CN117668498A - Pump health assessment method based on reliability distribution and anomaly detection - Google Patents
Pump health assessment method based on reliability distribution and anomaly detection Download PDFInfo
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- 238000012423 maintenance Methods 0.000 claims abstract description 9
- 238000007476 Maximum Likelihood Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000012544 monitoring process Methods 0.000 claims description 8
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- 238000004364 calculation method Methods 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 3
- 238000011478 gradient descent method Methods 0.000 claims description 3
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- 230000007774 longterm Effects 0.000 abstract description 4
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Abstract
The invention discloses a pump health assessment method based on reliability distribution and anomaly detection, which comprises the following steps: acquiring maintenance and replacement records of key components in pump equipment, and performing data processing on the records to obtain a life sample; calculating life distribution of the life samples based on a maximum likelihood estimation algorithm; calculating the operation reliability of each key component in the pump equipment according to the service life distribution; collecting real-time data of the current operation of the pump equipment; calculating an abnormality index of the pump equipment running in real time by using a clustering abnormality detection algorithm; calculating the operation health degree of the pump equipment according to the abnormality index and the operation reliability degree: if the value of the operation health degree is close to 1, the operation state of the pump equipment is healthy; if the value of the operational health is closer to 0, the pump equipment components are operating abnormally. On the basis of abnormality detection of the real-time running state of the equipment, the method increases the long-term degradation influence of the equipment based on reliability distribution, reduces the fault risk of key parts of the pump equipment, and reduces the potential running risk and maintenance cost.
Description
Technical Field
The invention relates to the technical field of pump equipment health monitoring, in particular to a pump health assessment method based on reliability distribution and anomaly detection.
Background
At present, the health monitoring of pump equipment is mainly divided into two types, one type is statistical analysis based on reliability life distribution, the method is obtained based on a statistical result of a large sample, and the reliability of the pump equipment can be estimated to be gradually reduced along with the increase of the running time, but the applicability of the pump equipment to single equipment is poor; the other type is an abnormality detection method based on the operation parameters of the equipment, which can evaluate whether the real-time operation state of the pump equipment is abnormal or not, but cannot effectively consider the long-term degradation trend of the equipment.
U.S. patent application publication No. 2019/0040812 (812 publication) describes a method of diagnosing a high-pressure fuel delivery system that includes sensing a starting fuel pressure in a fuel rail to establish or increase fuel pressure within the fuel rail upon engine start-up and misfire; the method determines a start leak rate based on the start fuel pressure, and when the start leak rate is greater than a start leak threshold, the method may identify a leak or inefficiency in the high pressure fuel pump, however, the method disclosed at 812 may not adequately determine the health of the pump and/or may falsely diagnose a leak or inefficiency of the pump.
CN111314463a discloses a method based on pump station unit health assessment, which establishes a network system overall architecture of a data acquisition front end combined with a monitoring network and remote diagnosis according to the requirements of pump station unit health assessment, and comprises a sensing layer, a network layer and an application layer, wherein a data acquisition device according to the sensing layer acquires corresponding data of the pump station unit, the network layer sends the acquired data to a server of the application layer, and judges whether to perform data analysis and fault diagnosis after comparing the acquired data with a preset alarm threshold value, and finally issues a diagnosis result; however, the method cannot effectively consider the long-term degradation trend of the equipment, and has poor applicability to single equipment.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
In order to solve the technical problems, the invention provides the following technical scheme: collecting maintenance and replacement records of key components in pump equipment, and performing data processing on the records to obtain life samples;
calculating life distribution of the life samples based on a maximum likelihood estimation algorithm;
calculating the operation reliability of each key component in the pump equipment according to the service life distribution;
collecting real-time data of the current operation of the pump equipment;
calculating an abnormality index of the pump equipment running in real time by using a clustering abnormality detection algorithm;
calculating the operation health degree of the pump equipment according to the abnormality index and the operation reliability degree:
if the value of the operation health degree is close to 1, the operation state of the pump equipment is healthy;
if the value of the operational health is closer to 0, the pump apparatus component is operating abnormally.
