CN114046993A - Slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM - Google Patents
Slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM Download PDFInfo
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Abstract
The invention provides a slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM, which comprises the following steps: preprocessing multiple physical signals, and denoising original signals by adopting enhanced local mean decomposition; extracting multi-domain characteristics of a signal time domain, a signal frequency domain and a signal time-frequency domain; in order to avoid the interference of characteristic redundancy or overlapping on a subsequent evaluation process, a comprehensive evaluation index is provided to screen out the winning characteristics; multiple physical signal health indexes are constructed based on an equidistant mapping algorithm, and the performance degradation trend of the slewing bearing is effectively represented; and identifying the degradation state transition process of the slewing bearing by combining the fuzzy C mean value and the hidden Markov model, and determining an early fault point and a failure early warning point of the slewing bearing. The method adopts slewing bearing full-life acceleration test data to carry out model verification, and effectively divides the degradation state of the slewing bearing.
Description
Technical Field
The invention relates to the field of parameter evaluation, in particular to a slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM.
Background
The slewing bearing is used as a core component of a rotary mechanical complete machine, is widely applied to various large engineering practice fields, and the operational reliability of the slewing bearing directly influences the health state of mechanical equipment. The method for accurately evaluating the service life state of the slewing bearing is researched, guidance is provided for active operation and maintenance, economic and safety problems caused by slewing bearing faults or failures are effectively reduced, and the method has important engineering significance. By means of the rapid development of an artificial intelligence technology, a data driving method does not need to depend on a damage mechanism of a slewing bearing, and reliable data driving modeling becomes a mainstream method for study of scholars. The evaluation method based on data driving has strong advantages when aiming at mechanical systems such as slewing bearings with unclear or complex damage mechanisms.
Due to the fact that the actual working environment is severe, effective information in monitored sensing signals is often covered by a large amount of noise, the original characteristic extraction of the signals is affected, particularly, the slewing bearing runs at a low speed, vibration signals are weak, and therefore effective noise reduction reconstruction processing needs to be carried out on the signals. The feature extraction of the signal can reduce the calculation amount and highlight the change trend of the signal, and is one of the indispensable steps in the life state evaluation. However, the phenomenon of redundancy or overlapping of various signal characteristics often exists among the various signal characteristics, and subsequent information fusion is easily interfered, so that the influence of the characteristic redundancy on an evaluation result can be reduced to a certain extent by effectively screening the characteristics.
The state evaluation of the slewing bearing still has several problems at present: firstly, the feature extraction of the signal not only needs to meet the comprehensive requirement, but also needs to avoid the interference of feature redundancy or overlapping on the subsequent evaluation process; secondly, the health state of the slewing bearing is monitored in real time, and higher efficiency is needed for determining state transfer; and thirdly, the damage information of the slewing bearing is difficult to be comprehensively acquired by a single vibration signal.
Disclosure of Invention
The invention provides an FCM-HMM modeling strategy aiming at the characteristics of complex actual working conditions and weak acquired signals of the slewing bearing and combining the existing problems in the estimation of the service life state of the slewing bearing so as to efficiently identify the progressive degradation process in the operation process of the slewing bearing.
The invention is directed to a high-reliability slewing bearing state evaluation method under complex working conditions. Preprocessing multiple physical signals, and denoising original signals by adopting enhanced local mean decomposition; extracting multi-domain characteristics of a signal time domain, a signal frequency domain and a signal time-frequency domain; in order to avoid the interference of characteristic redundancy or overlapping on a subsequent evaluation process, a comprehensive evaluation index is provided to screen out the winning characteristics; multiple physical signal health indexes are constructed based on an equidistant mapping algorithm, and the performance degradation trend of the slewing bearing is effectively represented; and identifying the degradation state transition process of the slewing bearing by combining the fuzzy C mean value and the hidden Markov model, and determining an early fault point and a failure early warning point of the slewing bearing.
