CN114966402B - Fault diagnosis system for switched reluctance motor - Google Patents
Fault diagnosis system for switched reluctance motor Download PDFInfo
- Publication number
- CN114966402B CN114966402B CN202210894566.8A CN202210894566A CN114966402B CN 114966402 B CN114966402 B CN 114966402B CN 202210894566 A CN202210894566 A CN 202210894566A CN 114966402 B CN114966402 B CN 114966402B
- Authority
- CN
- China
- Prior art keywords
- analysis object
- analysis
- fault
- normal
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
Abstract
The invention discloses a switched reluctance motor fault diagnosis system, which relates to the technical field of motor fault diagnosis and comprises a server, wherein the server is in communication connection with a motor data acquisition unit, an operation state identification unit, a real-time trend analysis unit and a fault diagnosis analysis unit; the motor operation is analyzed, the data of the motor is acquired through analysis, and the motor influence data is judged through normal operation and abnormal operation of the motor, so that the accuracy of motor fault diagnosis is improved, the operation efficiency of the motor can be enhanced, and the stability of the motor in the operation process is improved; the running state of the analysis object is analyzed, whether the current running state of the analysis object is qualified or not is judged, so that the real-time running efficiency of the analysis object is improved, the running efficiency of the analysis object is ensured, the situation that the running efficiency is reduced due to abnormal running to influence the real-time working progress of the analysis object is prevented, and the use quality of a user is improved.
Description
Technical Field
The invention relates to the technical field of motor fault diagnosis, in particular to a fault diagnosis system for a switched reluctance motor.
Background
The switched reluctance motor is a novel speed regulating motor, and is a latest generation speed regulating system of a relay variable frequency speed regulating system and a brushless direct current motor speed regulating system. Its simple structure is firm, and the speed governing scope is wide, and system reliability is high. The complete system mainly comprises a motor entity, a power converter, a controller, a position detector and the like. The controller contains a power converter and control circuitry, and a rotor position detector is mounted at one end of the motor.
However, in the prior art, the fault diagnosis accuracy of the switched reluctance motor is low, the operating efficiency of the switched reluctance motor can not be monitored while the fault diagnosis of the switched reluctance motor is carried out, and the condition that the equipment operation is affected due to the fault of the switched reluctance motor is prevented.
In view of the above technical drawbacks, a solution is proposed.
Disclosure of Invention
The invention aims to solve the problems, and provides a fault diagnosis system for a switched reluctance motor, which analyzes the operation of the motor, acquires data of the motor through analysis, and judges the influence data of the motor through normal operation and abnormal operation of the motor, thereby improving the accuracy of fault diagnosis of the motor, enhancing the operation efficiency of the motor and improving the stability of the motor in the operation process; the running state of the analysis object is analyzed, whether the current running state of the analysis object is qualified or not is judged, so that the real-time running efficiency of the analysis object is improved, the running efficiency of the analysis object is ensured, the situation that the running efficiency is reduced due to abnormal running to influence the real-time working progress of the analysis object is prevented, and the use quality of a user is improved.
The purpose of the invention can be realized by the following technical scheme:
the utility model provides a switched reluctance motor fault diagnosis system, includes the server, and the server communication is connected with:
the motor data acquisition unit is used for analyzing the operation of the motor, acquiring data of the motor through analysis, judging motor influence data through normal operation and abnormal operation of the motor, marking the motor as an analysis object, acquiring the operation time period of the analysis object, acquiring a fault time point and a normal time point of the analysis object according to the operation time period, acquiring operation parameters of the analysis object at the fault time point and the normal time point, acquiring preset influence parameters, analyzing the preset influence parameters, dividing the preset influence parameters into selected influence parameters and unselected influence parameters through analysis, and sending the selected influence parameters and the unselected influence parameters to the server;
the running state identification unit is used for analyzing the running state of the analysis object, judging whether the current running state of the analysis object is qualified or not, acquiring a running state analysis coefficient of the analysis object through analysis, comparing the running state analysis coefficient of the analysis object to generate a running state abnormal signal and a running state normal signal, and sending the running state abnormal signal and the running state normal signal to the server;
the real-time trend analysis unit is used for carrying out real-time trend analysis on the analysis object, judging the running trend of the current analysis object, generating a fault trend signal and a normal trend signal through analysis, and sending the fault trend signal and the normal trend signal to the server;
and the fault diagnosis and analysis unit is used for carrying out fault diagnosis and analysis on the corresponding analysis object, judging whether the current analysis object has an operation fault, generating a stator fault signal, a stator normal signal, an air gap eccentric fault signal and an air gap eccentric normal signal through analysis, and sending the signals to the server.
