CN108520093B - Mechanical equipment fault diagnosis method and device based on knowledge base - Google Patents
Mechanical equipment fault diagnosis method and device based on knowledge base Download PDFInfo
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- CN108520093B CN108520093B CN201810175974.1A CN201810175974A CN108520093B CN 108520093 B CN108520093 B CN 108520093B CN 201810175974 A CN201810175974 A CN 201810175974A CN 108520093 B CN108520093 B CN 108520093B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- 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
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
Abstract
The invention discloses a mechanical equipment fault diagnosis method and a device based on a knowledge base, wherein the method comprises the following steps: receiving equipment to be monitored to measure a vibration signal; calculating a characteristic frequency spectrum of the device based on a characteristic frequency knowledge base; performing knowledge reasoning on the characteristic spectrum based on a state knowledge base to acquire the running state of the equipment; wherein the characteristic frequency knowledge base stores a calculation method of the characteristic frequency; the state knowledge base stores the relationship between different operating states of the device and the characteristic frequency. The invention establishes a diagnosis knowledge base, has more diagnosis bases and has the characteristic of accurate diagnosis result.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of rotary mechanical equipment, in particular to a mechanical equipment fault diagnosis method and device based on a knowledge base.
Background
The fault diagnosis technology is used for judging whether the system works normally or not through various monitoring means in a system running state or a working state. Generally, a fault diagnosis technology adopts various state detection, measurement, analysis and judgment methods, combines the historical conditions and the operating environment factors of monitored equipment, evaluates the operating state of the equipment, judges whether the equipment is in a normal or abnormal state, and displays and records the state. If the equipment is in an abnormal state, an alarm needs to be given out so that operating personnel can process the equipment in time, and information and basic data are provided for fault analysis, performance evaluation, reasonable use and safe work.
With the development of modern science and technology, people put higher demands on the reliability of rotary mechanical equipment such as generators, blowers and the like under long-term and high load. The method for realizing the safe operation of the mechanical equipment by using the fault diagnosis technical means is an effective way. The system operation state is known in time, the fault is rapidly diagnosed and judged, the maintenance efficiency can be obviously improved, the maintenance cost is reduced, and the service life of the system can be prolonged. Therefore, the method has important practical significance for the research of fault diagnosis technology.
The contents of the fault diagnosis technology are increasingly enriched nowadays because the fault diagnosis technology is rapidly developed due to the urgent need of modern mass production. However, the related technologies have more researches on a specific part, less researches on universal equipment and systems, more functions of collecting and analyzing vibration signals and less functions of fault diagnosis.
Among the fault diagnosis methods, vibration signal processing and analysis based methods are the most effective common methods proved in practice. In the existing frequency domain characteristic analysis of the vibration signal, the main reference is frequency doubling of the rotating speed of mechanical equipment, such as 1 frequency doubling, 2 frequency doubling, 3 frequency doubling and the like. Although fault diagnosis based on characteristic frequency is a common technical means at present, there is no comprehensive method for generating characteristic frequency based on specific composition parameters of the diagnosed mechanical equipment.
Therefore, how to perform accurate fault diagnosis based on specific parameters of the equipment is a technical problem which needs to be solved urgently by those skilled in the art at present.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a fault diagnosis method, which comprises the steps of establishing a relevant model through equipment parameters, calculating a characteristic frequency set, generating an equipment state knowledge base according to historical vibration data, amplitude at the characteristic frequency and an artificial state mark, and analyzing a vibration signal to be diagnosed by using the knowledge base to obtain the equipment state. And if the equipment state is abnormal, giving the fault severity and a processing scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
a mechanical equipment fault diagnosis method based on a knowledge base comprises the following steps:
receiving equipment to be monitored to measure a vibration signal;
calculating a characteristic frequency spectrum of the device based on a characteristic frequency knowledge base;
performing knowledge reasoning on the characteristic spectrum based on a state knowledge base to acquire the running state of the equipment;
wherein the characteristic frequency knowledge base stores a calculation method of the characteristic frequency;
the state knowledge base stores the relationship between different operating states of the device and the characteristic frequency.
Further, the calculation of the characteristic frequency is based on the structural parameters of the device.
Further, the structural parameters include motor parameters, bearing parameters and specific mechanical parameters.
Further, the operational status includes normal and fault; when the running state is fault, the fault grade is also included.
Further, the state knowledge base establishing method comprises the following steps:
marking the running state of the historical vibration data of the surface of each device to generate the relationship between the characteristic spectral line and different running states of each device;
and calculating the amplitude ranges at the characteristic frequencies corresponding to different running states of each device to form a state knowledge base.
Further, the status flag includes:
firstly, marking a normal operation or fault type;
obtaining the maximum value and the minimum value of each characteristic frequency corresponding to each device according to the historical measurement record, thereby determining the normal value range of each characteristic frequency;
and further marking the fault grade of each device according to the normal value range.
According to a second object of the present invention, there is also provided a knowledge-base based mechanical equipment fault diagnosis system, comprising a memory and a processor, the memory storing a device base, a characteristic frequency knowledge base, a state knowledge base, and a computer program operable on the processor, the processor implementing the following steps when executing the program, including:
receiving the equipment name of the equipment to be monitored, and calling corresponding structural parameters from an equipment library;
receiving a vibration signal measurement result of equipment to be monitored;
calculating a characteristic frequency spectrum of the device based on a characteristic frequency knowledge base;
performing knowledge reasoning on the characteristic spectrum based on a state knowledge base to acquire the running state of the equipment;
wherein the characteristic frequency knowledge base stores a calculation method of the characteristic frequency;
the state knowledge base stores the relationship between different operating states of the device and the characteristic frequency.
