if the frequency conversion and the frequency multiplication exist.
Optionally, the collecting the multiple sets of vibration signals of the hydraulic steering engine including the hydraulic steering engine oil inlet and the hydraulic steering engine body includes:
The method comprises the steps that an acceleration sensor is used for synchronously collecting vibration acceleration signals of an oil inlet of a hydraulic steering machine and vibration acceleration signals of a hydraulic steering machine body in three coordinate axis directions of a space rectangular coordinate system, so that vibration acceleration data are obtained;
And preprocessing the vibration acceleration data to obtain a vibration signal sequence.
Optionally, the extracting of the fault characteristic signal from the vibration signal sequence by the adaptive noise removal algorithm, and obtaining a spectrogram according to the fault characteristic signal, includes:
(1) Setting parameters of an adaptive filter;
(2) Applying a time delay delta to the vibration signal sequence { x i (j) } to obtain a delayed vibration signal sequence { x Δi (j) };
(3) Inputting the delayed vibration signal sequence { x Δi (j) } to an adaptive filter for filtering to obtain an output signal sequence { x outi (j) };
(4) The vibration signal sequence { x i (j) } and the output signal sequence { x outi (j) } are subjected to difference to obtain a difference sequence { x subi (j) };
(5) Inputting the difference sequence { x subi (j) } as a reference signal into an adaptive filter, and updating the weight Wj of the adaptive filter based on a least square algorithm;
(6) Repeating steps 1 to 5 (n- (delta+L)) for a plurality of times, and extracting a last difference sequence { x subi (j) }, wherein n is the total length of the vibration signal sequence { x i (j) };
(7) And taking the last time difference sequence { x subi (j) } as a time domain diagram, and carrying out Fourier transformation on the time domain diagram and a region corresponding to the time delay data of the difference sequence to obtain a spectrogram.
Optionally, the updating the adaptive filter weight W j based on the least squares algorithm includes:
wherein μ is the learning rate;
Epsilon k is the output error at the kth iteration, i.e. the value is equal to x subi (k);
E is the sequence mean operator.
Optionally, detecting whether there is a frequency conversion and a frequency multiplication of the hydraulic steering machine in the spectrogram, if yes, determining that the hydraulic steering machine is faulty, including:
extracting a corresponding frequency band from the spectrogram according to the frequency conversion of the hydraulic steering machine or the frequency multiplication range of the frequency conversion;
If the frequency conversion and the frequency multiplication thereof exist in the extracted frequency band, the hydraulic steering machine fault is judged.
Optionally, before extracting the corresponding frequency band from the spectrogram, adding a margin to the frequency conversion or the multiple frequency range of the frequency conversion according to the hydraulic steering gear.
Optionally, the corresponding frequency band is in the range of 0-0.6 Hz.
The invention also provides a hydraulic steering gear fault detection device based on self-adaptive noise removal, which comprises:
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 { x i (j) }, wherein j is a j-th group of vibration signal sequence, and i is an i-th signal in a group of vibration signal sequences;
The spectrogram acquisition module is used for extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm and acquiring spectrograms according to the fault characteristic signals;
the fault judging module is used for detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering machine exist in the spectrogram, and judging the fault of the hydraulic steering machine if the frequency conversion and the frequency multiplication exist.
The invention has the following technical effects: the invention analyzes the characteristics of the hydraulic steering engine fault characteristic signal and the hydraulic impact signal from the instantaneous impact and periodicity angles of the signals based on the phenomenon that the vibration measurement signal is greatly interfered by the hydraulic impact noise in the actual vibration test environment of the hydraulic steering engine of the actual vehicle front axle, combines the self-adaptive noise elimination signal processing method to separate the two signals, further analyzes the extracted fault diagnosis signal based on the fault rotor dynamics theory, and diagnoses the possible unbalance, misalignment, rub-impact and other faults of each rotor in the steering engine. The whole set of the technical route can diagnose the rotor faults of the hydraulic steering engine of the vehicle, effectively fills the theoretical and technical blank of the fault characteristic signal extraction and fault detection method of the hydraulic steering engine, and has important practicability and engineering value.
