CN111896246A - Health management verifies evaluation system - Google Patents

Health management verifies evaluation system Download PDF

Info

Publication number
CN111896246A
CN111896246A CN202010744068.6A CN202010744068A CN111896246A CN 111896246 A CN111896246 A CN 111896246A CN 202010744068 A CN202010744068 A CN 202010744068A CN 111896246 A CN111896246 A CN 111896246A
Authority
CN
China
Prior art keywords
fault
simulation
fault diagnosis
bearing
prediction
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.)
Pending
Application number
CN202010744068.6A
Other languages
Chinese (zh)
Inventor
李臻
于峰涛
马海龙
王翔
贾洪钢
张建中
朱益军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Tiandi Longyue Technology Co ltd
Original Assignee
Beijing Tiandi Longyue Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Tiandi Longyue Technology Co ltd filed Critical Beijing Tiandi Longyue Technology Co ltd
Priority to CN202010744068.6A priority Critical patent/CN111896246A/en
Publication of CN111896246A publication Critical patent/CN111896246A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/021—Gearings
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/025—Test-benches with rotational drive means and loading means; Load or drive simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/027—Test-benches with force-applying means, e.g. loading of drive shafts along several directions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/028—Acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • 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
    • 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
    • G01R31/346—Testing of armature or field windings

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention provides a health management verification and evaluation system, which comprises: the health management module is used for acquiring operation parameters of the equipment, diagnosing and predicting faults according to the operation parameters and acquiring fault diagnosis and prediction results; and the verification evaluation module is used for verifying the fault diagnosis and prediction result to obtain a verification result. The health management verification and evaluation system verifies the fault diagnosis and prediction result based on the verification and evaluation module, and improves the accuracy and precision of diagnosis and prediction based on the verification result.