In order to achieve the purpose, the invention adopts the following technical scheme:
a slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM is characterized by comprising the following steps:
step 3, constructing multiple physical signal health indexes: the global characteristics are reserved by a nonlinear dimension reduction method, and an Isometric Mapping (ISOMAP) algorithm is selected to establish the health indexes of the vibration, temperature and torque signals of the slewing bearing;
In the step 1, the enhanced Local Mean Decomposition technology is an improved enhanced algorithm based on Local Mean Decomposition (LMD) proposed in recent years, and by optimizing three key parts of boundary conditions, envelope evaluation and screening stop criteria in the LMD algorithm, the optimal step size and the screening iteration number are determined in a self-adaptive manner, and the algorithm performance is improved.
In the step 2, the characteristic fields are as follows:
wherein, the values from (r) to (r) are time domain characteristics,
to
For the purpose of the frequency domain characterization,
is a time-frequency domain feature; wherein, X is a noise-reduced signal, xn is the nth point of X, f i represents the power spectrum frequency at the moment i, p i represents the power spectrum amplitude, and Ci (t) is the inherent mode function of signal decomposition; selecting monotonicity indexes and track difference indexes to quantitatively and effectively screen the characteristics of all physical signals of the slewing bearing; because the two quantitative evaluation indexes respectively represent the characteristic attributes from different angles, in order to comprehensively and visually screen the winning characteristics under the same physical signal, a mixed evaluation mode of linearly superposing the two quantitative evaluation indexes, namely a comprehensive evaluation index Score, is provided, and specifically:
Score＝map min max(Mon)+map min max(Div)；
in order to avoid negative influence on the screening result caused by the value domain difference of the two quantitative indexes, normalization processing is uniformly carried out before linear superposition, namely mapminmax operation in a formula.
In the step 3, an equidistant mapping algorithm is selected to establish the health indexes of the vibration, temperature and torque signals of the slewing bearing, and the problem to be solved in the dimensionality reduction algorithm is the estimation of intrinsic dimensionality, namely, the minimum embedding dimensionality of the low-dimensional space is determined under the condition of fully retaining the original information of high-dimensional data; reasonable eigen-dimension estimation is crucial. Using the classical dimensionality estimation method-Maximum Likelihood Estimation (MLE); and (3) realizing maximum likelihood estimation of the intrinsic dimension by establishing a likelihood function of the neighbor distance.
In step 4, the FCM-HMM model is established in two steps: dividing a state matrix and establishing a degradation state identification library; the specific steps of degradation state evaluation based on the FCM-HMM are as follows:
1) dividing a state matrix; carrying out unsupervised clustering on the observation sequence by adopting an FCM method, and dividing a state matrix corresponding to the state number K; initial parameters in FCM need to be preset, the clustering number is determined by a full-life acceleration test result, certain subjectivity is achieved, and meanwhile, the initial membership degree is randomly selected, so that the accurate judgment of the category of the critical point is difficult to realize; therefore, the state matrix is selected in proportion to construct an observation vector On, so that the influence of a critical point On the model is avoided; in addition, the observation sequence is a full life cycle sequence, the input type of the observation sequence is researched, and the degradation state evaluation result based on different observation sequences is discussed in a contrast manner;
2) establishing a degradation state identification library; initializing model parameters based On the constructed observation vector On, continuously optimizing the model parameters by adopting a Baum-welch algorithm, and obtaining an optimal model HMMn (lambda n) of each state; the established FCM-HMM degradation state recognition library is used for dividing the degradation stage of the current observation sequence and determining the state transition process;
3) evaluating the current degradation state of the slewing bearing; inputting the current observation sequence into an FCM-HMM degradation state recognition library, calculating the log-likelihood probability p (O | λ n) output by each model HMMn (λ n) by adopting a forward-backward algorithm, comparing n ═ arg max [ p (O | λ n) ], wherein n is the current degradation state of the slewing bearing; if n is equal to K, the slewing bearing is shown to enter a rapid decline stage of the whole life cycle, and a failure and blocking phenomenon may occur at a certain moment later, so that failure early warning should be performed at the stage, and economic loss caused by sudden failure of equipment is avoided.