As a preferred embodiment of the present invention, the operation process of the motor data acquisition unit is as follows:
collecting operation parameters of an analysis object at a fault time point and a normal time point, wherein the operation parameters are expressed as the temperature, the voltage, the current and the vibration frequency of the analysis object;
comparing the operation parameters of the fault time point with the operation parameters of the normal time point, and if the corresponding numerical deviation between the operation parameters of the fault time point and the operation parameters of the normal time point exceeds a numerical deviation threshold, marking the corresponding operation parameters as preset influence parameters; if the numerical deviation of the operating parameters of the fault time point and the corresponding numerical deviation of the operating parameters of the normal time point does not exceed the numerical deviation threshold, marking the corresponding operating parameters as the non-influence parameters;
analyzing the preset influence parameters of the analysis object, acquiring the frequency of fluctuation of the corresponding preset influence parameter values when the analysis object breaks down and the frequency of fault generation caused by fluctuation of the corresponding preset influence parameter values of the analysis object, and comparing the frequency of fluctuation of the corresponding preset influence parameter values when the analysis object breaks down and the frequency of fault generation caused by fluctuation of the corresponding preset influence parameter values of the analysis object with a numerical value fluctuation frequency threshold and a fault generation frequency threshold respectively:
if the frequency of numerical value floating of the corresponding preset influence parameter exceeds a numerical value floating frequency threshold value when the analysis object has a fault, or the frequency of fault generation caused by numerical value floating of the corresponding preset influence parameter exceeds a fault generation frequency threshold value, marking the corresponding preset influence parameter as a selected influence parameter, and sending the selected influence parameter to a server; if the frequency of the corresponding preset influence parameter value floating does not exceed the value floating frequency threshold when the analysis object has a fault, and the frequency of the fault generation caused by the fact that the corresponding preset influence parameter value floating corresponds to the analysis object does not exceed the fault generation frequency threshold, marking the corresponding preset influence parameter as a non-selected influence parameter, and sending the non-selected influence parameter to the server.
As a preferred embodiment of the present invention, the operation process of the operation state identification unit is as follows:
acquiring the maximum numerical value floating span of the selected influence parameters corresponding to the analysis object, the numerical value recovery time consumption after the selected influence parameters float, the type number of parameters caused by the real-time periphery of the selected influence parameters corresponding to the analysis object, and acquiring the running state analysis coefficient of the analysis object through analysis; comparing the running state analysis coefficient of the analysis object with a running state analysis coefficient threshold value:
if the running state analysis coefficient of the analysis object exceeds the running state analysis coefficient threshold, judging that the running state analysis of the analysis object is unqualified, generating a running state abnormal signal and sending the running state abnormal signal to a server; after receiving the running state abnormal signal, the server performs rectification on the corresponding analysis object;
and if the running state analysis coefficient of the analysis object does not exceed the running state analysis coefficient threshold, judging that the running state analysis of the analysis object is qualified, generating a running state normal signal and sending the running state normal signal to the server.
As a preferred embodiment of the present invention, the operation process of the real-time trend analysis unit is as follows:
acquiring the floating span increasing speed of the analysis object corresponding to the selected influence parameter and the success rate of recovering to the normal range after the corresponding selected influence parameter floats, and comparing the floating span increasing speed of the analysis object corresponding to the selected influence parameter and the success rate of recovering to the normal range after the corresponding selected influence parameter floats with a span increasing speed threshold value and a recovery success rate threshold value respectively:
if the floating span growth speed of the corresponding selected influence parameter of the analysis object exceeds a span growth speed threshold value, or the success rate of the corresponding selected influence parameter which is recovered to a normal range after floating does not exceed a recovery success rate threshold value, judging that the real-time trend analysis of the analysis object is abnormal, generating a fault trend signal and sending the fault trend signal to a server; if the floating span growth speed of the analysis object corresponding to the selected influence parameter does not exceed the span growth speed threshold value, and the success rate of recovering to the normal range after the corresponding selected influence parameter floats exceeds the recovery success rate threshold value, the real-time trend analysis of the analysis object is judged to be normal, a normal trend signal is generated, and the normal trend signal is sent to the server.