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The invention will become more apparent by describing in detail embodiments thereof with reference to the accompanying drawings, wherein a plurality of embodiments are illustrated for illustrative purposes and should not be construed to limit the scope of the embodiments in any way. Additionally, various features of the different disclosed embodiments can be combined to form additional embodiments, which are part of the present disclosure, wherein:
FIG. 1 is a flow chart of a hydraulic steering engine fault diagnosis method for self-adaptive noise removal provided by the invention;
FIG. 2 is a time domain diagram of a mixed signal composed of pre-processed oil pressure impact noise and white noise of a hydraulic steering gear fault characteristic signal;
FIG. 3 is a time domain diagram of a fault signature obtained after self-adaptive noise removal processing of an original mixed signal;
FIG. 4 is a frequency domain diagram of a fault signature obtained after self-adaptive noise removal processing of an original mixed signal;
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art will recognize that the described embodiments may be modified in various different ways, or combinations thereof, without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive in scope. Furthermore, in the present specification, the drawings are not drawn to scale, and like reference numerals denote 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 the embodiments may be practiced or carried out in various ways. Specific phrases and terms used herein are for convenience in description and should not be construed as limiting.
In the invention, the periodic characteristic signal separated by the self-adaptive noise removal method has similar characteristics to the hydraulic steering machine fault characteristic signal of the front axle steering machine of the vehicle chassis, the removed impact noise signal has similar characteristics to the oil pressure impact signal, and the judgment is made 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 signals of the oil inlet vibration measuring point and the vibration signals of the hydraulic steering engine body vibration signal measuring point, the vibration signals at the oil inlet and the impact characteristics of the hydraulic steering engine body in all directions can be obviously found. This means 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 actual measurement vibration signal of the hydraulic steering gear body is reduced.
(2) There are a large number of rotation mechanisms such as ball screws, ball bearings, and the like in the structural composition of a hydraulic steering machine of a vehicle steering machine. In an actual running state, the steering engine may have various fault forms such as rotor misalignment fault, unbalance fault, crack fault, rub-impact fault and the like. According to the theory of rotor dynamics, during the early stages of the fault, these faults may lead to the occurrence of frequency conversion and multiplication with the hydraulic steering machine in the frequency spectrum of the fault signature. The frequency conversion refers to the frequency corresponding to the rotating speed of the hydraulic steering gear, and the frequency multiplication is an integral multiple of the frequency conversion. The failure characteristic signal contains low energy, low impact and high periodicity in the initial stage of failure. Because of the interference of oil pressure impact and other noise at the oil inlet, the fault characteristic frequency of the hydraulic steering gear can be submerged in the noise, and the fault characteristic signal is difficult to extract.
The technical solutions in the embodiments will be specifically, clearly and completely described below with reference to the drawings in the embodiments.
As shown in fig. 1, the invention discloses a hydraulic steering gear fault diagnosis method of self-adaptive noise removal algorithm, which comprises the following steps:
step one: 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 steps of (1) synchronously collecting 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 a hydraulic steering engine body in three directions by using corresponding acceleration sensors according to set timing sampling parameters;
(2) The method comprises the steps of finishing to obtain vibration acceleration data of an oil inlet and three groups of hydraulic steering machine bodies;
(3) And preprocessing the collected and tidied four groups of vibration acceleration data to obtain four groups of vibration signal sequences.
The vibration acceleration signal of the hydraulic steering gear of the front axle steering gear of the vehicle chassis can be measured by a vibration acceleration sensor. Discrete vibration acceleration signals are obtained by sampling the vibration acceleration and further preprocessing this signal. The pretreatment mainly comprises the steps of trending treatment. The components with stronger trend in the signals can be removed through signal preprocessing to obtain main fluctuation components of the signals, and the processed vibration signal sequence is { x i (j) }.