As a preferable mode of the pump health evaluation method based on reliability distribution and abnormality detection according to the present invention, the maintenance replacement record includes a damage time of the critical component and a replacement component type;
the key components comprise a pump body, a motor and a bearing;
the data processing is to record and sort the service time of each component to obtain the life sample { x } 1 ,x 2 ,...x n }。
As a preferable mode of the pump health evaluation method based on reliability distribution and abnormality detection according to the present invention, calculating the life distribution includes:
fitting life distribution of each key component according to the maximum likelihood estimation algorithm, and defining life distribution compliance Weibull distribution of the key component of the pump equipment, wherein probability density functions are as follows:
；
wherein,is a scale parameter, k is a shape parameter, and t is a run time.
As a preferable scheme of the pump health evaluation method based on reliability distribution and anomaly detection, the mathematical expression formula of the log likelihood function corresponding to the key component is as follows:
；
where n is the number of life samples,is the i-th life sample;
estimating parameters lambda and k of the Weibull distribution by maximizing a log-likelihood function;
finding parameter values maximizing a log-likelihood function by using a gradient descent method to obtain a life distribution of the key component。
As a preferable scheme of the pump health evaluation method based on reliability distribution and abnormality detection, after the life distribution calculation of each key component is completed, the variation of the reliability of each component along with the running time of a single component can be obtained, and the reliability of the ith component is defined as follows:
；
wherein T is the current time, T i For the length of time the component has been running since the last replacement.
As a preferable scheme of the pump health evaluation method based on reliability distribution and anomaly detection, according to the reliability of the serial system, the mathematical expression formula of the reliability of the whole pump equipment of m key components is as follows:
；
wherein R (t) is a value between 0 and 1, and a value closer to 1 indicates a higher reliability and a value closer to 0 indicates a lower reliability.
As a preferable scheme of the pump health assessment method based on reliability distribution and anomaly detection, the method acquires real-time data of the current operation of the pump equipment, including flow, pressure, vibration, temperature, current and rotating speed;
define the data sample of p monitoring parameters that gathers as Y= { Y 1 ,y 2 ,...y p }。
As a preferable scheme of the pump health assessment method based on reliability distribution and anomaly detection, the definition of the normal running state of the pump equipment is completed based on a clustering anomaly detection algorithm, the real-time running state of the pump equipment is compared with the defined normal running state, deviation between the real-time running state and the defined normal running state is quantified, and the real-time running anomaly index of the pump equipment is formed.
As a preferable scheme of the pump health evaluation method based on reliability distribution and anomaly detection, the calculation formula of the health degree is a fusion index of the operation reliability and the anomaly index, and the method comprises the following steps:
；
wherein HI (t) is a value between 0 and 1, and a value closer to 1 indicates that the operation state of the pump equipment component is healthy, and a value closer to 0 indicates that the pump equipment component is abnormal;
when HI (t) is less than 0.6, triggering an alarm threshold to start, and sending out a pump equipment health alarm.
The invention has the beneficial effects that:
1. according to the method, two monitoring methods are combined, on the basis of abnormality detection of the real-time running state of the equipment, the long-term degradation influence of the equipment based on reliability distribution is increased, the running health of the pump equipment is estimated more comprehensively, the fault risk of key parts of the pump equipment is reduced, the problems can be reacted and treated early, and the potential running risk and the maintenance cost are reduced.
2. The method is used for identifying whether the pump equipment has abnormal parts or not, and giving corresponding alarm when the abnormality occurs, so as to serve as a reference basis for operation and maintenance.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a pump health assessment method based on reliability distribution and anomaly detection according to the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings.
All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Referring to fig. 1, a pump health assessment method based on reliability distribution and anomaly detection according to an embodiment of the present invention specifically includes the following steps:
s1: and acquiring maintenance and replacement records of key components in the pump equipment, and performing data processing on the records to obtain a life sample. The step needs to be described as follows:
the maintenance replacement record includes the time of damage to the critical component and the type of the replacement component;
the key components comprise a pump body, a motor and a bearing.
In an alternative embodiment, the data processing is to record and sort the age of each component to obtain a life sample.
As an example, the aggregate form of life samples is { x 1 ,x 2 ,...x n }。
S2: the lifetime distribution of the lifetime samples is calculated based on a maximum likelihood estimation algorithm. The step of calculating the lifetime distribution includes:
fitting the life distribution of each key component according to a maximum likelihood estimation algorithm, and defining the life distribution obeying Weibull distribution of the key component of the pump equipment, wherein the probability density function is as follows:
；
wherein,is a scale parameter, k is a shape parameter, and t is a run time.
Further, the mathematical expression formula of the log likelihood function corresponding to the key component is as follows:
；
where n is the number of life samples,is the i-th life sample;
estimating parameters lambda and k of the Weibull distribution by maximizing a log-likelihood function;
finding out the parameter value maximizing the log likelihood function by using the gradient descent method to obtain the life distribution of the key component。
S3: based on the life distribution, operational reliability of each critical component in the pump apparatus is calculated. The step also needs to be described, after the life distribution calculation of each key component is completed, the reliability of each component can be obtained along with the change of the running time of the single component, and the reliability of the ith component is defined as follows:
；
wherein T is the current time, T i For the length of time the component has been running since the last replacement.
Specifically, according to the reliability of the serial system, the mathematical expression formula of the reliability of the pump apparatus as a whole of m key components is as follows:
；
wherein R (t) is a value between 0 and 1, and a value closer to 1 indicates a higher reliability and a value closer to 0 indicates a lower reliability.
S4: real-time data of the current operation of the pump device is collected. The step is to collect real-time data of the current operation of the pump equipment, including flow, pressure, vibration, temperature, current and rotation speed.
In an alternative embodiment, the data samples defining the p collected monitoring parameters are y= { Y 1 ,y 2 ,...y p }。
S5: and calculating an abnormality index of the pump equipment running in real time by using a clustering abnormality detection algorithm. The step also needs to be described, namely, the definition of the normal running state of the pump equipment is completed based on the clustering abnormality detection algorithm, the real-time running state of the pump equipment is compared with the defined normal running state, the deviation between the real-time running state and the defined normal running state is quantized, and the real-time running abnormality index of the pump equipment is formed. Wherein:
(1) For the monitored data sample y= { Y 1 ,y 2 ,...y p Data normalization was performed, and the mathematical expression was as follows:
；
wherein,is normalized data, y is original data, < ->As the minimum value in the data sample set,maximum value in the data sample set;
setting a neighborhood radiusAnd a minimum number of neighbors minPts.
As an example of this, the number of devices,=0.1，minPts=10。
it should be noted that, the neighborhood radius specifies that the distance between two adjacent points is less than a certain number and can be classified into one class, and the minimum number of adjacent points defines the minimum number of points in one class.
(2) C clustering centers are formed through randomly set condition iteration core points；
Wherein C is i For the P-dimensional variable, each monitoring record data point is the P-dimensional variable, and the attribution of the data point clustering category is judged as the clustering center category of which the data point is nearest.
(3) Calculating average distance L from data point to clustering center under each category 1 ,. Lc, average center distance；
The distance from the data point at a certain moment to the clustering center is recorded as Lt.
(4) The real-time operation abnormality index of the pump equipment is as follows:
；
wherein H (t) is a value between 0 and 1, and the closer to 1 is that the pump equipment operation parameter is normal, and the closer to 0 is that the pump equipment operation parameter is abnormal.
S6: calculating the operation health degree of the pump equipment according to the abnormality index and the operation reliability degree:
a: if the value of the operation health degree is close to 1, the operation state of the pump equipment is healthy;
b: if the value of the operational health is closer to 0, the pump equipment components are operating abnormally.
As an example, the calculation formula of the health degree is a fusion index of the operation reliability and the abnormality index, as follows:
；
wherein HI (t) is a value between 0 and 1, and a value closer to 1 indicates that the operation state of the pump equipment component is healthy, and a value closer to 0 indicates that the pump equipment component is abnormal.
In an alternative embodiment, when HI (t) < 0.6, an alarm threshold is triggered to activate, and a pump device health alarm is issued.
It should be noted that, the alarm threshold may be set according to the actual operating situation of the field device, for example, the threshold may be set to (0.5,0.6), which is not limited by the embodiment of the present invention.
In an alternative embodiment, the bearer form of the pump device health alert may be pushed through the system platform or a cell phone SMS notification.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (9)
1. A pump health assessment method based on reliability distribution and anomaly detection, comprising:
collecting maintenance and replacement records of key components in pump equipment, and performing data processing on the records to obtain life samples;
calculating life distribution of the life samples based on a maximum likelihood estimation algorithm;
calculating the operation reliability of each key component in the pump equipment according to the service life distribution;
collecting real-time data of the current operation of the pump equipment;
calculating an abnormality index of the pump equipment running in real time by using a clustering abnormality detection algorithm;
calculating the operation health degree of the pump equipment according to the abnormality index and the operation reliability degree:
if the value of the operation health degree is close to 1, the operation state of the pump equipment is healthy;
if the value of the operational health is closer to 0, the pump apparatus component is operating abnormally.
2. The pump health assessment method based on reliability distribution and anomaly detection of claim 1, wherein the service replacement record includes a time of failure of the critical component and a replacement component type;
the key components comprise a pump body, a motor and a bearing;
the data processing is to record and sort the service time of each component to obtain the life sample { x } 1 ,x 2 ,...x n }。
3. The pump health assessment method based on reliability distribution and anomaly detection of claim 1, wherein calculating the life distribution comprises:
fitting life distribution of each key component according to the maximum likelihood estimation algorithm, and defining life distribution compliance Weibull distribution of the key component of the pump equipment, wherein probability density functions are as follows:
；
wherein,is a scale parameter, k is a shape parameter, and t is a run time.
4. A pump health assessment method based on reliability distribution and anomaly detection according to claim 3, wherein the mathematical expression formula of the log likelihood function corresponding to the key component is as follows:
；
where n is the number of life samples,is the i-th life sample;
estimating parameters lambda and k of the Weibull distribution by maximizing a log-likelihood function;
finding parameter values maximizing a log-likelihood function by using a gradient descent method to obtain a life distribution of the key component。
5. The pump health assessment method based on reliability distribution and anomaly detection according to claim 4, wherein after the life distribution calculation of each critical component is completed, the variation of the reliability of each component with the operation time of a single component is obtained, and the reliability of the ith component is defined as:
；
wherein T is the current time, T i For the length of time the component has been running since the last replacement.
6. The pump health assessment method based on reliability distribution and anomaly detection according to claim 5, wherein the mathematical expression formula of the reliability of the pump apparatus as a whole of m key components is as follows, based on the reliability of the serial system:
；
wherein R (t) is a value between 0 and 1, and a value closer to 1 indicates a higher reliability and a value closer to 0 indicates a lower reliability.
7. The pump health assessment method based on reliability distribution and anomaly detection of claim 1, wherein real-time data of the current operation of the pump device is collected, including flow, pressure, vibration, temperature, current, rotational speed;
define the data sample of p monitoring parameters that gathers as Y= { Y 1 ,y 2 ,...y p }。
8. The pump health assessment method based on reliability distribution and anomaly detection according to claim 1 or 7, wherein definition of the normal operation state of the pump equipment is completed based on a clustering anomaly detection algorithm, the real-time operation state of the pump equipment is compared with the defined normal operation state, deviation between the two is quantified, and the real-time operation anomaly index of the pump equipment is formed.
9. The pump health assessment method based on reliability distribution and anomaly detection according to claim 1, wherein the calculation formula of the health degree is a fusion index of the operational reliability degree and the anomaly index, as follows:
；
wherein HI (t) is a value between 0 and 1, and a value closer to 1 indicates that the operation state of the pump equipment component is healthy, and a value closer to 0 indicates that the pump equipment component is abnormal;
when HI (t) is less than 0.6, triggering an alarm threshold to start, and sending out a pump equipment health alarm.
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