In a preferred embodiment of the present invention, the operation of the failure diagnosis and analysis unit is as follows:
acquiring local temperature values between adjacent windings in a coil of an analysis object by using insulated current values between the adjacent windings in the coil, and comparing the local temperature values between the adjacent windings in the coil of the analysis object by using insulated current values between the adjacent windings in the coil with a local temperature value threshold and an insulated current value threshold respectively:
if the local temperature value between adjacent windings in the coil of the analysis object exceeds the local temperature value threshold value, or the insulated current value between the adjacent windings in the coil exceeds the insulated current value threshold value, judging the stator fault of the analysis object, generating a stator fault signal and sending the stator fault signal to a server;
if the local temperature value between adjacent windings in the coil of the analysis object does not exceed the local temperature value threshold value and the insulated current value between the adjacent windings in the coil does not exceed the insulated current value threshold value, judging that the stator of the analysis object is normal, generating a normal stator signal and sending the normal stator signal to a server;
the method comprises the following steps of collecting the ovality of the inner diameter of the stator core in the analysis object and the rotation frequency of the inner rotor in the analysis object, and comparing the ovality of the inner diameter of the stator core in the analysis object and the rotation frequency of the inner rotor in the analysis object with an ovality threshold range and a rotation frequency threshold range respectively:
if the ellipticity of the inner diameter of the stator core in the analysis object is not in the ellipticity threshold range or the rotating frequency of the inner rotor in the analysis object is not in the rotating frequency threshold range, judging the air gap eccentricity fault of the analysis object, generating an air gap eccentricity fault signal and sending the air gap eccentricity fault signal to a server; if the ellipticity of the inner diameter of the stator core in the analysis object is in the ellipticity threshold range and the rotating frequency of the inner rotor in the analysis object is in the rotating frequency threshold range, judging that the air gap eccentricity of the analysis object is normal, generating an air gap eccentricity normal signal and sending the air gap eccentricity normal signal to a server.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the operation of the motor is analyzed, the data of the motor is acquired through analysis, and the influence data of the motor is judged through the normal operation and the abnormal operation of the motor, so that the accuracy of motor fault diagnosis is improved, the operation efficiency of the motor can be enhanced, and the stability of the motor in the operation process is improved; analyzing the running state of the analysis object, and judging whether the current running state of the analysis object is qualified or not, so that the real-time running efficiency of the analysis object is improved, the running efficiency of the analysis object is ensured, the reduction of the running efficiency caused by abnormal running is prevented from influencing the real-time working progress of the analysis object, and the use quality of a user is improved; the analysis object is subjected to real-time trend analysis, and the operation trend of the current analysis object is judged, so that the operation efficiency of the analysis object is accurately detected, the working efficiency of the analysis object is improved, and the problem that the analysis object is in a failure trend and cannot be maintained in time is avoided, and the abrasion degree of the analysis object equipment is increased; and carrying out fault diagnosis and analysis on the corresponding analysis object, and judging whether the current analysis object has an operation fault or not, so that the operation qualification rate of the analysis object is improved, the fault of the analysis object is found in time, and the influence caused by the fault of the analysis object is reduced.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of the present invention as a whole;
FIG. 2 is a flow chart of a method of the motor data acquisition unit of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a switched reluctance motor fault diagnosis system includes a server, the server is in communication connection with a motor data acquisition unit, an operating state identification unit, a real-time trend analysis unit and a fault diagnosis analysis unit, wherein the server is in bidirectional communication connection with the motor data acquisition unit, the operating state identification unit, the real-time trend analysis unit and the fault diagnosis analysis unit;
referring to fig. 2, a server generates a motor data acquisition signal and sends the motor data acquisition signal to a motor data acquisition unit, the motor data acquisition unit receives the motor data acquisition signal and analyzes the operation of a motor, the motor data acquisition unit performs data acquisition through analysis, and influence data of the motor are judged through normal operation and abnormal operation of the motor, so that the accuracy of motor fault diagnosis is improved, meanwhile, the operation efficiency of the motor can be enhanced, and the stability of the motor in the operation process is improved;
marking the motor as an analysis object, setting a mark i as a natural number more than 1, acquiring an operation time period of the analysis object, acquiring a fault time point and a normal time point of the analysis object according to the operation time period, and acquiring operation parameters of the analysis object at the fault time point and the normal time point, wherein the operation parameters are expressed as parameters such as temperature, voltage, current and vibration frequency of the analysis object;
comparing the operation parameters of the fault time point with the operation parameters of the normal time point, and if the corresponding numerical deviation of the operation parameters of the fault time point and the operation parameters of the normal time point exceeds a numerical deviation threshold, marking the corresponding operation parameters as preset influence parameters; if the numerical deviation of the operating parameters of the fault time point and the corresponding numerical deviation of the operating parameters of the normal time point does not exceed the numerical deviation threshold, marking the corresponding operating parameters as the non-influence parameters;