Compared with the prior art, the invention has the beneficial effects that:
1. the RLMD method adopted by the invention is used for carrying out noise reduction processing on the signals, thereby effectively reducing the interference of noise components. And a multi-field feature extraction mode is adopted to construct a high-dimensional feature vector, so that the integrity of the signal is ensured. The provided comprehensive evaluation index screens multiple characteristics, and avoids the interference of characteristic redundancy or overlapping on a subsequent evaluation process.
2. The invention adopts the multi-physical signal health index constructed based on the ISOMAP algorithm, solves the problem that the damage information of the slewing bearing is difficult to be comprehensively acquired by a single vibration signal, effectively represents the performance degradation trend of the slewing bearing and lays a foundation for the subsequent state evaluation of the slewing bearing.
The FCM-HMM modeling strategy provided by the invention can more effectively divide the degradation state of the slewing bearing, greatly shorten the evaluation time and provide a good basis for judging the state transition of the slewing bearing in real time.
The FCM method provided by the invention solves the problem that the HMM needs to preset the state number of the model and the limiting conditions of the corresponding observation sequence, and determines the early fault point and the failure early warning point of the slewing bearing by utilizing the dynamic modeling capability of the HMM. The FCM-HMM method can effectively divide the degradation state of the slewing bearing, greatly shortens the evaluation time, and provides a good basis for judging the state of the slewing bearing in real time.
Drawings
FIG. 1 is a multi-physical signal pre-processing framework of the method of the present invention;
FIG. 2 is a diagram of multiple physical raw vibration signals in the present invention;
FIG. 3 is a graph of multiple physical raw temperature signals in accordance with the present invention;
FIG. 4 is a graph of multiple physical raw torque signals in accordance with the present invention;
FIG. 5 is a vibration comprehensive evaluation index diagram in the present invention;
FIG. 6 is a comprehensive evaluation index chart of temperature in the present invention;
FIG. 7 is a torque integrated evaluation index chart according to the present invention;
FIG. 8 is a schematic diagram of the vibration signal health indicator of the present invention;
FIG. 9 is a schematic diagram of a temperature signal health indicator in accordance with the present invention;
FIG. 10 is a schematic illustration of a torque signal health indicator in accordance with the present invention;
FIG. 11 is a diagram of FCM clustering results in the present invention;
FIG. 12 is a diagram showing the result of FCM-HMM evaluation in the present invention;
FIG. 13 is a schematic diagram of the state transition process of the original vibration signal in the present invention;
FIG. 14 is a schematic diagram illustrating a state transition process of an original temperature signal according to the present invention;
FIG. 15 is a schematic diagram of the state transition process of the raw torque signal in the present invention;
FIG. 16 is a diagram illustrating a state transition process of the multiple physical quantity health indicator according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the scope of the present invention.
As shown in fig. 1 to 16, the present embodiment describes a slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM, which includes the following steps:
step (1), denoising an original signal: acquiring original signals of the slewing bearing of the service part through acceleration, temperature and torque sensors, and reducing noise by using a Robust Local Mean Decomposition (RLMD);
step (2), feature extraction and screening: extracting multi-domain characteristics of a signal time domain, a signal frequency domain and a signal time-frequency domain, and providing comprehensive evaluation indexes to screen out superior characteristics in order to avoid the interference of characteristic redundancy or overlapping on a subsequent evaluation process;
step (3), constructing multiple physical signal health indexes: the global characteristics are reserved by a nonlinear dimension reduction method, and an Isometric Mapping (ISOMAP) algorithm is selected to establish the health indexes of the vibration, temperature and torque signals of the slewing bearing;
step (4), establishing a state evaluation model: based on multiple physical signal health indexes and in combination with a Fuzzy C-means (FCM) and Hidden Markov Model (HMM), namely an FCM-HMM joint modeling strategy, a state evaluation Model is established, and recognition of a slewing bearing degradation state transition process is achieved.