Description

Health management verifies evaluation system
Technical Field
The invention relates to the technical field of health management, in particular to a health management verification and evaluation system.
Background
At present, in the engineering application of the PHM system in the coal industry, due to the severe working environment, narrow space, voltage fluctuation, variable load, variable speed, large impact vibration, full-load starting, large moist dust and serious electromagnetic interference of fully-mechanized mining equipment, the PHM system not only has great influence on the reliability and the service life of a transmission system, but also has great influence on signals such as vibration, current and the like. The accuracy and precision of fault diagnosis and residual life prediction cannot be influenced by the interference, and the conventional technical performance indexes of the conventional fault diagnosis and prediction algorithm have high missing judgment rate and false alarm rate, so that the high accuracy and precision are achieved.
Disclosure of Invention
One of the objectives of the present invention is to provide a health management verification and evaluation system, which verifies the failure diagnosis and prediction result based on a verification and evaluation module, and improves the accuracy and precision of diagnosis and prediction based on the verification result.
The health management verification and evaluation system provided by the embodiment of the invention comprises:
the health management module is used for acquiring the operation parameters of the equipment, diagnosing and predicting faults according to the operation parameters and acquiring fault diagnosis and prediction results;
and the verification evaluation module is used for verifying the fault diagnosis and prediction result to obtain a verification result.
Preferably, the operating parameters include: one or more of the load, the running speed, the voltage, the environmental electromagnetic index and the lubrication index of the equipment are combined.
Preferably, the verification evaluation module includes: and the simulation verification test platform is initialized based on the operation parameters, performs simulation operation and realizes verification of the fault diagnosis and prediction results.
Preferably, the verification evaluation module includes: and the sample data verification submodule is used for verifying the fault diagnosis and prediction results according to pre-stored sample data.
Preferably, the simulation verification test platform comprises: the system comprises a variable-frequency speed-regulating back-to-back loading unit, a power transmission unit, a power supply unit, a working condition simulation unit, a fault simulation component, a sensing unit and a data acquisition and analysis unit;
variable Frequency Speed Governing (VFSG) load unit includes back to back: the driving motor variable frequency speed regulation subunit and the generator variable frequency loading subunit;
the power transmission unit includes: the device comprises a coupler, a supporting seat, a torque meter and a speed reducer;
the working condition simulation unit comprises:
the medium injection subunit is used for injecting a medium to simulate the oil quality change of the lubricating oil;
the fault simulation component comprises:
bearing with fault outer ring of bearing, bearing with fault inner ring of bearing, bearing with fault retainer of bearing, bearing with fault roller of bearing, bearing with fault misalignment of shaft, bearing with fault unbalance of shaft, speed reducer with fault loose foundation, speed reducer with fault gear and speed reducer with fault lubricating oil of gear box; a driving motor with a motor turn-to-turn short circuit, a driving motor with a rotor eccentric fault, a driving motor with a current imbalance fault, a driving motor with a rotor broken bar fault, a driving motor with a rotor bending fault, a driving motor with a motor rotor imbalance fault and a driving motor with a motor rotor misalignment fault;
the sensing unit includes:
one or more combinations of a displacement sensor, a vibration sensor, a temperature sensor, an oil quality sensor, a liquid level sensor, a current sensor, a voltage sensor and a rotating speed sensor;
the data acquisition and analysis unit comprises: a PC computer.
Preferably, the operating condition simulation unit further includes:
the vibration simulation subunit is used for outputting vibration working conditions of the vibration simulation equipment;
and the electromagnetic interference subunit is used for outputting electromagnetic harmonic interference working conditions of the electromagnetic interference simulation equipment.
Preferably, the electromagnetic interference subunit includes: an electromagnetic interference pulse generator.
Preferably, the fault diagnosis and prediction is performed according to the operation parameters to obtain a fault diagnosis and prediction result, and the fault diagnosis and prediction method includes:
extracting the characteristics of the operation parameters, and substituting the extracted characteristic values into a pre-established fault diagnosis and prediction model to obtain fault diagnosis and prediction results;
or the like, or, alternatively,
a diagnosis library is constructed according to the operation parameters of the historical records, and a matrix X is formed according to the parameter fault diagnosis of different parameters contained in the diagnosis library and records corresponding to the prediction results, wherein the matrix X is as follows:
Figure BDA0002607739830000031
wherein x isnmThe mth parameter data corresponding to the nth fault diagnosis and prediction result; xnThe nth fault diagnosis and prediction result is obtained;
after the matrix X is constructed, the management module carries out filling processing on parameter data in the X, wherein the filling processing is carried out according to the following formula:
Figure BDA0002607739830000032
wherein x isstThe missing position of the parameter data is the parameter data of the s-th row and t-th column, xs1The non-missing parameter data is the parameter data of the s-th row in the first column, xitThe parameter data which is not missing is the parameter data of the ith row and the tth column;
acquiring the operation parameters of the equipment in real time, and determining a judgment vector A according to the operation parameters, namely:
A＝(a1,a2,…,am)；
wherein, amParameter data for the mth operating parameter;
calculating the similarity between the judgment vector A and the parameter data of each row in the matrix X, wherein the calculation formula is as follows:
Figure BDA0002607739830000033
wherein D isjTo determine the similarity between the vector A and the jth row parameter data of the matrix, xjkParameter data of the jth row and the kth column; a iskParameter data for the kth operating parameter in vector a;
and determining the fault diagnosis and prediction result corresponding to the maximum similarity as the fault diagnosis and prediction result obtained by fault diagnosis and prediction according to the operation parameters.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram of a health management verification and evaluation system according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
An embodiment of the present invention provides a health management verification and evaluation system, as shown in fig. 1, including:
the health management module 1 is used for acquiring operation parameters of equipment, diagnosing and predicting faults according to the operation parameters and acquiring fault diagnosis and prediction results;