analyzing the preset influence parameters of the analysis object, acquiring the frequency of fluctuation of the corresponding preset influence parameter values when the analysis object breaks down and the frequency of fault generation caused by fluctuation of the corresponding preset influence parameter values of the analysis object, and comparing the frequency of fluctuation of the corresponding preset influence parameter values when the analysis object breaks down and the frequency of fault generation caused by fluctuation of the corresponding preset influence parameter values of the analysis object with a numerical value fluctuation frequency threshold and a fault generation frequency threshold respectively:
if the frequency of the numerical value floating of the corresponding preset influence parameter exceeds a numerical value floating frequency threshold value when the analysis object has a fault, or the frequency of the fault generation caused by the numerical value floating of the corresponding preset influence parameter exceeds a fault generation frequency threshold value, marking the corresponding preset influence parameter as a selected influence parameter, and sending the selected influence parameter to a server;
if the frequency of the numerical value floating of the corresponding preset influence parameter does not exceed the numerical value floating frequency threshold value when the analysis object has a fault, and the frequency of the fault generation caused by the numerical value floating of the corresponding preset influence parameter of the analysis object does not exceed the fault generation frequency threshold value, marking the corresponding preset influence parameter as a non-selected influence parameter, and sending the non-selected influence parameter to the server;
the server generates an operation state identification signal and sends the operation state identification signal to the operation state identification unit, and the operation state identification unit analyzes the operation state of the analysis object after receiving the operation state identification signal and judges whether the current operation state of the analysis object is qualified or not, so that the real-time operation efficiency of the analysis object is improved, the operation efficiency of the analysis object is ensured, the reduction of the operation efficiency caused by abnormal operation is prevented from influencing the real-time working progress of the analysis object, and the use quality of a user is improved;
acquiring the maximum numerical floating span of the selected influence parameters corresponding to the analysis object and the numerical recovery time after the selected influence parameters float, and respectively marking the maximum numerical floating span of the selected influence parameters corresponding to the analysis object and the numerical recovery time after the selected influence parameters float as KDi and HSi; collecting the type number of the real-time peripheral induced parameters of the selected influence parameters corresponding to the analysis object, and marking the type number of the real-time peripheral induced parameters of the selected influence parameters corresponding to the analysis object as SLi; wherein the parameters causing the parameters are expressed as parameters causing numerical value floating of the selected influencing parameters;
by the formula
Acquiring a running state analysis coefficient Ci of an analysis object, wherein s1, s2 and s3 are all preset proportionality coefficients, and s1 is greater than s2 and s3 is greater than 0;
comparing the running state analysis coefficient Ci of the analysis object with a running state analysis coefficient threshold value:
if the running state analysis coefficient Ci of the analysis object exceeds the running state analysis coefficient threshold, judging that the running state analysis of the analysis object is unqualified, generating a running state abnormal signal and sending the running state abnormal signal to a server; after receiving the running state abnormal signal, the server performs rectification on the corresponding analysis object;
if the running state analysis coefficient Ci of the analysis object does not exceed the running state analysis coefficient threshold, judging that the running state analysis of the analysis object is qualified, generating a running state normal signal and sending the running state normal signal to the server;
the server generates a real-time trend analysis signal and sends the real-time trend analysis signal to the real-time trend analysis unit, and the real-time trend analysis unit carries out real-time trend analysis on an analysis object after receiving the real-time trend analysis signal and judges the operation trend of the current analysis object, so that the operation efficiency of the analysis object is accurately detected, the work efficiency of the analysis object is improved, and the analysis object is prevented from being in a fault trend and cannot be maintained in time, and the abrasion degree of analysis object equipment is increased;
acquiring the floating span increasing speed of the analysis object corresponding to the selected influence parameter and the success rate of recovering to the normal range after the corresponding selected influence parameter floats, and comparing the floating span increasing speed of the analysis object corresponding to the selected influence parameter and the success rate of recovering to the normal range after the corresponding selected influence parameter floats with a span increasing speed threshold value and a recovery success rate threshold value respectively:
if the floating span growth speed of the corresponding selected influence parameter of the analysis object exceeds a span growth speed threshold value, or the success rate of the corresponding selected influence parameter which is recovered to a normal range after floating does not exceed a recovery success rate threshold value, judging that the real-time trend analysis of the analysis object is abnormal, generating a fault trend signal and sending the fault trend signal to a server;
if the floating span growth speed of the corresponding selected influence parameter of the analysis object does not exceed the span growth speed threshold value, and the success rate of recovering to a normal range after the corresponding selected influence parameter floats exceeds the recovery success rate threshold value, judging that the real-time trend analysis of the analysis object is normal, generating a normal trend signal and sending the normal trend signal to a server;
