CN111852837B - Clustering-based reciprocating compressor self-adaptive fault monitoring method - Google Patents
Clustering-based reciprocating compressor self-adaptive fault monitoring method Download PDFInfo
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- CN111852837B CN111852837B CN202010672515.1A CN202010672515A CN111852837B CN 111852837 B CN111852837 B CN 111852837B CN 202010672515 A CN202010672515 A CN 202010672515A CN 111852837 B CN111852837 B CN 111852837B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B51/00—Testing machines, pumps, or pumping installations
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B49/00—Control, e.g. of pump delivery, or pump pressure of, or safety measures for, machines, pumps, or pumping installations, not otherwise provided for, or of interest apart from, groups F04B1/00 - F04B47/00
- F04B49/10—Other safety measures
Abstract
The invention discloses a clustering-based reciprocating compressor self-adaptive fault monitoring method, which comprises the following steps: an off-line modeling stage and an on-line monitoring stage; the hardware components involved are: multi-source sensors (displacement, acceleration, velocity), field servers. According to the clustering-based reciprocating compressor self-adaptive fault monitoring method provided by the invention, reciprocating working condition processes are classified, intelligent early warning limits are set for all working conditions, operators are timely reminded of carrying out subsequent treatment when suspected faults occur, and economic loss is avoided or reduced. Meanwhile, the requirements of real-time performance and accuracy can be met while multi-type fault detection can be carried out. The key point is that the data is divided into clusters under different working conditions through self-adaptive clustering, and the clusters are obtained through the relation among cluster data points.
Description
Technical Field
The invention relates to the field of online mechanical fault monitoring, in particular to a clustering-based reciprocating compressor self-adaptive fault monitoring method.
Background
The reciprocating compressor is an indispensable key unit in the field of petrochemical industry, and is mainly applied to oil refining, oil extraction, gas transportation and other purposes. Due to the complex structure of the reciprocating machine, signals of all parts in the operation state are mutually excited and interfered, and fault signals of a specific part are often submerged in noise. In addition, the reciprocating machine has various failure reasons during operation, mainly including air valve type failure, filler type failure, piston failure and transmission system failure, which are reflected differently in different physical quantities, and it is difficult to evaluate the failure degree of all types by using a single physical quantity. Because the fault characteristics of the reciprocating engine are relatively complex, the current domestic detection standard of the reciprocating engine only has the intensity evaluation index [ GB/T12779-91 ] aiming at the vibration speed signal of the whole engine, the comprehensive consideration of a heterogeneous signal of the whole engine is not carried out, and a specific unified standard is not provided aiming at a certain fault characteristic. In addition, the single hard index is adopted to ignore the difference between reciprocating mechanical units. If the alarm threshold is too high, the system reaction is slow, the hysteresis is large, and small faults are ignored; otherwise, the threshold limit is too low, the system is too sensitive, false alarms occur frequently, and waste of resources and time is caused. Therefore, the step-by-step intelligent threshold value alarm limit is necessary to be divided according to the fault degree of the working condition when the reciprocating engine operates, and the accuracy rate of online monitoring alarm can be obviously improved.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the invention provides a clustering-based reciprocating compressor self-adaptive fault monitoring method. The technical scheme is as follows:
in one aspect, a clustering-based adaptive fault monitoring method for a reciprocating compressor is provided, which includes: the method comprises the steps of off-line modeling and on-line monitoring;
the offline modeling comprises the following steps:
step 1: data acquisition is carried out through multi-source sensors at different positions on the reciprocating machine, and the data are led into a field server and transmitted to a middleware;
step 2: taking data from the middleware through a TCP/IP protocol, wherein the data comprises a cylinder body vibration signal, a piston rod displacement signal and a process quantity signal, and a main analysis object is a vibration signal;
and step 3: initializing data and carrying out preliminary wavelet denoising;
and 4, step 4: obtaining the characteristics of the vibration signal data, and obtaining 6 time domain characteristics of standard deviation, root mean square, kurtosis, skewness, peak value and peak value, 5 time domain factors, 5 frequency domain characteristics of center of gravity frequency, variance frequency, mean square frequency, power spectrum energy and power spectrum peak value, and 6 chaotic characteristics of approximate entropy, sample entropy, fuzzy entropy, Teager operator energy, Teager operator peak value and LZ complexity;