The enhanced Local Mean Decomposition in the step (1) is an improved enhancement algorithm based on Local Mean Decomposition (LMD) proposed in recent years, and by optimizing three key parts of boundary conditions, envelope evaluation and screening stopping standards in the LMD algorithm, the self-adaptive determination of the optimal step size and the screening iteration times is realized, and the algorithm performance is improved. The original signal is as in fig. 2. The test is ended by the dead locking of the rotation support, and the disassembly and inspection result shows that all parts are damaged in different degrees, so that the collected test data are effective. Because there are more interference in the test process, the background noise is serious, the fault signal is weak and is easy to be covered by the external interference component, so the whole change is not obvious, but the amplitude presents a slow increasing trend, which accords with the decline condition. The temperature signal shows a fluctuation rising trend due to the external environment temperature change and day-night temperature difference, and maintenance processing such as bolt adjustment, lubrication and the like is carried out near the 8 th day, so that the temperature and torque signals are reduced to some extent near the temperature and torque signals, and the temperature and torque are gradually increased due to the fading condition after restarting and finally enter a failure stage. Compared with temperature and torque signals, the vibration signal has relatively better anti-interference performance, and is commonly used for fault diagnosis and state evaluation of mechanical equipment.
The characteristic field in the step (2) is as followsTable 1 shows: phi to r are time domain features,
to
For the purpose of the frequency domain characterization,
is a time-frequency domain feature. Wherein X is a noise-reduced signal, XnN-th point of X, fiRepresenting power spectrum frequency, p, at time iiRepresenting the amplitude of the power spectrum, Ci(t) is the natural mode function of the signal decomposition. And (3) quantitatively and effectively screening the characteristics of each physical signal of the slewing bearing by using a monotonicity index and a track difference index. Because the two quantitative evaluation indexes respectively represent the characteristic attributes from different angles, in order to comprehensively and visually screen the winning characteristics under the same physical signal, a mixed evaluation mode of linearly superposing the two quantitative evaluation indexes, namely a comprehensive evaluation index Score, is provided. In order to avoid negative influence on the screening result caused by the value domain difference of the two quantitative indexes, normalization processing is uniformly carried out before linear superposition, namely mapminmax operation in a formula. The comprehensive evaluation indexes of the vibration, temperature and torque signals are shown in figure 3.
Score＝map min max(Mon)+map min max(Div)
And (3) selecting an equidistant mapping algorithm to establish the health indexes of the vibration, temperature and torque signals of the slewing bearing, wherein the problem to be solved in the dimensionality reduction algorithm is the estimation of intrinsic dimensionality, namely, the minimum embedding dimensionality of the low-dimensional space is determined under the condition of fully retaining the original information of high-dimensional data. Reasonable eigen-dimension estimation is crucial. The classical dimensionality estimation method, Maximum Likelihood Estimation (MLE), is used. And (3) realizing maximum likelihood estimation of the intrinsic dimension by establishing a likelihood function of the neighbor distance. The vibration, temperature and torque signal health indexes are shown in figure 4.
The FCM-HMM model establishment in the step (4) is divided into two steps: dividing the state matrix and establishing a degradation state identification library. The specific steps of degradation state evaluation based on the FCM-HMM are as follows:
1) the state matrix is partitioned. And carrying out unsupervised clustering on the observation sequence by adopting an FCM method, and dividing a state matrix corresponding to the state number K. Initial parameters in FCM need to be preset, the clustering number is determined by a full-life acceleration test result, certain subjectivity is achieved, and meanwhile, accurate judgment of the class of the critical point is difficult to achieve due to random selection of the initial membership degree. Therefore, the state matrix is selected in proportion to construct an observation vector On, and the influence of the critical point On the model is avoided. In addition, the observation sequence is a full life cycle sequence, the input type of the observation sequence is researched, and the degradation state evaluation result based on different observation sequences is discussed in a contrast mode.