the server generates a fault diagnosis analysis signal and sends the fault diagnosis analysis signal to the fault diagnosis analysis unit, and the fault diagnosis analysis unit carries out fault diagnosis analysis on a corresponding analysis object after receiving the fault diagnosis analysis signal and judges whether an operation fault exists in the current analysis object, so that the operation qualification rate of the analysis object is improved, the fault existing in the analysis object is found in time, and the influence caused by the fault of the analysis object is reduced;
acquiring local temperature values between adjacent windings in a coil of an analysis object and insulated current values between the adjacent windings in the coil, and comparing the local temperature values between the adjacent windings in the coil of the analysis object and the insulated current values between the adjacent windings in the coil with a local temperature value threshold and an insulated current value threshold respectively:
if the local temperature value between adjacent windings in the coil of the analysis object exceeds the local temperature value threshold value, or the current value after insulation between the adjacent windings in the coil exceeds the current value threshold value after insulation, the stator fault of the analysis object is judged, a stator fault signal is generated, and the stator fault signal is sent to a server;
if the local temperature value between adjacent windings in the coil of the analysis object does not exceed the local temperature value threshold value and the insulated current value between the adjacent windings in the coil does not exceed the insulated current value threshold value, judging that the stator of the analysis object is normal, generating a normal stator signal and sending the normal stator signal to a server;
the method comprises the following steps of collecting the ovality of the inner diameter of the stator core in the analysis object and the rotation frequency of the inner rotor in the analysis object, and comparing the ovality of the inner diameter of the stator core in the analysis object and the rotation frequency of the inner rotor in the analysis object with an ovality threshold range and a rotation frequency threshold range respectively:
if the ellipticity of the inner diameter of the stator core in the analysis object is not in the ellipticity threshold range or the rotating frequency of the inner rotor in the analysis object is not in the rotating frequency threshold range, judging the air gap eccentric fault of the analysis object, generating an air gap eccentric fault signal and sending the air gap eccentric fault signal to a server;
if the ellipticity of the inner diameter of the stator core in the analysis object is within the ellipticity threshold range and the rotation frequency of the inner rotor in the analysis object is within the rotation frequency threshold range, judging that the air gap eccentricity of the analysis object is normal, generating an air gap eccentricity normal signal and sending the air gap eccentricity normal signal to a server.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
when the motor analysis system is used, the motor operation is analyzed through the motor data acquisition unit, the motor is subjected to data acquisition through analysis, motor influence data are judged through normal operation and abnormal operation of the motor, the motor is marked as an analysis object, the operation time period of the analysis object is obtained, the fault time point and the normal time point of the analysis object are obtained according to the operation time period, the operation parameters of the analysis object at the fault time point and the normal time point are collected, the preset influence parameters are obtained and analyzed, the preset influence parameters are divided into selected influence parameters and unselected influence parameters through analysis, and the selected influence parameters and the unselected influence parameters are sent to the server; analyzing the running state of the analysis object through a running state identification unit, judging whether the current running state of the analysis object is qualified, acquiring a running state analysis coefficient of the analysis object through analysis, comparing the running state analysis coefficient of the analysis object to generate a running state abnormal signal and a running state normal signal, and sending the running state abnormal signal and the running state normal signal to a server; the real-time trend analysis unit is used for carrying out real-time trend analysis on the analysis object, judging the operation trend of the current analysis object, generating a fault trend signal and a normal trend signal through analysis, and sending the fault trend signal and the normal trend signal to the server; and performing fault diagnosis and analysis on the corresponding analysis object through a fault diagnosis and analysis unit, judging whether the current analysis object has an operation fault, generating a stator fault signal, a stator normal signal, an air gap eccentric fault signal and an air gap eccentric normal signal through analysis, and sending the signals to a server.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (3)
1. The utility model provides a switched reluctance motor fault diagnosis system which characterized in that, includes the server, and the server communication is connected with:
the motor data acquisition unit is used for analyzing the operation of the motor, acquiring data of the motor through analysis, judging motor influence data through normal operation and abnormal operation of the motor, marking the motor as an analysis object, acquiring the operation time period of the analysis object, acquiring a fault time point and a normal time point of the analysis object according to the operation time period, acquiring operation parameters of the analysis object at the fault time point and the normal time point, acquiring preset influence parameters, analyzing the preset influence parameters, dividing the preset influence parameters into selected influence parameters and unselected influence parameters through analysis, and sending the selected influence parameters and the unselected influence parameters to the server;
the running state identification unit is used for analyzing the running state of the analysis object, judging whether the current running state of the analysis object is qualified or not, acquiring a running state analysis coefficient of the analysis object through analysis, comparing the running state analysis coefficient of the analysis object to generate a running state abnormal signal and a running state normal signal, and sending the running state abnormal signal and the running state normal signal to the server;