Step two: extracting fault characteristic signals from the vibration signal sequence through an self-adaptive noise removal algorithm (SANC);
the method comprises the following specific steps of:
(1) According to the prior knowledge of the fault and the test condition of the hydraulic steering engine of the front axle steering engine of the vehicle chassis, the order L of the adaptive filter, the initial weight W 0 of the adaptive filter and the time delay length delta of the vibration signal sequence are selected; the super parameters that need to be selected in advance are mainly the order L of the adaptive filter, the delay time length delta, and the learning rate mu. The value of L is generally larger, so that the oil pressure impact noise and the fault characteristic of the hydraulic steering gear show obvious correlation difference; on the other hand, considering that the length of the mixed sequence is limited, L cannot be too large, so that the iterative algorithm can be guaranteed to have enough iteration times to adjust the mixed signal to meet 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 a better filtering effect, the order should be more than twice the minimum order. In general any test signal can be considered as a combination of a random signal component and a deterministic signal component, while the value of the time delay length delta needs to be longer than the associated length of the random component in the signal. The learning rate mu cannot be selected to be too large, otherwise the algorithm can not meet the convergence criterion; meanwhile, if μ is too small, it may result in too high computational complexity. The setting of the order L, the delay time length Δ, and the learning rate μ is a conventional technique, and will not be described in detail here.
(2) As shown in fig. 1, a time delay of a magnitude Δ is applied to the vibration signal sequence { x i (j) } acquired and preprocessed in the first step, resulting in a delayed vibration signal sequence { x Δi (j) }, where z -Δ represents the time delay of a magnitude Δ.
(3) The delayed vibration signal sequence { x Δi (j) } after the delay processing is input into the adaptive filter H (z) for filtering, and an output signal sequence { x outi (j) } is obtained.
(4) The initial vibration signal sequence { x i (j) } is differenced from the output signal sequence { x outi (j) }, resulting in a differenced sequence { x subi (j) }.
(5) The obtained difference sequence { x subi (j) } is input as a reference signal into the adaptive filter, and the adaptive filter weight W j is updated based on a least squares algorithm.
The updating process is as follows:
Wherein μ is a learning rate, and its value cannot be too large to avoid divergence, and cannot be too small to avoid causing too long training time; epsilon k is the output error at the kth iteration, i.e. the value is equal to x subi (k); e is the sequence mean operator.
(6) Repeating the steps 1 to 5 (n- (delta+L)) for a plurality of times, and extracting a difference sequence { x subi (j) } of the last time; where n is the total length of the original signal.
(7) And carrying out time domain diagram on the sequence { X subi (j) } extracted for the last time, and carrying out Fourier transform on the delayed part of the difference sequence to obtain a Fourier transform sequence X (&) and a corresponding spectrogram.
Step three: and (3) based on the obtained spectrogram, carrying out low-pass filtering on the frequency domain, and observing whether the low frequency band in the frequency spectrum has frequency conversion or the occurrence of frequency conversion and frequency multiplication thereof, thereby being used as a criterion of whether the hydraulic steering engine has faults or not. If there is a turn or turn and its multiple frequency, the hydraulic steering engine of the front axle steering engine of the vehicle chassis may have initial faults such as unbalanced rotor components, misalignment, rub-impact, etc.
Embodiment one: fault feature extraction simulation analysis of oil pressure 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 part can be expressed as follows: 1) Oil pressure impact noise: y 1 =50 (mod (p, T) = 0), t=1/0.30, p=0:n-1, n=32768; 2) Fault signature: y 2＝sin(w2t),w2＝2πf2,t＝n(1/Fs),Fs = 2000Hz; 3) White noise signal: y 3＝50y30,y30 to N (0, 1); 4) Trending noise: y 4 = t. In the above equations, 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 impact, f 2 represents the failure characteristic frequency, and N (0, 1) is a standard normal distribution.
Operating conditions of the hydraulic steering gear: rotational speed 8rpm (rotational frequency f 2 =0.133 Hz).
Data acquisition parameters: hydraulic steering machine vibration accelerometer sampling frequency fs=2000 Hz, hydraulic steering machine data acquisition length n=32768.
Based on the above technical embodiment and the simulation parameters, three super parameters involved in self-adaptive noise removal, namely, the order L, the delay time length delta and the learning rate mu of the adaptive filter, can be respectively determined as: l=20000; delta = 14999; μ=0.1.