and 5: respectively normalizing a time domain, a frequency domain and the chaotic characteristic set, then carrying out PCA to obtain a characteristic matrix of the vibration signal after dimension reduction, taking the characteristic matrix as a training sample, and recording corresponding parameters;
step 6: calculating the optimal cluster number of the clusters by adopting DaviesBouldin and Calinski Harabasz indexes; clustering the training samples into a plurality of classes by adopting a K-means clustering mode, carrying out second normalization in the classes, and recording normalization parameters;
and 7: extracting the edge of each cluster, dividing the data in each cluster into safety data, dangerous data and edge data by adopting LOF (low level of detail);
and 8: independently taking an edge sample set to perform modeling of the OCSVM, and storing the parameters obtained in the steps 5 and 6 and the OCSVM model into a field server;
the online monitoring comprises the following steps:
step a: data acquisition is carried out through a multi-source sensor, and the data are led into a field server;
step b: initializing data and carrying out preliminary wavelet denoising;
step c: the same as the step 4 of the off-line modeling stage, three feature groups are obtained for the real-time data;
step d: normalizing and reducing dimensions of the feature group by using the parameters obtained in the step 5 of the off-line modeling stage;
step e: normalizing by using the intra-class normalization parameters in the step 6 of the off-line modeling stage, solving the near neighbor of the real-time data point by adopting a KNN method, and outputting the cluster to which the point belongs if the point is a safe point; if the point is a dangerous point or an edge point, calculating an LOF value of a point adjacent to the point; additionally, if the point is an edge point, calling an OCSVM model of the edge of the cluster to which the point belongs to perform single classification on the point to obtain a clear outlier judgment result;
step f: classifying the outlier degree of the real-time detection points according to the LOF value of the outlier and the LOF threshold division limit of the current cluster, and recording the corresponding alarm result;
step g: and the alarm processing program feeds back the final result to a field personnel end after the alarm mechanism is triggered by the data alarm result.
Further, the normalization mode is Zscore,
wherein
σxMean and standard deviation of x, respectively. The recording parameters were specifically normalized Mean1, standard deviation Std1, and PCA score matrix Coeff.
Further, the normalization is Zscore or Zscore by Mad (Median absolute difference), and the formula of Zscore-Mad normalization is x' ═ x-media)/Mad, where media is the x Median and Mad is the x Median absolute difference. When the data in the clusters are very different, a Zscore-Mad normalization mode is adopted.
Further, the specific process of the LOF algorithm is as follows:
1. defining a K distance
The k distance of the data object q is defined as the distance from the nearest k point to the data object q in the data set, and is denoted as k-distance (q), and the k distance is referred to as Euclidean distance;
k distance neighborhood
The set of data points in the data set having a distance to the data object q of no more than k, i.e. Nk-distance (q) ({ p ∈ D { q } | D (p, q) ≦ k-distance (q) }. d (p, q) refers to the Euclidean distance between p and q;
3. reachable distance
p and q are any two points in the data set, and the reachable distance from p to q is defined as: reach-distk (p, q) ═ max { d (p, q), k-distance (q) };
4. local achievable density
The local reachable density of q refers to the inverse of the average reachable distance of q to all points in the neighborhood, and is calculated as follows:
wherein, | Nk(q) | is the number of points in the k neighborhood of q. If lrdk(q) the larger the density of q, the more normal the point q is;
5. local outlier factor
If LOF > 1, the difference between the q point density and the whole data density is large, namely, the outlier; if LOF is close to 1, the point q is normal;
6. edge extraction
Two outlier ratio control limits of p0 and p1 are set, wherein 0< p0< p1< 1.
And sequencing the size of the dimensionless LOF value to obtain a LOF sequence of the current data point from small to large.
Taking th0 ═ n × p0 and th1 ═ n × p1, wherein n is the length of the sequence, i.e., the number of data points;
data points in the sequence below th0 belong to the safety set, and this part should be included in the LOF value floating above or below 1; data points in the sequence with higher ranking than th0 belong to an unsafe set, where the data below th1 part has approached the boundary of the data, called a hazard set, representing the transition of data points from safe to outlier, and the data above th1 part has been at the boundary of the full data set, called an edge set.
Additionally, if the components in the data are complex and the outliers of individual points in the edge set are high, the noise points and the outliers should be separated by dividing the edge set.
Furthermore, the dimension reduction method is to perform zero-mean value on the feature set and then multiply the feature set by a PCA (principal component analysis) score matrix Coeff.