and the verification evaluation module 2 is used for verifying the fault diagnosis and prediction result to obtain a verification result.
The working principle and the beneficial effects of the technical scheme are as follows:
firstly, monitoring various operation parameters of the fully mechanized mining equipment through various sensors arranged on the fully mechanized mining equipment, carrying out fault diagnosis on the fully mechanized mining equipment based on the operation parameters, and predicting the residual life of various parts of the fully mechanized mining equipment to obtain fault diagnosis and prediction results; then, the fault diagnosis and prediction result is verified through a verification evaluation module 2 to obtain a verification result; the verification can be performed by adopting a pre-established verification model; and the established simulation verification test platform can also be adopted for verification. The pre-established verification model is a neural network model established based on a large amount of historical monitoring data.
The health management verification and evaluation system verifies the fault diagnosis and prediction result based on the verification and evaluation module 2, and improves the accuracy and precision of diagnosis and prediction based on the verification result.
In one embodiment, the operating parameters include: one or more of the load, the running speed, the voltage, the environmental electromagnetic index and the lubrication index of the equipment are combined.
The working principle and the beneficial effects of the technical scheme are as follows:
the fault diagnosis factors of the fully mechanized mining equipment mainly comprise the load, the running speed, the voltage, the environmental electromagnetic indexes and the lubrication indexes of the equipment; therefore, the parameters of the load, the running speed, the voltage, the environmental electromagnetic index and the lubrication index of the equipment are monitored, the fault of the fully-mechanized mining equipment can be accurately diagnosed, and the residual service life can be accurately predicted.
In one embodiment, the verification evaluation module 2 includes: and the simulation verification test platform is initialized based on the operation parameters, performs simulation operation and realizes verification of the fault diagnosis and prediction results.
The working principle and the beneficial effects of the technical scheme are as follows:
the operation parameters are loaded through a simulation verification test platform established in advance, simulation operation is carried out, the fault diagnosis and prediction results of the health management module 1 are verified based on the operation results, and the accuracy and precision of diagnosis and prediction are improved.
In one embodiment, the verification evaluation module 2 includes: and the sample data verification submodule is used for verifying the fault diagnosis and prediction results according to pre-stored sample data.
The working principle and the beneficial effects of the technical scheme are as follows:
the fault diagnosis and prediction results of the health management module 1 are verified through historical sample data, and the accuracy and precision of diagnosis and prediction are improved.
In one embodiment, the simulation proof test platform comprises: the system comprises a variable-frequency speed-regulating back-to-back loading unit, a power transmission unit, a power supply unit, a working condition simulation unit, a fault simulation component, a sensing unit and a data acquisition and analysis unit;
variable Frequency Speed Governing (VFSG) load unit includes back to back: the driving motor variable frequency speed regulation subunit and the generator variable frequency loading subunit;
the power transmission unit includes: the device comprises a coupler, a supporting seat, a torque meter and a speed reducer;
the working condition simulation unit comprises:
the medium injection subunit is used for injecting a medium to simulate the oil quality change of the lubricating oil;
the fault simulation component comprises:
bearing with fault outer ring of bearing, bearing with fault inner ring of bearing, bearing with fault retainer of bearing, bearing with fault roller of bearing, bearing with fault misalignment of shaft, bearing with fault unbalance of shaft, speed reducer with fault loose foundation, speed reducer with fault gear and speed reducer with fault lubricating oil of gear box; a driving motor with a motor turn-to-turn short circuit, a driving motor with a rotor eccentric fault, a driving motor with a current imbalance fault, a driving motor with a rotor broken bar fault, a driving motor with a rotor bending fault, a driving motor with a motor rotor imbalance fault and a driving motor with a motor rotor misalignment fault;
the sensing unit includes:
one or more combinations of a displacement sensor, a vibration sensor, a temperature sensor, an oil quality sensor, a liquid level sensor, a current sensor, a voltage sensor and a rotating speed sensor;
the data acquisition and analysis unit comprises: a PC computer.
The working principle and the beneficial effects of the technical scheme are as follows:
the embodiment is a main structure of a simulation verification test platform, and the operation parameters are loaded through the simulation verification test platform established in advance to perform simulation operation, and the fault diagnosis and prediction results of the health management module 1 are verified based on the operation results, so that the accuracy and precision of diagnosis and prediction are improved.
The method can be developed through a simulation verification test platform:
developing simulation experiment research of electromechanical faults of a transmission system under a simulated working condition;
because the interference source of the field measured signal is formed by compounding various interferences, the performance of the algorithm can be evaluated only, and the source of the problem cannot be provided, so that the algorithm cannot be effectively improved; therefore, experimental study of simulated working conditions is needed, fault samples or full-life cycle samples obtained by carrying out simulation experiments of single working conditions on various working conditions are used for checking the root cause of poor robustness of the algorithm, and the algorithm is improved; and the comprehensive performance of the algorithm is verified by simulating the composite interference. Therefore, experimental studies of single-condition and compound-condition conditions are required to obtain the required fault samples.
First, fault diagnosis experiment research
The method mainly obtains various electromechanical fault state data samples under different working conditions, including data samples in three states of a normal state, a weak fault state, a fault state and the like. The main experimental research contents are as follows:
b) developing fault injection simulation experiments under three states of universal working conditions and single fault to obtain a fault data standard sample;
c) developing an experimental study of common working conditions and multi-fault simultaneous injection simulation to obtain a multi-fault interference fault sample;
d) carrying out fault injection simulation experiments under three states of single working condition simulation and single fault to obtain a fault data sample under the working condition;
e) developing a fault injection simulation experiment under three states of composite working condition simulation and single fault to obtain a fault data sample under the composite working condition;