First, it can be determined that the failure characteristic frequency of the hydraulic steering machine should be in the vicinity of the turning frequency thereof according to the operation condition of the hydraulic steering machine. Considering that the rotation speed is 8rpm and the corresponding rotation frequency is f 2 =0.133 Hz, the rotation frequency can be properly enlarged by a certain integer multiple to obtain corresponding frequency doubling (f 2 '=0.266 Hz) and frequency tripling (f 2' =0.40 Hz), and a small margin is properly increased, so that the failure characteristic frequency can be initially determined to be in the range of 0-0.6 Hz. When the mixed signal is analyzed, the mixed signal is preprocessed to remove the interference of trending noise y 4, and a time domain signal of the preprocessed original signal is 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 submerged in oil pressure shocks and other noise. And then the self-adaptive noise removal processing is carried out on the mixed signal, and the separated signal is obtained as shown in fig. 3. It can be found that there is a significant periodicity in the portion of the signal after the time delay; the further extracted delayed fraction is subjected to a fast fourier transform and a spectrum is shown in fig. 4, wherein the vertical axis is amplitude and the horizontal axis is frequency. Based on the spectrogram, the energy of the signal can be found to be more obviously concentrated in the frequency range (0-0.6 Hz) corresponding to the fault characteristic frequency, and obvious spectral peaks are captured near the frequency conversion (f 2 =0.133 Hz). As shown in table 1, comparing the signal-to-noise ratios before and after the self-adaptive noise removal, it can be found that the signal-to-noise ratio of the mixed signal after the self-adaptive noise removal processing is improved, that is, the fault characteristic signal of the hydraulic steering machine is effectively extracted. Based on the extracted fault characteristic signals, the frequency conversion of the fault characteristic signals can be captured through frequency spectrum analysis, and the faults of the hydraulic steering engine can be detected in a steady mode.
TABLE 1
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A hydraulic steering engine fault detection method based on self-adaptive noise removal is characterized in that: the method comprises the following steps:
Collecting a plurality of groups of vibration signals of a hydraulic steering engine, wherein the vibration signals comprise 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) }, wherein j is a j-th group of vibration signal sequence, and i is an i-th signal in a group of vibration signal sequence;
Extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm, and acquiring a spectrogram according to the fault characteristic signals;
detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering machine exist in the spectrogram, judging the fault of the hydraulic steering machine if the frequency conversion and the frequency multiplication exist,
The extracting of fault characteristic signals of the vibration signal sequence through the self-adaptive noise removal algorithm, and obtaining a spectrogram according to the fault characteristic signals comprise the following steps:
(1) Setting parameters of an adaptive filter;
(2) Applying a time delay delta to the vibration signal sequence { x i (j) } to obtain a delayed vibration signal sequence { x Δi (j) };
(3) Inputting the delayed vibration signal sequence { x Δi (j) } to an adaptive filter for filtering to obtain an output signal sequence { x outi (j) };
(4) The vibration signal sequence { x i (j) } and the output signal sequence { x outi (j) } are subjected to difference to obtain a difference sequence { x subi (j) };
(5) Inputting the difference sequence { x subi (j) } as a reference signal into an adaptive filter, and updating the weight Wj of the adaptive filter based on a least square algorithm;
(6) Repeating (n- (delta+L)) times from step 1 to step 5, and extracting a difference sequence { x subi (j) } of the last time, wherein n is the total length of the vibration signal sequence { x i (j) };
(7) And taking the last time difference sequence { x subi (j) } as a time domain diagram, and carrying out Fourier transformation on the time domain diagram and a region corresponding to the time delay data of the difference sequence to obtain a spectrogram.
2. The adaptive noise removal-based hydraulic steering machine fault detection method according to claim 1, wherein: the collection hydraulic steering machine includes the multiunit vibration signal of hydraulic steering machine oil liquid entry and hydraulic steering machine organism, includes:
The method comprises the steps that an acceleration sensor is used for synchronously collecting vibration acceleration signals of an oil inlet of a hydraulic steering machine and vibration acceleration signals of a hydraulic steering machine body in three coordinate axis directions of a space rectangular coordinate system, so that vibration acceleration data are obtained;
And preprocessing the vibration acceleration data to obtain a vibration signal sequence.