the real-time trend analysis unit is used for carrying out real-time trend analysis on the analysis object, judging the running trend of the current analysis object, generating a fault trend signal and a normal trend signal through analysis, and sending the fault trend signal and the normal trend signal to the server;
the fault diagnosis and analysis unit is used for carrying out fault diagnosis and analysis on the corresponding analysis object, judging whether the current analysis object has an operation fault, generating a stator fault signal, a stator normal signal, an air gap eccentric fault signal and an air gap eccentric normal signal through analysis, and sending the signals to the server;
the operation process of the motor data acquisition unit is as follows:
collecting operation parameters of an analysis object at a fault time point and a normal time point, wherein the operation parameters are expressed as the temperature, the voltage, the current and the vibration frequency of the analysis object;
comparing the operation parameters of the fault time point with the operation parameters of the normal time point, and if the corresponding numerical deviation of the operation parameters of the fault time point and the operation parameters of the normal time point exceeds a numerical deviation threshold, marking the corresponding operation parameters as preset influence parameters; if the corresponding numerical deviation of the operating parameters of the fault time point and the operating parameters of the normal time point does not exceed the numerical deviation threshold, marking the corresponding operating parameters as non-influence parameters;
analyzing the preset influence parameters of the analysis object, acquiring the frequency of fluctuation of the corresponding preset influence parameter values when the analysis object breaks down and the frequency of fault generation caused by fluctuation of the corresponding preset influence parameter values of the analysis object, and comparing the frequency of fluctuation of the corresponding preset influence parameter values when the analysis object breaks down and the frequency of fault generation caused by fluctuation of the corresponding preset influence parameter values of the analysis object with a numerical value fluctuation frequency threshold and a fault generation frequency threshold respectively:
if the frequency of numerical value floating of the corresponding preset influence parameter exceeds a numerical value floating frequency threshold value when the analysis object has a fault, or the frequency of fault generation caused by numerical value floating of the corresponding preset influence parameter exceeds a fault generation frequency threshold value, marking the corresponding preset influence parameter as a selected influence parameter, and sending the selected influence parameter to a server; if the frequency of the corresponding preset influence parameter value fluctuation does not exceed the value fluctuation frequency threshold when the analysis object has a fault, and the frequency of the fault generation caused by the corresponding preset influence parameter value fluctuation does not exceed the fault generation frequency threshold, marking the corresponding preset influence parameter as a non-selected influence parameter, and sending the non-selected influence parameter to a server;
the operation process of the operation state identification unit is as follows:
acquiring the maximum numerical value floating span of the analysis object corresponding to the selected influence parameter, the numerical value recovery time consumption after the corresponding selected influence parameter floats, the number of types of parameters caused by the real-time periphery of the analysis object corresponding to the selected influence parameter, and acquiring the running state analysis coefficient of the analysis object through analysis; comparing the running state analysis coefficient of the analysis object with a running state analysis coefficient threshold value:
if the running state analysis coefficient of the analysis object exceeds the running state analysis coefficient threshold, judging that the running state analysis of the analysis object is unqualified, generating a running state abnormal signal and sending the running state abnormal signal to a server; after receiving the running state abnormal signal, the server performs rectification on the corresponding analysis object;
and if the running state analysis coefficient of the analysis object does not exceed the running state analysis coefficient threshold, judging that the running state analysis of the analysis object is qualified, generating a running state normal signal and sending the running state normal signal to the server.
2. The system for diagnosing the fault of the switched reluctance motor according to claim 1, wherein the real-time trend analysis unit operates as follows:
acquiring the floating span increasing speed of the analysis object corresponding to the selected influence parameter and the success rate of recovering to the normal range after the corresponding selected influence parameter floats, and comparing the floating span increasing speed of the analysis object corresponding to the selected influence parameter and the success rate of recovering to the normal range after the corresponding selected influence parameter floats with a span increasing speed threshold value and a recovery success rate threshold value respectively:
if the floating span growth speed of the analysis object corresponding to the selected influence parameter exceeds a span growth speed threshold value, or the success rate of the analysis object which is recovered to a normal range after corresponding to the selected influence parameter floats does not exceed a recovery success rate threshold value, judging that the real-time trend analysis of the analysis object is abnormal, generating a fault trend signal and sending the fault trend signal to a server; if the floating span growth speed of the corresponding selected influence parameter of the analysis object does not exceed the span growth speed threshold value, and the success rate of the corresponding selected influence parameter which is recovered to the normal range after floating exceeds the recovery success rate threshold value, the real-time trend analysis of the analysis object is judged to be normal, a normal trend signal is generated, and the normal trend signal is sent to the server.