f) carrying out experimental research of composite working condition simulation and multi-fault simultaneous injection simulation to obtain a fault data sample under the composite working condition;
through fault injection simulation experiments under the conditions, 6 single working conditions (normal operation under rated conditions and 5 types of single working condition simulation such as variable load, variable speed, voltage fluctuation, electromagnetic interference, lubrication fault and the like) and 16 electromechanical fault state samples under composite working conditions are obtained, and the number of each electromechanical fault sample under each working condition is not less than 10;
and secondly, carrying out accelerated life tests on gears and bearings in the straight gear transmission and the planetary gear transmission under different working conditions to obtain life cycle data samples. The main experimental study contents are as follows:
a) developing a straight gear transmission accelerated life test under a general working condition until a special weak part (gear and bearing) is damaged, and obtaining a full life cycle data sample under the working condition;
b) carrying out a single working condition simulation straight gear transmission accelerated life experiment until a special weak part (gear and bearing) is damaged, and obtaining a working condition full life cycle data sample;
c) developing a composite working condition simulation straight gear transmission accelerated life experiment until a special weak part (gear and bearing) is damaged to obtain a working condition full life cycle data sample;
d) developing a planetary gear transmission accelerated life test under a general working condition until a special weak part (gear and bearing) is damaged, and obtaining a full life cycle data sample under the working condition;
e) carrying out a single working condition simulation planetary gear transmission accelerated life experiment until a special weak part (gear and bearing) is damaged, and obtaining a working condition full life cycle data sample;
f) developing a composite working condition simulation planetary gear transmission accelerated life experiment until a special weak part (gear and bearing) is damaged, and obtaining a working condition full life cycle data sample;
through the accelerated life test under the conditions, 6 single working conditions (normal operation under rated conditions and 5 types of single working condition simulation such as variable load, variable speed, voltage fluctuation, electromagnetic interference, lubrication fault and the like) and 4 types of full life cycle data samples of the straight-tooth transmission bearing and the gear and the planetary transmission bearing and the gear under the composite working condition are not lower than 28 groups.
In one embodiment, the operating condition simulation unit further includes:
the vibration simulation subunit is used for outputting vibration working conditions of the vibration simulation equipment;
and the electromagnetic interference subunit is used for outputting electromagnetic harmonic interference working conditions of the electromagnetic interference simulation equipment.
The working principle and the beneficial effects of the technical scheme are as follows:
the vibration simulation subunit simulates the vibration working condition of the fully mechanized mining equipment, the electromagnetic interference subunit simulates the electromagnetic interference working condition, the simulation degree of the simulation verification test platform is improved, and the accuracy of the verification result is improved.
In one embodiment, the electromagnetic interference subunit includes: an electromagnetic interference pulse generator.
In one embodiment, the fault diagnosis and prediction is performed according to the operation parameters to obtain a fault diagnosis and prediction result, including:
extracting the characteristics of the operation parameters, and substituting the extracted characteristic values into a pre-established fault diagnosis and prediction model to obtain fault diagnosis and prediction results;
or the like, or, alternatively,
a diagnosis library is constructed according to the operation parameters of the historical records, and a matrix X is formed according to the parameter fault diagnosis of different parameters contained in the diagnosis library and records corresponding to the prediction results, wherein the matrix X is as follows:
Figure BDA0002607739830000081
wherein x isnmThe mth parameter data corresponding to the nth fault diagnosis and prediction result; xnThe nth fault diagnosis and prediction result is obtained;
after the matrix X is constructed, the management module carries out filling processing on parameter data in the X, wherein the filling processing is carried out according to the following formula:
Figure BDA0002607739830000091
wherein x isstThe missing position of the parameter data is the parameter data of the s-th row and t-th column, xs1The non-missing parameter data is the parameter data of the s-th row in the first column, xitThe parameter data which is not missing is the parameter data of the ith row and the tth column;
acquiring the operation parameters of the equipment in real time, and determining a judgment vector A according to the operation parameters, namely:
A＝(a1,a2,…,am)；
wherein, amParameter data for the mth operating parameter;
calculating the similarity between the judgment vector A and the parameter data of each row in the matrix X, wherein the calculation formula is as follows:
Figure BDA0002607739830000092
wherein D isjTo determine the similarity between the vector A and the jth row parameter data of the matrix, xjkParameter data of the jth row and the kth column; a iskParameter data for the kth operating parameter in vector a;
and determining the fault diagnosis and prediction result corresponding to the maximum similarity as the fault diagnosis and prediction result obtained by fault diagnosis and prediction according to the operation parameters.
The working principle and the beneficial effects of the technical scheme are as follows:
in the embodiment, two methods are mainly used for performing fault diagnosis and prediction according to operation parameters to obtain fault diagnosis and prediction results, one method is based on a preset neural network model, and is used for performing feature extraction on the parameters, bringing the features into the model and obtaining a diagnosis and prediction result; establishing a diagnosis library, and determining a diagnosis result according to the similarity between the monitored parameter data and the parameter data corresponding to the diagnosis prediction result in the diagnosis library; the embodiment realizes the fault diagnosis and prediction based on the operation parameters.
The specific case of applying the embodiment of the invention is as follows:
1) developing a transmission system electromechanical fault simulation experiment verification platform for simulating working conditions such as variable load, variable speed, voltage fluctuation, electromagnetic interference, lubrication fault and the like;
the fully mechanized mining equipment has the advantages of severe working environment, narrow space, large voltage fluctuation, variable load, variable speed, large impact vibration, full-load starting, large damp dust and serious electromagnetic interference, and not only has great influence on the reliability and the service life of a transmission system, but also has great influence on signals such as vibration, current and the like. Therefore, fault state identification and accelerated full-life experiments need to be carried out by simulating working conditions of the fully mechanized mining face, fault sample data of various working conditions are rapidly obtained as verification data sources, various algorithms of the PHM are verified and improved, and the use requirements are met.
The working condition simulation of the fully mechanized mining equipment:
the test bed can simulate working conditions such as variable load, variable speed, voltage fluctuation, electromagnetic interference, lubrication fault, external impact and the like, and can simulate a single working condition and also simulate a plurality of working conditions in a composite mode.
The working condition simulation content is as follows:
a) the load can be loaded according to a preset load spectrum, b) the speed can be regulated according to a preset speed curve, c) the voltage fluctuation, d) the lubricating oil quality change simulation, e) the external vibration and electromagnetic harmonic interference are added, and g) the composite application of various working conditions is realized.
Development of a variable-frequency speed-regulating back-to-back transmission loading system:
the device performance testing technology, the accelerated life testing technology, the simulated fault injection technology and the working condition simulation are utilized to complete the development of a transmission system electromechanical fault simulation experiment table for simulating the working condition, realize the reappearance of the typical working condition and the fault of the power transmission system of the fully mechanized mining equipment and the accelerated life test, and provide reliable experimental verification for the algorithm related to the fault prediction and the health management.
The fault simulation experiment table consists of the following systems: the system comprises a variable frequency speed regulation back-to-back loading system, a power transmission system, a power supply system, a working condition simulation system, a fault simulation component, a sensing system, a data acquisition and analysis module and the like. The main development contents are as follows:
a) a back-to-back transmission loading system of a motor and a generator is designed, the motor is driven to regulate speed through frequency conversion, and the generator is loaded through a four-quadrant feedback frequency converter.
b) A coupling, a supporting seat, a torque meter and a speed reducer (a straight gear speed reducer and a planetary speed reducer can be replaced) are arranged between the motor and the generator.
c) The power supply and protection are provided, and the voltage can be adjusted to simulate voltage fluctuation.
d) Simulating the change of lubricating oil quality by medium injection.
e) External vibration and current harmonic interference are applied through a vibration source and an electromagnetic interference pulse generator.
f) Each fault simulation (fault injection) is carried out in a driving motor, a supporting seat, a speed reducer and a coupling.
Thirdly, simulating main contents of the fault:
the fault injection effect is achieved by developing a component with typical faults and installing the component on a laboratory bench.
● mechanical failure 9: the method comprises the following steps of bearing outer ring fault, bearing inner ring fault, bearing retainer fault, bearing roller fault, shaft misalignment fault, shaft unbalance fault, foundation loosening fault, gear fault and gear box lubricating oil fault.
● Electrical failure 7: the method comprises the following steps: the method comprises the following steps of motor turn-to-turn short circuit, rotor eccentricity, current imbalance fault, rotor broken bar fault, rotor bending fault, motor rotor imbalance fault and motor rotor misalignment fault.
Fourthly, the main content of the sensing system is as follows:
system configuration: displacement, vibration, temperature, oil quality, liquid level, current, voltage, rotating speed and other sensors.
Developing a data acquisition and analysis module:
and configuring a high-precision multi-channel data acquisition, analysis and storage module, wherein the maximum sampling frequency is more than or equal to 100kS/s, and the signal acquisition channels are more than or equal to 16 (the number of simultaneous sampling channels is increased and used for multi-parameter fusion algorithm verification).
Sixthly, the main technical performance indexes of the experiment table are as follows:
● driving motor power: 5kW, voltage: a three-phase line 380V;
● load mode: four-quadrant feedback programmable loading;
● speed regulation mode: carrying out variable frequency speed regulation at 0-3000 rpm;
● acquisition system: the maximum sampling frequency is more than or equal to 100kS/s, and the signal acquisition channel is more than or equal to 16;
● Gear drive: 1-stage spur gear transmission and 1-stage planetary gear transmission can be replaced.
● fault injection simulation: mechanical failure 9, electrical failure 7.
2) Developing simulation experiment research of electromechanical faults of a transmission system under a simulated working condition;
in the above, because the interference source of the field measured signal is formed by compounding various interferences, only the performance of the algorithm can be evaluated, and the root cause of the problem cannot be provided, so that the algorithm cannot be effectively improved; therefore, experimental study of simulated working conditions is needed, fault samples or full-life cycle samples obtained by carrying out simulation experiments of single working conditions on various working conditions are used for checking the root cause of poor robustness of the algorithm, and the algorithm is improved; and the comprehensive performance of the algorithm is verified by simulating the composite interference. Therefore, experimental studies of single-condition and compound-condition conditions are required to obtain the required fault samples.
Firstly, fault diagnosis experimental study:
a) the method mainly obtains various electromechanical fault state data samples under different working conditions, including data samples in three states of a normal state, a weak fault state, a fault state and the like. Through the working condition and fault injection simulation experiment, 6 single working conditions (normal operation under rated conditions and 5 types of single working condition simulation such as variable load, variable speed, voltage fluctuation, electromagnetic interference, lubrication fault and the like) and 16 electromechanical fault state simulation experiments under composite working condition conditions are carried out in total, and the number of samples of each electromechanical fault under each working condition is not less than 10.
Fault prediction experiment research:
the accelerated life test of gears and bearings in the straight gear transmission and the planetary gear transmission under different working conditions is mainly developed, and a life cycle data sample is obtained. The experiment is carried out on 6 single working conditions (normal operation under rated conditions and 5 types of single working condition simulation such as variable load, variable speed, voltage fluctuation, electromagnetic interference, lubrication fault and the like) and 4 types of full life cycle accelerated experiments of the straight-tooth transmission bearing and the gear, the planetary transmission bearing and the gear under the composite working condition, and the obtained data samples are not lower than 28 groups.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (8)

1. A health management verification evaluation system, comprising:
the health management module is used for acquiring operation parameters of the equipment, diagnosing and predicting faults according to the operation parameters and acquiring fault diagnosis and prediction results;
and the verification evaluation module is used for verifying the fault diagnosis and prediction result to obtain a verification result.
2. The health management validation evaluation system of claim 1, wherein the operating parameters comprise: one or more of the load, the running speed, the voltage, the environmental electromagnetic index and the lubrication index of the equipment are combined.
3. The health management validation evaluation system of claim 1, wherein the validation evaluation module comprises: and the simulation verification test platform is initialized based on the operation parameters, performs simulation operation and realizes verification of the fault diagnosis and prediction results.
4. The health management validation evaluation system of claim 1, wherein the validation evaluation module comprises: and the sample data verification submodule is used for verifying the fault diagnosis and prediction result according to pre-stored sample data.
5. The health management validation evaluation system of claim 3, wherein the simulation validation test platform comprises: the system comprises a variable-frequency speed-regulating back-to-back loading unit, a power transmission unit, a power supply unit, a working condition simulation unit, a fault simulation component, a sensing unit and a data acquisition and analysis unit;
the variable frequency speed regulation back-to-back loading unit comprises: the driving motor variable frequency speed regulation subunit and the generator variable frequency loading subunit;
the power transmission unit includes: the device comprises a coupler, a supporting seat, a torque meter and a speed reducer;
the working condition simulation unit comprises:
the medium injection subunit is used for injecting a medium to simulate the oil quality change of the lubricating oil;
the fault simulation component includes:
bearing with fault outer ring of bearing, bearing with fault inner ring of bearing, bearing with fault retainer of bearing, bearing with fault roller of bearing, bearing with fault misalignment of shaft, bearing with fault unbalance of shaft, speed reducer with fault loose foundation, speed reducer with fault gear and speed reducer with fault lubricating oil of gear box; a driving motor with a motor turn-to-turn short circuit, a driving motor with a rotor eccentric fault, a driving motor with a current imbalance fault, a driving motor with a rotor broken bar fault, a driving motor with a rotor bending fault, a driving motor with a motor rotor imbalance fault and a driving motor with a motor rotor misalignment fault;
the sensing unit includes:
one or more combinations of a displacement sensor, a vibration sensor, a temperature sensor, an oil quality sensor, a liquid level sensor, a current sensor, a voltage sensor and a rotating speed sensor;
the data acquisition and analysis unit comprises: a PC computer.
6. The health management validation evaluation system of claim 5, wherein the condition simulation unit further comprises:
the vibration simulation subunit is used for outputting vibration working conditions of the vibration simulation equipment;
and the electromagnetic interference subunit is used for outputting electromagnetic harmonic interference working conditions of the electromagnetic interference simulation equipment.
7. The health management validation evaluation system of claim 6, wherein the electromagnetic interference subunit comprises: an electromagnetic interference pulse generator.
8. The health management validation evaluation system of claim 1, wherein the performing fault diagnosis and prediction based on the operating parameters to obtain fault diagnosis and prediction results comprises:
extracting the characteristics of the operating parameters, and substituting the extracted characteristic values into a pre-established fault diagnosis and prediction model to obtain the fault diagnosis and prediction results;
or the like, or, alternatively,
constructing a diagnosis library according to the operation parameters of the historical records, and forming a matrix X according to the records corresponding to the fault diagnosis and prediction results of the parameters of different parameters contained in the diagnosis library, wherein the matrix X is as follows:
Figure FDA0002607739820000021
wherein x isnmThe mth parameter data corresponding to the nth fault diagnosis and prediction result; xnThe nth fault diagnosis and prediction result is obtained;
after the matrix X is constructed, the management module carries out filling processing on parameter data in the X, wherein the filling processing is carried out according to the following formula:
Figure FDA0002607739820000031
wherein x isstFor the parameter data whose missing position is in the s-th row and t-th column, xs1The non-missing parameter data is the parameter data of the s-th row in the first column, xitThe parameter data which is not missing is the parameter data of the ith row and the tth column;
acquiring the operation parameters of the equipment in real time, and determining a judgment vector A according to the operation parameters, namely:
A＝(a1,a2,…,am)；
wherein, amParameter data for the mth operating parameter;
calculating the similarity between the judgment vector A and each row of parameter data in the matrix X, wherein the calculation formula is as follows:
Figure FDA0002607739820000032
wherein D isjIs the similarity, x, between the judgment vector A and the jth row parameter data of the matrixjkParameter data of the jth row and the kth column; a iskParameter data for the kth operating parameter in vector a;
and determining the fault diagnosis and prediction result corresponding to the maximum similarity as the fault diagnosis and prediction result obtained by fault diagnosis and prediction according to the operation parameters.
CN202010744068.6A 2020-07-29 2020-07-29 Health management verifies evaluation system Pending CN111896246A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010744068.6A CN111896246A (en) 2020-07-29 2020-07-29 Health management verifies evaluation system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010744068.6A CN111896246A (en) 2020-07-29 2020-07-29 Health management verifies evaluation system

Publications (1)

Publication Number Publication Date
CN111896246A true CN111896246A (en) 2020-11-06

Family

ID=73182394

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010744068.6A Pending CN111896246A (en) 2020-07-29 2020-07-29 Health management verifies evaluation system

Country Status (1)

Country Link
CN (1) CN111896246A (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101387886A (en) * 2008-10-22 2009-03-18 哈尔滨工业大学 Processing method for imperfection failure diagnosis information
CN105258947A (en) * 2015-11-06 2016-01-20 北京航空航天大学 Compressed sensing-based antifriction bearing fault diagnosis method under working condition disturbance condition
CN105467975A (en) * 2015-12-29 2016-04-06 山东鲁能软件技术有限公司 Equipment fault diagnosis method
CN106295809A (en) * 2016-07-13 2017-01-04 北京航空航天大学 Aircraft embedded real-time diagnosis reasoning algorithm pilot system
CN107526784A (en) * 2017-07-27 2017-12-29 上海电力学院 A kind of method for diagnosing faults based on matrix fill-in
CN109146213A (en) * 2017-06-16 2019-01-04 北京航空航天大学 Spacecraft power supply system health admin-authentication Evaluation Environment
CN109187022A (en) * 2018-08-31 2019-01-11 南京信息工程大学 A kind of Fault Diagnosis of Roller Bearings based on accidental resonance and autocoder
CN111198098A (en) * 2020-01-14 2020-05-26 重庆邮电大学 Wind power generator bearing fault prediction method based on artificial neural network