3. The method for detecting the fault of the hydraulic steering gear based on the adaptive noise removal according to claim 1, wherein the method comprises the following steps of: the updating the adaptive filter weight W j based on the least squares algorithm includes:
wherein μ is the learning rate;
Epsilon k is the output error at the kth iteration, i.e. the value is equal to x subi (k);
E is the sequence mean operator.
4. The method for detecting the fault of the hydraulic steering gear based on the adaptive noise removal according to claim 1, wherein the method comprises the following steps of: detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering machine exist in the spectrogram, and judging the fault of the hydraulic steering machine if the frequency conversion and the frequency multiplication exist, wherein the method comprises the following steps of:
extracting a corresponding frequency band from the spectrogram according to the frequency conversion of the hydraulic steering machine or the frequency multiplication range of the frequency conversion;
If the frequency conversion and the frequency multiplication thereof exist in the extracted frequency band, the hydraulic steering machine fault is judged.
5. The method for detecting the fault of the hydraulic steering gear based on the adaptive noise removal according to claim 4, wherein the method comprises the following steps of:
and before the corresponding frequency band is extracted from the spectrogram, adding a margin to the frequency conversion of the hydraulic steering machine or the frequency multiplication range of the frequency conversion.
6. The method for detecting the fault of the hydraulic steering gear based on the adaptive noise removal according to claim 4, wherein the method comprises the following steps of: the corresponding frequency range is within the range of 0-0.6 Hz.
7. Hydraulic steering machine fault detection device based on self-adaptation noise clearance, characterized by, include:
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 { x i (j) }, wherein j is a j-th group of vibration signal sequence, and i is an i-th signal in a group of vibration signal sequences;
The spectrogram acquisition module is used for extracting fault characteristic signals from the vibration signal sequence through a self-adaptive noise removal algorithm and acquiring spectrograms according to the fault characteristic signals;
the fault judging module is used for detecting whether the frequency conversion and the frequency multiplication of the hydraulic steering machine exist in the spectrogram, judging the fault of the hydraulic steering machine if the frequency conversion and the frequency multiplication exist,
The extracting of fault characteristic signals of the vibration signal sequence through the self-adaptive noise removal algorithm, and obtaining a spectrogram according to the fault characteristic signals comprise the following steps:
(1) Setting parameters of an adaptive filter;
(2) Applying a time delay delta to the vibration signal sequence { x i (j) } to obtain a delayed vibration signal sequence { x Δi (j) }
(3) Inputting the delayed vibration signal sequence { x Δi (j) } to an adaptive filter for filtering to obtain an output signal sequence { x outi (j) };
(4) The vibration signal sequence { x i (j) } and the output signal sequence { x outi (j) } are subjected to difference to obtain a difference sequence { x subi (j) };
(5) Inputting the difference sequence { x subi (j) } as a reference signal into an adaptive filter, and updating the weight Wj of the adaptive filter based on a least square algorithm;
(6) Repeating (n- (delta+L)) times from step 1 to step 5, and extracting a difference sequence { x subi (j) } of the last time, wherein n is the total length of the vibration signal sequence { x i (j) };
(7) And taking the last time difference sequence { x subi (j) } as a time domain diagram, and carrying out Fourier transformation on the time domain diagram and a region corresponding to the time delay data of the difference sequence to obtain a spectrogram.
8. The adaptive noise removal-based hydraulic steering machine fault detection device of claim 7, wherein: the collection hydraulic steering machine includes the multiunit vibration signal of hydraulic steering machine oil liquid entry and hydraulic steering machine organism, includes:
The method comprises the steps that an acceleration sensor is used for synchronously collecting vibration acceleration signals of an oil inlet of a hydraulic steering machine and vibration acceleration signals of a hydraulic steering machine body in three coordinate axis directions of a space rectangular coordinate system, so that vibration acceleration data are obtained;
And preprocessing the vibration acceleration data to obtain a vibration signal sequence.
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