Further, a hardware alarm strategy is to accumulate alarm data points in a time window, wherein each alarm point is 1 point, if the alarm points continuously appear, the next alarm point is 2 times of the previous alarm point, and if the alarm accumulation score in the time window exceeds the alarm limit, the alarm is triggered and recorded; no alarm is given for a period of time after the alarm, but the current alarm score is recorded.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the clustering-based reciprocating compressor self-adaptive fault monitoring method provided by the invention, reciprocating working condition processes are classified, intelligent early warning limits are set for all working conditions, operators are timely reminded of carrying out subsequent treatment when suspected faults occur, and economic loss is avoided or reduced. Meanwhile, the requirements of real-time performance and accuracy can be met while multi-type fault detection can be carried out. The key point is that the data is divided into clusters under different working conditions through self-adaptive clustering, and the clusters are obtained through the relation among cluster data points.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an off-line modeling flow chart of a cluster-based reciprocating compressor adaptive fault monitoring method of an embodiment of the present invention;
FIG. 2 is a flow chart of an on-line monitoring method of a cluster-based reciprocating compressor adaptive fault monitoring according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a clustering-based reciprocating compressor self-adaptive fault monitoring method, which comprises the following steps of: an off-line modeling stage and an on-line monitoring stage;
in the present embodiment, the hardware components include: multi-source sensors (displacement, acceleration, velocity), field servers.
The off-line modeling phase, see fig. 1:
step 1: data acquisition is carried out through multi-source sensors at different positions on the reciprocating machine, and the data are led into a field server and transmitted to the middleware.
Step 2: and taking data from the middleware through a TCP/IP protocol, wherein the data comprises a cylinder vibration signal, a piston rod displacement signal and a process quantity signal, and a main analysis object is a vibration signal.
And step 3: initializing data and carrying out preliminary wavelet denoising.
And 4, step 4: the method comprises the steps of obtaining characteristics of vibration signal data, and obtaining 6 time domain characteristics of standard deviation, root mean square, kurtosis, skewness, peak value and peak-peak value, 5 time domain characteristics of 5 time domain factors, 5 frequency domain characteristics of center-of-gravity frequency, variance frequency, mean square frequency and the like, and 6 chaotic characteristics of approximate entropy, sample entropy, fuzzy entropy, LZ complexity and the like.
And 5: respectively normalizing the time domain, the frequency domain and the chaotic characteristic set, then carrying out PCA to obtain a characteristic matrix of the vibration signal after dimension reduction, taking the characteristic matrix as a training sample, and recording corresponding parameters.
Further, the normalization mode is Zscore,
wherein
σxMean and standard deviation of x, respectively. The recording parameters were specifically normalized Mean1, standard deviation Std1, and PCA score matrix Coeff.
Step 6: the optimal cluster number for clustering was calculated using DaviesBouldin and Calinski Harabasz indices. And clustering the training samples into a plurality of classes by adopting a K-means clustering mode, carrying out second normalization in the classes, and recording normalization parameters.
Further, the normalization is Zscore or Zscore by Mad (Median absolute difference), and the formula of Zscore-Mad normalization is x' ═ x-media)/Mad, where media is the x Median and Mad is the x Median absolute difference. When the data in the clusters are very different, a Zscore-Mad normalization mode is adopted.
And 7: and extracting the edge of each cluster, and dividing the data in the class of each cluster into safety data, dangerous data and edge data by adopting an LOF (local outlier factor) method.
Further, the specific process of the LOF algorithm is as follows:
1. defining a K distance
The k-distance of the data object q is defined as the distance from the nearest k-th point to the data object q in the data set, and is denoted as k-distance (q), and is referred to as Euclidean distance.
K distance neighborhood
The set of data points in the data set having a distance to the data object q of no more than k, i.e. Nk-distance (q) ({ p ∈ D { q } | D (p, q) ≦ k-distance (q) }. d (p, q) refers to the Euclidean distance between p and q.
3. Reachable distance
p and q are any two points in the data set, and the reachable distance from p to q is defined as: reach-distk (p, q) ═ max { d (p, q), k-distance (q) }.
4. Local achievable density
The local reachable density of q refers to the inverse of the average reachable distance of q to all points in the neighborhood, and is calculated as follows:
wherein, | Nk(q) | is the number of points in the k neighborhood of q. If lrdkThe larger (q) indicates the higher density of q and the more normal the q point.
5. Local outlier factor
If LOF > 1, it indicates that the q-point density is far from the overall data density, i.e., the outliers. LOF is close to 1, then point q is more normal.
6. Edge extraction
Two outlier ratio control limits of p0 and p1 are set, wherein 0< p0< p1< 1.