3. The switched reluctance motor fault diagnosis system according to claim 1, wherein the operation of the fault diagnosis analysis unit is as follows:
acquiring local temperature values between adjacent windings in a coil of an analysis object and insulated current values between the adjacent windings in the coil, and comparing the local temperature values between the adjacent windings in the coil of the analysis object and the insulated current values between the adjacent windings in the coil with a local temperature value threshold and an insulated current value threshold respectively:
if the local temperature value between adjacent windings in the coil of the analysis object exceeds the local temperature value threshold value, or the current value after insulation between the adjacent windings in the coil exceeds the current value threshold value after insulation, the stator fault of the analysis object is judged, a stator fault signal is generated, and the stator fault signal is sent to a server;
if the local temperature value between adjacent windings in the coil of the analysis object does not exceed the local temperature value threshold value, and the insulated current value between the adjacent windings in the coil does not exceed the insulated current value threshold value, judging that the stator of the analysis object is normal, generating a normal stator signal and sending the normal stator signal to a server;
the method comprises the following steps of collecting the ovality of the inner diameter of the stator core in the analysis object and the rotation frequency of the inner rotor in the analysis object, and comparing the ovality of the inner diameter of the stator core in the analysis object and the rotation frequency of the inner rotor in the analysis object with an ovality threshold range and a rotation frequency threshold range respectively:
if the ellipticity of the inner diameter of the stator core in the analysis object is not in the ellipticity threshold range or the rotating frequency of the inner rotor in the analysis object is not in the rotating frequency threshold range, judging the air gap eccentric fault of the analysis object, generating an air gap eccentric fault signal and sending the air gap eccentric fault signal to a server; if the ellipticity of the inner diameter of the stator core in the analysis object is within the ellipticity threshold range and the rotation frequency of the inner rotor in the analysis object is within the rotation frequency threshold range, judging that the air gap eccentricity of the analysis object is normal, generating an air gap eccentricity normal signal and sending the air gap eccentricity normal signal to a server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210894566.8A CN114966402B (en) | 2022-07-28 | 2022-07-28 | Fault diagnosis system for switched reluctance motor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210894566.8A CN114966402B (en) | 2022-07-28 | 2022-07-28 | Fault diagnosis system for switched reluctance motor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114966402A CN114966402A (en) | 2022-08-30 |
| CN114966402B true CN114966402B (en) | 2022-11-01 |
Family
ID=82968877
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210894566.8A Active CN114966402B (en) | 2022-07-28 | 2022-07-28 | Fault diagnosis system for switched reluctance motor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114966402B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115130702B (en) * | 2022-09-02 | 2022-12-02 | 山东汇泓纺织科技有限公司 | Textile machine fault prediction system based on big data analysis |
| CN115356921B (en) * | 2022-10-19 | 2023-01-17 | 上海泛腾半导体技术有限公司 | Voltage closed-loop control system for linear driver |
| CN115514292B (en) * | 2022-10-21 | 2023-05-05 | 江门市力丰电机有限公司 | Servo motor rotating speed control system and method |
| CN115423134B (en) * | 2022-11-04 | 2023-02-28 | 淄博睿智通机电科技有限公司 | Heavy film inflation film manufacturing machine operation detecting system based on artificial intelligence |
| CN115616404B (en) * | 2022-12-19 | 2023-04-18 | 山东协和学院 | Servo motor test system for industrial robot |
| CN116141976B (en) * | 2023-04-24 | 2023-06-30 | 江西科技学院 | Electric automobile stability monitoring management and control system based on in-wheel motor drive |
| CN117419800B (en) * | 2023-10-19 | 2024-04-09 | 安徽润安思变能源技术有限公司 | Noise vibration test system of gas suspension centrifugal refrigeration compressor |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6014598A (en) * | 1996-06-28 | 2000-01-11 | Arcelik A.S. | Model-based fault detection system for electric motors |
| JP2001326777A (en) * | 2000-05-17 | 2001-11-22 | Mitsubishi Heavy Ind Ltd | Failure diagnosis system for industrial device |
| CN105978429A (en) * | 2016-03-28 | 2016-09-28 | 上海交通大学 | Switched reluctance motor monitoring system and method |
| CN110177017A (en) * | 2019-06-04 | 2019-08-27 | 沃德(天津)智能技术有限公司 | A kind of speed reducer intelligent Fault Diagnose Systems and its diagnostic method |
| CN111381161A (en) * | 2018-12-29 | 2020-07-07 | 中国葛洲坝集团装备工业有限公司 | Remote online health state diagnosis method for generator set |
| CN111509847A (en) * | 2020-04-17 | 2020-08-07 | 贵州电网有限责任公司 | Intelligent detection system and method for power grid unit state |