2) And establishing a degradation state identification library. Model parameters are initialized based On the constructed observation vector On, and the model parameters are continuously optimized by adopting a Baum-welch algorithm to obtain an optimal model HMMn (lambda n) of each state. And the established FCM-HMM degradation state recognition library is used for dividing the degradation stage of the current observation sequence and determining the state transition process.
3) And evaluating the current degradation state of the slewing bearing. Inputting the current observation sequence into an FCM-HMM degradation state recognition library, calculating the log-likelihood probability p (O | λ n) output by each model HMMn (λ n) by adopting a forward-backward algorithm, comparing n ═ arg max [ p (O | λ n) ], and obtaining n as the current degradation state of the slewing bearing. If n is equal to K, the slewing bearing is indicated to enter a rapid decline stage of the whole life cycle, and a failure and locking phenomenon may occur at a certain moment later, so that failure early warning should be performed at the stage.
Dividing a state matrix corresponding to the four states by adopting an FCM (fuzzy C-means) method, wherein the number of categories is 4, m is 2, and the maximum iteration number is 150; the stop threshold is ∈ 10-5, and the clustering result is shown in fig. 5. As can be seen from the figure, the life cycle of the slewing bearing can be effectively divided into four stages based on the multiple physical signal health indexes, and the failure stage is obviously distinguished from the normal stage. However, the FCM clustering result tends to be stable after multiple operations, and meanwhile, the category of the critical point is difficult to accurately distinguish through random selection of the initial clustering center, so that the critical point is avoided, 50% of each state matrix is selected to construct an observation vector On, and a degraded state identification library HMMn (lambda n) is established. And (4) inputting the multiple physical signal health indexes established in the step (3) into a degradation state identification library HMMn (lambda n), wherein the state evaluation result of the full life cycle sequence is shown in FIG. 6. As can be seen from the figure, the difference degree of the output probabilities p (O | λ n) of different models HMMn (λ n) calculated by adopting a forward-backward algorithm is obvious, the HMM model with the maximum output probability can be clearly judged, and the corresponding state of the model is the current degradation stage of the slewing bearing. The slewing bearing degradation state was effectively partitioned based on the FCM-HMM model, and the results are shown in table 1.
TABLE 1 degradation status partitioning results
In conclusion, the slewing bearing state evaluation method based on the FCM-HMM can realize the state recognition of the current observation sequence, the state transfer process has good interpretability, and reference is provided for realizing the online state evaluation of equipment.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the preferred embodiments of the invention and described in the specification are only preferred embodiments of the invention and are not intended to limit the invention, and that various changes and modifications may be made without departing from the novel spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM is characterized by comprising the following steps:
step 1, denoising an original signal: acquiring original signals of the slewing bearing of the service part through acceleration, temperature and torque sensors, and reducing noise by using a Robust Local Mean Decomposition (RLMD);
step 2, feature extraction and screening: extracting multi-domain characteristics of a signal time domain, a signal frequency domain and a signal time-frequency domain, and providing comprehensive evaluation indexes to screen out superior characteristics in order to avoid the interference of characteristic redundancy or overlapping on a subsequent evaluation process;
step 3, constructing multiple physical signal health indexes: the global characteristics are reserved by a nonlinear dimension reduction method, and an Isometric Mapping (ISOMAP) algorithm is selected to establish the health indexes of the vibration, temperature and torque signals of the slewing bearing;
step 4, establishing a state evaluation model: based on multiple physical signal health indexes and in combination with a Fuzzy C-means (FCM) and Hidden Markov Model (HMM), namely an FCM-HMM joint modeling strategy, a state evaluation Model is established, and recognition of a slewing bearing degradation state transition process is achieved.
2. The slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM according to claim 1, wherein: in the step 1, the enhanced Local Mean Decomposition technology is an improved enhanced algorithm based on Local Mean Decomposition (LMD) proposed in recent years, and by optimizing three key parts of boundary conditions, envelope evaluation and screening stop criteria in the LMD algorithm, the optimal step size and the screening iteration number are determined in a self-adaptive manner, and the algorithm performance is improved.
3. The slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM according to claim 1, wherein: in the step 2, the characteristic fields are as follows:
wherein, the values from (r) to (r) are time domain characteristics,
to
For the purpose of the frequency domain characterization,
is a time-frequency domain feature; wherein X is a noise-reduced signal, XnN-th point of X, fiRepresenting power spectrum frequency, p, at time iiRepresenting the amplitude of the power spectrum, Ci(t) is the natural mode function of the signal decomposition; selecting monotonicity indexes and track difference indexes to quantitatively and effectively screen the characteristics of all physical signals of the slewing bearing; because the two quantitative evaluation indexes respectively represent the characteristic attributes from different angles, in order to comprehensively and visually screen the winning characteristics under the same physical signal, a mixed evaluation mode of linearly superposing the two quantitative evaluation indexes, namely a comprehensive evaluation index Score, is provided, and specifically:
Score＝map min max(Mon)+map min max(Div)；
in order to avoid negative influence on the screening result caused by the value domain difference of the two quantitative indexes, normalization processing is uniformly carried out before linear superposition, namely mapminmax operation in a formula.
4. The slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM according to claim 1, wherein: in the step 3, an equidistant mapping algorithm is selected to establish the health indexes of the vibration, temperature and torque signals of the slewing bearing, and the problem to be solved in the dimensionality reduction algorithm is the estimation of intrinsic dimensionality, namely, the minimum embedding dimensionality of the low-dimensional space is determined under the condition of fully retaining the original information of high-dimensional data; reasonable intrinsic dimension estimation is crucial; using the classical dimensionality estimation method-Maximum Likelihood Estimation (MLE); and (3) realizing maximum likelihood estimation of the intrinsic dimension by establishing a likelihood function of the neighbor distance.
5. The slewing bearing state evaluation method based on multi-feature parameter fusion and FCM-HMM according to claim 1, wherein: in step 4, the FCM-HMM model is established in two steps: dividing a state matrix and establishing a degradation state identification library; the specific steps of degradation state evaluation based on the FCM-HMM are as follows:
1) dividing a state matrix; carrying out unsupervised clustering on the observation sequence by adopting an FCM method, and dividing a state matrix corresponding to the state number K; initial parameters in FCM need to be preset, the clustering number is determined by a full-life acceleration test result, certain subjectivity is achieved, and meanwhile, the initial membership degree is randomly selected, so that the accurate judgment of the category of the critical point is difficult to realize; therefore, the state matrix is selected in proportion to construct an observation vector On, so that the influence of a critical point On the model is avoided; in addition, the observation sequence is a full life cycle sequence, the input type of the observation sequence is researched, and the degradation state evaluation result based on different observation sequences is discussed in a contrast manner;
2) establishing a degradation state identification library; initializing model parameters based On the constructed observation vector On, continuously optimizing the model parameters by adopting a Baum-welch algorithm, and obtaining an optimal model HMMn (lambda n) of each state; the established FCM-HMM degradation state recognition library is used for dividing the degradation stage of the current observation sequence and determining the state transition process;
3) evaluating the current degradation state of the slewing bearing; inputting the current observation sequence into an FCM-HMM degradation state recognition library, calculating the log-likelihood probability p (O | λ n) output by each model HMMn (λ n) by adopting a forward-backward algorithm, comparing n ═ arg max [ p (O | λ n) ], wherein n is the current degradation state of the slewing bearing; if n is equal to K, the slewing bearing is shown to enter a rapid decline stage of the whole life cycle, and a failure and blocking phenomenon may occur at a certain moment later, so that failure early warning should be performed at the stage, and economic loss caused by sudden failure of equipment is avoided.
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