And sequencing the size of the dimensionless LOF value to obtain a LOF sequence of the current data point from small to large.
Let th 0-n p0 and th 1-n p1, where n is the length of the sequence, i.e., the number of data points.
Data points in the sequence below th0 belong to the safety set, and this part should be included in the LOF value floating above or below 1; data points in the sequence with higher ranking than th0 belong to an unsafe set, where the data below th1 part has approached the boundary of the data, called a hazard set, representing the transition of data points from safe to outlier, and the data above th1 part has been at the boundary of the full data set, called an edge set.
Additionally, if the components in the data are complex and the outliers of individual points in the edge set are high, the noise points and the outliers should be separated by dividing the edge set.
And 8: and (4) independently taking an edge sample set to perform OCSVM modeling, and storing the parameters obtained in the steps (5) and (6) and the OCSVM model into a field server.
On-line monitoring phase, see fig. 2:
step 1: and data acquisition is carried out through a multi-source sensor, and the data is imported into a field server.
Step 2: initializing data and carrying out preliminary wavelet denoising.
And step 3: as in step 4 of the off-line phase, three feature sets are obtained for the real-time data.
And 4, step 4: and (5) normalizing and reducing the dimension of the feature group by using the parameters obtained in the off-line step 5.
Furthermore, the dimension reduction method is to perform zero-mean value on the feature set and then multiply the feature set by a PCA (principal component analysis) score matrix Coeff.
And 5: normalizing by using the in-class normalization parameters in the off-line step 6 again, then solving the near neighbor of the real-time data point by adopting a KNN method, and if the point is a safe point, outputting the cluster to which the point belongs; if the point is a dangerous point or an edge point, the LOF value of the point adjacent to the point is obtained. Additionally, if the point is an edge point, an OCSVM model of the edge of the cluster to which the point belongs is called to perform single classification on the point, and a clear outlier judgment result is obtained.
Step 6: and classifying the outlier degree of the real-time detection points according to the LOF value of the outlier and the LOF threshold division limit of the current cluster, and recording the corresponding alarm result.
And 7: and the alarm processing program feeds back the final result to a field personnel end after the alarm mechanism is triggered by the data alarm result.
Further, the hardware alarm strategy is to accumulate alarm data points in a time window, wherein each alarm point is 1 point, if the alarm points continuously appear, the later alarm point is 2 times of the former alarm point, and if the alarm accumulation score in the time window exceeds the alarm limit, the alarm is triggered and recorded. No alarm is given for a period of time after the alarm, but the current alarm score is recorded.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the clustering-based reciprocating compressor self-adaptive fault monitoring method provided by the invention, reciprocating working condition processes are classified, intelligent early warning limits are set for all working conditions, operators are timely reminded of carrying out subsequent treatment when suspected faults occur, and economic loss is avoided or reduced. Meanwhile, the requirements of real-time performance and accuracy can be met while multi-type fault detection can be carried out. The key point is that the data is divided into clusters under different working conditions through self-adaptive clustering, and the clusters are obtained through the relation among cluster data points.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A clustering-based reciprocating compressor adaptive fault monitoring method is characterized by comprising the following steps: performing off-line modeling and on-line monitoring;
the offline modeling comprises the following steps:
step 1: data acquisition is carried out through multi-source sensors at different positions on the reciprocating machine, and the data are led into a field server and transmitted to a middleware;
step 2: taking data from the middleware through a TCP/IP protocol, wherein the data comprises a cylinder body vibration signal, a piston rod displacement signal and a process quantity signal, and a main analysis object is a vibration signal;
and step 3: initializing data and carrying out preliminary wavelet denoising;
and 4, step 4: obtaining the characteristics of the vibration signal data, and obtaining 6 time domain characteristics of standard deviation, root mean square, kurtosis, skewness, peak value and peak value, 5 time domain factors, 5 frequency domain characteristics of center of gravity frequency, variance frequency, mean square frequency, power spectrum energy and power spectrum peak value, and 6 chaotic characteristics of approximate entropy, sample entropy, fuzzy entropy, Teager operator energy, Teager operator peak value and LZ complexity;
and 5: respectively normalizing a time domain, a frequency domain and the chaotic characteristic set, then carrying out PCA to obtain a characteristic matrix of the vibration signal after dimension reduction, taking the characteristic matrix as a training sample, and recording corresponding parameters;
wherein the normalization mode is Zscore,
wherein
The mean and standard deviation of x, respectively; recording parameters are specifically normalized Mean1, standard deviation Std1 and PCA score matrix Coeff;
step 6: calculating the optimal cluster number of the clusters by adopting DaviesBouldin and Calinski Harabasz indexes; clustering the training samples into a plurality of classes by adopting a K-means clustering mode, carrying out second normalization in the classes, and recording normalization parameters;
and 7: extracting the edge of each cluster, dividing the data in each cluster into safety data, dangerous data and edge data by adopting LOF (low level of detail);
and 8: independently taking an edge sample set to perform modeling of the OCSVM, and storing the parameters obtained in the steps 5 and 6 and the OCSVM model into a field server;
the online monitoring comprises the following steps:
step 1: data acquisition is carried out through a multi-source sensor, and the data are led into a field server;
step 2: initializing data and carrying out preliminary wavelet denoising;
and step 3: the same as the step 4 of the off-line modeling stage, three feature groups are obtained for the real-time data;
and 4, step 4: normalizing and reducing dimensions of the feature group by using the parameters obtained in the step 5 of the off-line modeling stage;
and 5: normalizing by using the intra-class normalization parameters in the step 6 of the off-line modeling stage, solving the near neighbor of the real-time data point by adopting a KNN method, and outputting the cluster to which the point belongs if the point is a safe point; if the point is a dangerous point or an edge point, calculating an LOF value of a point adjacent to the point; additionally, if the point is an edge point, calling an OCSVM model of the edge of the cluster to which the point belongs to perform single classification on the point to obtain a clear outlier judgment result;
step 6: classifying the outlier degree of the real-time detection points according to the LOF value of the outlier and the LOF threshold division limit of the current cluster, and recording the corresponding alarm result;
and 7: after the alarm mechanism is triggered by the data alarm result, the alarm processing program feeds back the final result to the site personnel end;
the normalization mode is Zscore or Zscore through Mad, and the formula of Zscore-Mad normalization is x' ═ x-Median/Mad, wherein Median is x Median and Mad is x absolute Median difference; when the data in the clusters are very different, a Zscore-Mad normalization mode is adopted.
2. The method of claim 1, wherein the specific process of the LOF algorithm is:
1. defining a K distance
The k distance of the data object q is defined as the distance from the nearest k point to the data object q in the data set, and is denoted as k-distance (q), and the k distance is referred to as Euclidean distance;
k distance neighborhood
The set of data points in the data set having a distance to the data object q of no more than k, i.e. Nk-distance (q) ({ p ∈ D { q } | D (p, q) ≦ k-distance (q)) }; d (p, q) refers to the Euclidean distance between p and q;
3. reachable distance
p and q are any two points in the data set, and the reachable distance from p to q is defined as: reach-distk (p, q) ═ max { d (p, q), k-distance (q) };
4. local achievable density
The local reachable density of q refers to the inverse of the average reachable distance of q to all points in the neighborhood, and is calculated as follows:
wherein, | Nk(q) | is the number of points in the k neighborhood of q; if lrdk(q) the larger the density of q, the more normal the point q is;
5. local outlier factor
If LOF > 1, the difference between the q point density and the whole data density is large, namely, the outlier; if LOF is close to 1, the point q is normal;
6. edge extraction
Setting two outlier ratio control limits of p0 and p1, wherein 0< p0< p1< 1;
obtaining a LOF sequence of the current data point from small to large by sequencing the size of the LOF dimensionless value;
taking th0 ═ n × p0 and th1 ═ n × p1, wherein n is the length of the sequence, i.e., the number of data points;
data points in the sequence below th0 belong to the safety set, and this part should be included in the LOF value floating above or below 1; data points in the sequence with the ranking order higher than th0 belong to an unsafe set, wherein the data below th1 part already approaches the boundary of the data, called a danger set, representing the transition of data points from safe to outlier, and the data above th1 part already lies at the boundary of the full data set, called an edge set;
additionally, if the components in the data are complex and the outliers of individual points in the edge set are high, the noise points and the outliers should be separated by dividing the edge set.
3. The method of claim 2 wherein the dimension reduction is performed by zero averaging the feature set and then multiplying by a PCA score matrix Coeff.
4. The method of claim 3, wherein the hardware alarm strategy is to accumulate alarm data points within a time window, each alarm point is 1 point, if the alarm points continuously appear, the latter alarm point is 2 times of the former alarm point, if the alarm accumulated points within the time window exceed the alarm limit, the alarm is triggered and recorded; no alarm is given for a period of time after the alarm, but the current alarm score is recorded.
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