| CN112232370A (en) * | 2020-09-16 | 2021-01-15 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Fault analysis and prediction method for engine |
| CN114781669A (en) * | 2022-04-19 | 2022-07-22 | 淮北市华明工业变频设备有限公司 | Numerical control management system of high-efficient modularization IGBT smelting power |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3306284A1 (en) * | 2016-10-10 | 2018-04-11 | Rolls-Royce plc | A method and apparatus for diagnosing a fault condition in an electric machine |
| KR102092185B1 (en) * | 2019-10-07 | 2020-05-26 | 팩트얼라이언스 주식회사 | Platform for analyzing electric motor health and analysis method using the same |
| KR102340395B1 (en) * | 2020-01-02 | 2021-12-15 | 두산중공업 주식회사 | Apparatus for diagnosing plant failure and method therefor |
-
2022
- 2022-07-28 CN CN202210894566.8A patent/CN114966402B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6014598A (en) * | 1996-06-28 | 2000-01-11 | Arcelik A.S. | Model-based fault detection system for electric motors |
| JP2001326777A (en) * | 2000-05-17 | 2001-11-22 | Mitsubishi Heavy Ind Ltd | Failure diagnosis system for industrial device |
| CN105978429A (en) * | 2016-03-28 | 2016-09-28 | 上海交通大学 | Switched reluctance motor monitoring system and method |
| CN111381161A (en) * | 2018-12-29 | 2020-07-07 | 中国葛洲坝集团装备工业有限公司 | Remote online health state diagnosis method for generator set |
| CN110177017A (en) * | 2019-06-04 | 2019-08-27 | 沃德(天津)智能技术有限公司 | A kind of speed reducer intelligent Fault Diagnose Systems and its diagnostic method |
| CN111509847A (en) * | 2020-04-17 | 2020-08-07 | 贵州电网有限责任公司 | Intelligent detection system and method for power grid unit state |
| CN112232370A (en) * | 2020-09-16 | 2021-01-15 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Fault analysis and prediction method for engine |
| CN114781669A (en) * | 2022-04-19 | 2022-07-22 | 淮北市华明工业变频设备有限公司 | Numerical control management system of high-efficient modularization IGBT smelting power |
Non-Patent Citations (3)
| Title |
|---|
| Static and Dynamic Vibration Analyses of Switched Reluctance Motors Including Bearings， Housing，Rotor Dynamics， and Applied Loads;K. N. Srinivas et al;《IEEE TRANSACTIONS ON MAGNETICS》;20040731;第40卷(第4期);全文 * |
| 基于BP神经网络的微电机故障测试研究;沈勇;《电子测试》;20180430(第4期);全文 * |
| 研究机车牵引电机故障分类及运行维护监测设计;呼明;《运输经理世界》;20200731(第7期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114966402A (en) | 2022-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114966402B (en) | Fault diagnosis system for switched reluctance motor | |
| CN115130702B (en) | Textile machine fault prediction system based on big data analysis | |
| CN107247230A (en) | A kind of electric rotating machine state monitoring method based on SVMs and data-driven | |
| WO2021063294A1 (en) | Rotor center offset detection method and apparatus, storage medium, and device | |
| CN103926506B (en) | Based on the rotor of steam turbo generator Winding Short Fault Diagnosis method of constructor | |
| CN103912448B (en) | A kind of regional wind power power of the assembling unit characteristic monitoring method | |
| JP2003525002A (en) | Shaft voltage and current monitoring system | |
| CN115559890B (en) | Water pump unit operation fault prediction adjustment method and system | |
| CN105372591A (en) | A transient process-based hydroelectric generating set health status quantitative evaluation method | |
| CN104155567B (en) | Positioning method of turn-to-turn short circuit fault of doubly-fed generator rotor | |
| CN114801751A (en) | Automobile battery fault prediction system based on data analysis | |
| CN115102290A (en) | Real-time safety early warning system of smart power grids | |
| CN115061391A (en) | Energy consumption control system for hydrology and water resource remote measuring terminal machine based on data processing | |
| CN114061770B (en) | Distributed prefabricated optical fiber bus temperature measurement system | |
| CN115638975A (en) | Dynamic performance acquisition and analysis system and method for high-speed spindle | |
| US20230167801A1 (en) | Wind turbine generator, and minimum rotational speed control method and device therefor | |
| CN116609606B (en) | Railway moving ring real-time safety detection system based on artificial intelligence | |
| CN115800552A (en) | Intelligent regulation and control system and method for super capacitor operation power frequency modulation | |
| CN112098906B (en) | Winding performance detection system of all-in-one machine | |
| CN115013086A (en) | Remote data acquisition device based on offshore power grid turbine generator | |
| CN113740764A (en) | On-line detection method and device for turn-to-turn short circuit defect of generator rotor winding | |
| CN112034344A (en) | Real-time online diagnosis method for turn-to-turn short circuit fault of rotor winding of hydraulic generator | |
| CN112835326A (en) | Intelligent method and system for processing large-scale casting and forging | |
| CN110967182A (en) | Cone crusher vibration data acquisition and preprocessing method | |
| CN114264956B (en) | Method for identifying initial state of synchronous motor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |