CN113485287A - Multi-granularity-measurement dynamic system fault diagnosis method and system - Google Patents

Multi-granularity-measurement dynamic system fault diagnosis method and system Download PDF

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CN113485287A
CN113485287A CN202110697377.7A CN202110697377A CN113485287A CN 113485287 A CN113485287 A CN 113485287A CN 202110697377 A CN202110697377 A CN 202110697377A CN 113485287 A CN113485287 A CN 113485287A
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CN113485287B (en
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余伟
江艳
陈睿
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Foshan University
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

The invention relates to the technical field of fault diagnosis, in particular to a multi-granularity-measurement dynamic system fault diagnosis method and a multi-granularity-measurement dynamic system fault diagnosis system, wherein the method comprises the following steps: acquiring a first state quantity acquired in advance, wherein the first state quantity is acquired by a sensor when a permanent magnet synchronous motor system normally operates; when the permanent magnet synchronous motor system runs, acquiring a second state quantity acquired by the sensor in real time for the permanent magnet synchronous motor system; determining a training value according to the first state quantity, and taking the second state quantity as a test value; performing multi-granularity measurement according to the training value and the test value to obtain a plurality of measurement values; each metric value is used for representing the variation of the permanent magnet synchronous motor system; carrying out fault diagnosis on the permanent magnet synchronous motor system according to the plurality of measurement values; the invention can improve the diagnosis precision of early faults and different types of faults.

Description

Multi-granularity-measurement dynamic system fault diagnosis method and system
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a multi-granularity-measurement dynamic system fault diagnosis method and system.
Background
Fault diagnosis techniques for dynamic systems have developed rapidly over the past few decades and have yielded abundant research results. The fault diagnosis methods can be roughly classified into two categories: (1) an analytical model-based approach; (2) a data-driven based method. The main theory of the fault diagnosis method based on the analytical model is that on the basis of modeling of state space representation or transfer function of a prior model, data residual error information is generated through the model to reflect the difference between the theoretical operation and the actual state of a system, and then the residual error information is introduced into the fault diagnosis research. As the degree of information dependence of system analysis increases, research on data-driven methods is also increasingly important. The data driving method is to process and analyze the system data without depending on the analytic model, so as to obtain the results of fault detection and isolation.
Most of the conventional fault diagnosis methods, whether based on models or data driving, design a single control limit and a threshold value to perform fault diagnosis research of a dynamic system under the framework of Euclidean measurement.
In the fault diagnosis method based on the analytic model and the data drive, the deviation (residual estimation) based on physical values under the framework of Euclidean measurement is only an expression, is extremely dependent on the physical dimension of each component, can only reflect the difference of the absolute values of the physical values, and is difficult to depict the intrinsic essential change of the system performance, for example, the absolute value of a certain physical value is changed greatly, although the detection is easy, the influence on the system performance is small; the absolute value of the other physical value changes slightly, so that the detection is difficult, but the influence on the system performance is large; therefore, the existing fault diagnosis method is not beneficial to improving the early fault diagnosis and the fault diagnosis precision of different categories.
Disclosure of Invention
The present invention is directed to a method and system for diagnosing a fault in a multi-granularity dynamic system, which solves one or more problems of the prior art and provides at least one useful choice or creation condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a multi-granularity metric dynamic system fault diagnosis method, the method comprising the steps of:
acquiring a first state quantity acquired in advance, and determining a training value according to the first state quantity; the first state quantity is acquired by a sensor when a permanent magnet synchronous motor system normally operates;
in the running process of the permanent magnet synchronous motor system, the sensor is used for collecting the permanent magnet synchronous motor system in real time to obtain a second state quantity, and the second state quantity is used as a test value;
performing multi-granularity measurement according to the training value and the test value to obtain a plurality of measurement values; each metric value is used for representing the variation of the permanent magnet synchronous motor system;
and carrying out fault diagnosis on the permanent magnet synchronous motor system according to the plurality of measurement values.