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101387886A (en) * 2008-10-22 2009-03-18 哈尔滨工业大学 Processing method for imperfection failure diagnosis information
CN105258947A (en) * 2015-11-06 2016-01-20 北京航空航天大学 Compressed sensing-based antifriction bearing fault diagnosis method under working condition disturbance condition
CN105467975A (en) * 2015-12-29 2016-04-06 山东鲁能软件技术有限公司 Equipment fault diagnosis method
CN106295809A (en) * 2016-07-13 2017-01-04 北京航空航天大学 Aircraft embedded real-time diagnosis reasoning algorithm pilot system
CN109146213A (en) * 2017-06-16 2019-01-04 北京航空航天大学 Spacecraft power supply system health admin-authentication Evaluation Environment
CN107526784A (en) * 2017-07-27 2017-12-29 上海电力学院 A kind of method for diagnosing faults based on matrix fill-in
CN109187022A (en) * 2018-08-31 2019-01-11 南京信息工程大学 A kind of Fault Diagnosis of Roller Bearings based on accidental resonance and autocoder
CN111198098A (en) * 2020-01-14 2020-05-26 重庆邮电大学 Wind power generator bearing fault prediction method based on artificial neural network

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
徐扬: "电子产品PHM方法开发和验证实验平台研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 *
煤炭工业职业技能鉴定指导中心: "干扰的概念", 《采掘电钳工 技师、高级技师》 *
邱明权: "矿用带式输送机托辊健康监测方法研究", 《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅰ辑》 *
韩哲: "矿井提升设备健康管理***研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 *
黄凯峰: "煤矿安全监测监控***瓦斯浓度异常信号辨识方法研究", 《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅰ辑》 *

Similar Documents

Publication Publication Date Title
Imam et al. Development of an on-line rotor crack detection and monitoring system
Villa et al. Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load
Peng et al. Comprehensive planet gear diagnostics: Use of transmission error and mesh phasing to distinguish localised fault types and identify faulty gears
CN108896299A (en) A kind of gearbox fault detection method
Reges et al. Electric submersible pump vibration analysis under several operational conditions for vibration fault differential diagnosis
CN102735442A (en) Method for online monitoring and fault diagnosis of rotor
CN109542045A (en) Numerically-controlled machine tool predictive maintenance standard testing bed and operating method
Cheng et al. Quantitative damage detection for planetary gear sets based on physical models
Koshekov et al. An intelligent system for vibrodiagnostics of oil and gas equipment
Medvedovsky et al. Spall length estimation based on strain model and experimental FBG data
CN109543357A (en) Fault degree quantitative evaluation method for multivariate regression model optimization
CN111896246A (en) Health management verifies evaluation system
El Mahdi et al. Real Time Assessment of Novel Predictive Maintenance System Based on Artificial Intelligence for Rotating Machines.
Zhukovytskyy et al. Information-measuring test system of diesel locomotive hydraulic transmissions
Amin Al Hajj et al. Condition‐Based Monitoring on High‐Precision Gearbox for Robotic Applications
Leith et al. Real time expert system for identifying rotor faults and mechanical influences in induction motor phase current
KR102601338B1 (en) Apparatus for predicting equipment failure using learning data based on maintenance history and method thereof
JP2023179851A (en) Device state evaluating system and device state evaluating method
Dubaish et al. Fabrication of a test rig for gearbox fault simulation and diagnosis
RU2457456C1 (en) System for diagnosing submersible electric motors
An et al. Retrogressive analysis of industrial robot rotate vector reducer using acoustic emission techniques
EP3953580A1 (en) A method for computer-implemented monitoring of a component of a wind turbine
Jarrah et al. Web-based monitoring and fault diagnostics of machinery
CN111912642A (en) PHM cloud sharing verification platform
Nayak et al. design and development of machine fault simulator (MFS) for fault diagnosis

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20201106