Further, the performing multi-granularity measurement according to the training value and the test value to obtain a plurality of measurement values, including at least two of:
performing Euclidean measurement on the training value and the test value, and calculating to obtain a first measurement value;
calculating the relative change rate of the training value and the test value to obtain a second metric value;
and performing v-gap measurement on the training value and the test value, and calculating to obtain a third measurement value.
Further, the calculation formula of the first metric value is as follows:
d (test value, training value) | test value-training value |;
wherein d (test value, training value) is the first metric.
Further, the calculation formula of the second metric value is as follows:
Figure BDA0003128381750000021
the relative rate of change in the equation is the second metric.
Further, the calculation formula of the third metric value is as follows:
Figure BDA0003128381750000022
wherein, deltavThe (test value, training value) is the third metric value.
Further, the fault diagnosis of the permanent magnet synchronous motor system according to the plurality of measurement values comprises:
respectively determining a threshold value corresponding to each metric value;
if the multiple measurement values are all smaller than the corresponding threshold values, the state of the permanent magnet synchronous motor system is judged to be normal, and otherwise, the state of the permanent magnet synchronous motor system is judged to be a fault.
Further, the method further comprises:
respectively establishing a coordinate system corresponding to each measurement value, drawing the corresponding measurement value in each coordinate system in real time, and displaying a threshold value corresponding to the measurement value to obtain a multi-granularity measurement coordinate graph updated in real time;
and presenting the real-time updated multi-granularity measurement coordinate graph.
A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the multi-granularity metric dynamic system fault diagnosis method described in any one of the above.
A multi-granularity metric dynamic system fault diagnosis system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the multi-granularity metric dynamic system fault diagnosis method of any of the above.
The invention has the beneficial effects that: the invention discloses a multi-granularity measurement dynamic system fault diagnosis method and a multi-granularity measurement dynamic system fault diagnosis system, which solve the difficulty that the traditional European measurement is difficult to describe the essence of the system, and are different from the conventional fault diagnosis that a single control limit or threshold is set. Through the design of the fault diagnosis method with multi-granularity measurement, various faults of the dynamic system can be diagnosed, and the improvement of the fault diagnosis precision of different types is facilitated.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described 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 without inventive exercise.
FIG. 1 is a flow chart of a multi-granularity-metric dynamic system fault diagnosis method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the geometrical significance of Riemann's sphere and v-gap measurements in an embodiment of the invention.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, fig. 1 shows a multi-granularity dynamic system fault diagnosis method provided by an embodiment of the present application, where the method includes the following steps:
s100, acquiring a first state quantity acquired in advance, and determining a training value according to the first state quantity; the first state quantity is acquired by a sensor when a permanent magnet synchronous motor system normally operates;
step S200, in the running process of the permanent magnet synchronous motor system, acquiring the permanent magnet synchronous motor system in real time through the sensor to obtain a second state quantity, and taking the second state quantity as a test value;
step S300, performing multi-granularity measurement according to the training value and the test value to obtain a plurality of measurement values; each metric value is used for representing the variation of the permanent magnet synchronous motor system;
and S400, carrying out fault diagnosis on the permanent magnet synchronous motor system according to the plurality of measurement values.
In some embodiments, the state quantity of the dynamic system can be collected by a sensor at a certain frequency, and in order to meet the requirement of timeliness when data is collected, a second state quantity can be collected every 10 minutes; the first state quantity and the second state quantity contain the same physical quantity, and the physical quantity contains at least one of voltage and current. Specifically, in the actual operation of the permanent magnet synchronous motor system, state quantities such as voltage, current and the like are directly measured through a sensor, wherein a training value is acquired when the permanent magnet synchronous motor system normally operates, and a test value is acquired when the permanent magnet synchronous motor system operates.
The multi-granularity measurement method in the embodiment is an innovative idea of fault diagnosis from outside to inside, the multi-granularity measurement fault diagnosis is used for fault detection of different types, the idea of macroscopic view and then microscopic view is similar, the multi-granularity measurement method is designed, the improvement of the precision of the fault diagnosis of different types can be facilitated, and the innovative idea of the fault diagnosis is provided. The invention provides a multi-granularity measurement fault diagnosis method for a dynamic system, and provides a new idea and a new method for fault diagnosis of the dynamic system.
As a further improvement of the foregoing embodiment, in step S100, the acquiring a first state quantity collected in advance, and determining a training value according to the first state quantity includes:
when a permanent magnet synchronous motor system normally operates, acquiring a first state quantity through a sensor, wherein the first state quantity comprises a plurality of groups of state quantities; each group of state quantities comprises at least one state period and a corresponding state value in each state period;
carrying out denoising treatment on the plurality of groups of state quantities respectively to obtain a plurality of groups of state quantities subjected to denoising treatment;
and solving an average value of the plurality of groups of state quantities subjected to denoising processing to obtain a training value.
It should be noted that, the permanent magnet synchronous motor system is used as a dynamic system, the state quantities of which change with time, and the state quantities at different times have different sizes, so that when performing multi-granularity measurement according to the training value and the test value, the state quantity of at least one state cycle needs to be extracted, and the training value and the test value at the corresponding time need to be measured; in the embodiment, the multiple groups of state quantities are denoised respectively, and an average value is obtained, so that the obtained training values can more accurately reflect the state quantities of the permanent magnet synchronous motor system during normal operation, and the accuracy of subsequent fault diagnosis is improved.
As a further improvement of the foregoing embodiment, in step S300, the performing multi-granularity measurement according to the training value and the test value to obtain a plurality of measurement values includes at least two of the following:
performing Euclidean measurement on the training value and the test value, and calculating to obtain a first measurement value;
calculating the relative change rate of the training value and the test value to obtain a second metric value;
and performing v-gap measurement on the training value and the test value, and calculating to obtain a third measurement value.
The invention takes a dynamic system as a research object, designs a multi-granularity measurement fault diagnosis method, and is favorable for early fault diagnosis and improvement of different types of fault diagnosis precision from a new perspective of residual multi-granularity measurement, such as Euclidean distance, relative change rate and v-gap measurement (v-gap metric), in order to overcome the difficulty of difficultly depicting the inherent change of system performance. The invention opens up a new idea and a new method for fault diagnosis of complex systems such as dynamic systems.
As a further improvement of the above embodiment, the calculation formula of the first metric value is:
d (test value, training value) | test value-training value |;
wherein d (test value, training value) is the first metric.
It should be noted that the euclidean metric is a commonly used distance definition, which refers to the real distance between two points in the m-dimensional space, or the natural length of the vector (i.e., the distance from the point to the origin). The euclidean distance in two and three dimensions is the actual distance between two points.
The distance formula of the m-dimensional space is:
Figure BDA0003128381750000051
wherein (x)i,yi) Representing any point in the m-dimensional space.
In this embodiment, the euclidean metric is performed by combining the test value and the training value to obtain the calculation formula of the first metric value.
As a further improvement of the above embodiment, the calculation formula of the second metric value is:
Figure BDA0003128381750000052
the relative rate of change in the equation is the second metric.
As a further improvement of the above embodiment, the calculation formula of the third metric value is:
Figure BDA0003128381750000053
wherein, deltavThe (test value, training value) is the third metric value.
In some embodiments, use is made of
Figure BDA0003128381750000054
And
Figure BDA0003128381750000055
representing a real number P1,P2E.g. spherical projection of R on Riemann's sphere of diameter 1, if P1And P2The chord distance therebetween is deltav(P1,P2) Is represented byv(P1,P2) Is defined by the formula:
Figure BDA0003128381750000056
if using theta (P)1,P2) Represents P1And P2By spherical distance therebetween, i.e. connected on the Riemann ball
Figure BDA0003128381750000057
And
Figure BDA0003128381750000058
arc length of (d), then:
Figure BDA0003128381750000059
referring to FIG. 2, it can be seen from FIG. 2 that the shortest arc length on the circle is determined by passing through the center of the sphere,
Figure BDA00031283817500000510
And
Figure BDA00031283817500000511
the plane determined by the three points is obtained by intercepting the Riemannian ball;
as a class of metrology methods, the v-gap metric satisfies three elements of the metric space (non-negative, symmetric, triangular inequalities). Similar to the properties in the control system, the v-gap metric has the following properties in data space:
(1) the v-gap metric can be regarded as a distance characterization of data in Riemann space sphere level projection, and is an extension of the traditional Euclidean metric space metric method.
(2) The value of the v-gap metric is between 0 and 1. The smaller the value, the closer the features of the two data sets. The larger the value, the greater the difference in characteristics of the two data sets. If the difference metric of two datasets is 0, then they contain exactly the same features;
as a further improvement of the above embodiment, the step S500 includes:
step S510, respectively determining a threshold value corresponding to each metric value;
and step S520, if the multiple measurement values are all smaller than the corresponding threshold values, judging that the state of the permanent magnet synchronous motor system is normal, otherwise, judging that the state of the permanent magnet synchronous motor system is a fault.
As shown in Table 1, Table 1 shows the strategy for determining whether a PMSM system is out of order, where ξ1Representing a first metric, η1Representing a first threshold value, ξ2Representing a second measure, η2Indicating a second threshold value, ξ3Representing a third metric, eta3Representing a third threshold.
TABLE 1 Multi-granularity metric Fault Distinguishing policy Table
Figure BDA0003128381750000061
As a further improvement of the foregoing embodiment, in step S510, the respectively determining the threshold corresponding to each metric value includes:
respectively determining a first threshold value of the first metric value, a second threshold value of the second metric value and a third threshold value of the third metric value;
wherein, the value range of the first threshold is [0, 0.05P ], the value range of the second threshold is [ -5%, + 5% ], and the value range of the third threshold is:
Figure BDA0003128381750000062
where P represents a training value.
For example, when the training value P is 1, the first threshold value has a value range of [0, 0.05], the second threshold value has a value range of [ -5%, + 5% ], and the third threshold value has a value range of [0.02438, 0.02563 ]. When the training value P is 2, the first threshold value has a value range of [0, 0.1], the second threshold value has a value range of [ -5%, + 5% ], and the third threshold value has a value range of [0.01923, 0.02354 ]. That is to say, the second threshold reflects a ratio of the test value to the training value, and the first threshold and the third threshold change along with the change of the training value, so that the threshold corresponding to each measurement value can measure different permanent magnet synchronous motor systems, thereby reflecting actual states to the different permanent magnet synchronous motor systems.
It can be understood that, since the permanent magnet synchronous motor system is a dynamic system, the threshold value corresponding to each metric value is updated according to actual conditions. Theoretically, the first threshold value has a value range of [0, ∞ ], the second threshold value has a value range of [ - ∞%, ∞% ], and the third threshold value has a value range of [0, 1 ]. In this embodiment, the allowable deviation for each test value and training value is set within 5%, that is, only 5% fluctuation of the state quantity of the permanent magnet synchronous motor system is allowed for each measurement value.
As a further refinement of the above embodiment, the method further comprises:
step S610, respectively establishing a coordinate system corresponding to each metric value, drawing the corresponding metric value in each coordinate system in real time, and displaying a threshold value corresponding to the metric value to obtain a multi-granularity metric coordinate graph updated in real time;
and S620, presenting the real-time updated multi-granularity measurement coordinate graph.
In some embodiments, a first coordinate system, a second coordinate system and a third coordinate system are established respectively; the first coordinate system is used for displaying a first threshold value and drawing the first metric value in real time to obtain a first coordinate graph updated in real time; the second coordinate system is used for displaying a second threshold value and drawing the second metric value in real time to obtain a second coordinate graph updated in real time; the third coordinate system is used for displaying a third threshold value and drawing the third metric value in real time to obtain a real-time updated third coordinate graph; by drawing the data calculated under the multi-granularity measurement and the threshold value into a coordinate graph and updating the coordinate graph in real time and visualizing (displaying the graph to a screen) the drawn graph, a user can conveniently know the state of the permanent magnet synchronous motor system in real time.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, on which a multi-granularity dynamic system fault diagnosis program is stored, and when executed by a processor, the multi-granularity dynamic system fault diagnosis program implements the steps of the multi-granularity dynamic system fault diagnosis method according to any of the above embodiments.
Corresponding to the method in fig. 1, an embodiment of the present invention further provides a multi-granularity-metric dynamic system fault diagnosis system, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the multi-granularity-metric dynamic system fault diagnosis method according to any one of the above embodiments.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is a control center of the multi-granularity dynamic system fault diagnosis system, and various interfaces and lines are utilized to connect various parts of the whole multi-granularity dynamic system fault diagnosis system operable device.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the multi-granularity metric dynamic system fault diagnosis system by running or executing the computer programs and/or modules stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.

Claims (9)

1. A multi-granularity metric dynamic system fault diagnosis method, comprising the steps of:
acquiring a first state quantity acquired in advance, and determining a training value according to the first state quantity; the first state quantity is acquired by a sensor when a permanent magnet synchronous motor system normally operates;
in the running process of the permanent magnet synchronous motor system, the sensor is used for collecting the permanent magnet synchronous motor system in real time to obtain a second state quantity, and the second state quantity is used as a test value;
performing multi-granularity measurement according to the training value and the test value to obtain a plurality of measurement values; each metric value is used for representing the variation of the permanent magnet synchronous motor system;
and carrying out fault diagnosis on the permanent magnet synchronous motor system according to the plurality of measurement values.
2. The method of claim 1, wherein the performing the multi-granularity metric based on the training values and the testing values to obtain a plurality of metric values comprises at least two of:
performing Euclidean measurement on the training value and the test value, and calculating to obtain a first measurement value;
calculating the relative change rate of the training value and the test value to obtain a second metric value;
and performing v-gap measurement on the training value and the test value, and calculating to obtain a third measurement value.
3. The method of claim 2, wherein the first metric is calculated by the following formula:
d (test value, training value) ═ test value-training value;
wherein d (test value, training value) is the first metric.
4. The method of claim 2, wherein the second metric is calculated by the following formula:
Figure FDA0003128381740000011
the relative rate of change in the equation is the second metric.
5. The method of claim 2, wherein the third metric value is calculated by the following formula:
Figure FDA0003128381740000012
wherein, deltavThe (test value, training value) is the third metric value.
6. The multi-granularity-metric dynamic system fault diagnosis method according to claim 2, wherein the fault diagnosis of the permanent magnet synchronous motor system according to the multiple metric values comprises:
respectively determining a threshold value corresponding to each metric value;
if the multiple measurement values are all smaller than the corresponding threshold values, the state of the permanent magnet synchronous motor system is judged to be normal, and otherwise, the state of the permanent magnet synchronous motor system is judged to be a fault.
7. The method of claim 6, further comprising:
respectively establishing a coordinate system corresponding to each measurement value, drawing the corresponding measurement value in each coordinate system in real time, and displaying a threshold value corresponding to the measurement value to obtain a multi-granularity measurement coordinate graph updated in real time;
and presenting the real-time updated multi-granularity measurement coordinate graph.
8. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the multi-granularity metric dynamic system fault diagnosis method according to any one of claims 1 to 7.
9. A multi-granularity metric dynamic system fault diagnosis system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the multi-granular metric dynamic system fault diagnosis method of any of claims 1 to 7.
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