The invention has the advantages of
1. The method mainly aims at bearing faults and motor faults commonly existing in general mechanical equipment, fault diagnosis modeling and algorithm design are carried out, and identification and fault grade analysis of various types of mechanical faults are achieved. The method can determine 20-30 different characteristic frequencies according to specific composition parameters of mechanical equipment, such as the inner diameter and the outer diameter of a bearing, and greatly increases the information content according to diagnosis, so that the method has the characteristic of accurate diagnosis result.
2. The invention adopts an automatic diagnosis technology based on a knowledge base, wherein the knowledge base comprises a characteristic frequency knowledge base and an equipment state knowledge base. The characteristic frequency knowledge base records the model of the equipment and the characteristic frequency set, and the equipment state knowledge base records the content such as the equipment state, the equipment characteristic frequency spectrum range and the like. For different mechanical equipment, the characteristic frequency can be determined according to specific composition parameters of the equipment instead of simply according to the rotating speed of the equipment, so that the characteristic of strong adaptability is realized, and the reliability and maintainability are high.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the equipment fault modeling and fault diagnosis of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example one
The embodiment discloses a mechanical equipment fault diagnosis modeling method based on a knowledge base, which comprises the following steps:
step 1, receiving structural parameters of rotary equipment.
The structural parameters include: motor parameters, bearing parameters, and specific mechanical parameters. In particular, the amount of the solvent to be used,
1) the motor parameters comprise: the motor main frequency, the motor rotating speed, the number of teeth of a motor stator, the number of teeth of a motor rotor and the number of pole pairs of the motor.
2) The bearing parameters include: the diameter of the bearing inner ring, the diameter of the bearing outer ring, the diameter of the bearing balls, the radius of the bearing inner circle, the number of the bearing balls, the number of the bearing rows and the number of the bearing spiral lines.
3) The device parameters include: equipment rotating speed, equipment bearing number, equipment blade number, equipment rotor stage number, equipment first-stage rotor tooth number, equipment second-stage rotor tooth number and equipment gear ratio.
And 2, establishing a characteristic frequency knowledge base, wherein the characteristic frequency knowledge base comprises characteristic frequency calculation formulas required in fault diagnosis, and characteristic frequency sets corresponding to the rotary equipment and calculated according to the formulas to generate characteristic frequency spectrums.
The characteristic frequencies T1-T26 are calculated according to the formula:
t1 ═ motor speed/60.0;
T2＝2.0*T1；
t3 ═ T1 bearing inner circle radius ═ number of rows of bearings)/(2.0 × (Z1+ (bearing ball diameter/2.0));
t4 ═ T3 ═ number of bearing balls × -number of bearing rows;
t5 ═ T4 ═ Z4 ═ number of rows of bearings;
t6 ═ (T1 bearing inner circle radius × (number of rows of bearings)/bearing ball diameter;
t7 ═ T6 ═ number of bearing balls × -number of bearing rows;
t8 ═ (T6-T1) number of rows of bearings;
t9 ═ T8 ═ number of bearing balls × -number of bearing rows;
t10 ═ (T1-T3) number of rows of bearings;
t11 ═ T10 ═ number of bearing balls × -number of bearing rows;
t12 ═ T1 ═ motor stator tooth counts;
t13 ═ T1 motor rotor tooth counts;
t14 ═ T1 (number of teeth of motor rotor + number of teeth of motor stator)
T15 ═ T1 equipment blade count
T16 ═ T1 number of rotor stages of device
T17 ═ 2.0 motor main frequency
4.0 main frequency of motor T18
T19 ═ 2.0 major frequency Z3 of the motor
T20 (main frequency of motor) (1-Z3)
T21 ═ electric machine main frequency (1-2.0 x Z3)
T22 ═ 2.0 × motor main frequency (1-2.0 × Z3)
T23＝2300
T24＝3150
T25＝4500
T26 is the main frequency of the motor;
the intermediate parameters involved include:
z1 ═ 4.0- (bearing inner race diameter + bearing outer race diameter)/2.0- (bearing ball diameter);
z2 ═ (motor dominant frequency 60.0)/(motor pole pair number);
z3 ═ Z2-motor speed)/Z2
Z4 ═ (Z1+2 bearing ball diameter)/(bearing ball diameter/2.0);
z5 ═ bearing ball diameter
And 3, establishing a state knowledge base, wherein the state knowledge base comprises the relation between different running states of each device and the characteristic frequency spectrum. The operation state is normal operation, or fault type and fault grade.
The state knowledge base establishing method comprises the following steps:
the expert marks the running state of the historical vibration data of the surface of each device, and marks the normal running or fault type;
obtaining the maximum value and the minimum value of each characteristic frequency corresponding to each device according to the historical measurement record, thereby determining the normal value range of each characteristic frequency;
further marking the fault grade of each device according to the normal value range;
generating a relation between characteristic spectral lines (amplitudes of the vibration signals at different characteristic frequencies) and different operation states of each device according to the marks;
and calculating the amplitude ranges at the characteristic frequencies corresponding to different running states of each device to form a state knowledge base.
And 4, measuring a vibration signal of the equipment to be monitored, calculating a characteristic frequency spectrum according to the characteristic frequency set generated in the step 2, and performing knowledge reasoning on the characteristic frequency spectrum according to the state knowledge base in the step 3 to obtain a corresponding operation state.
The method mainly aims at bearing faults and motor faults commonly existing in general mechanical equipment, fault diagnosis modeling and algorithm design are carried out, and identification and fault grade analysis of various types of mechanical faults are achieved.
Preferably, the characteristic frequency knowledge base records the model of the device and the characteristic frequency set, and the device state knowledge base records the model of the device, the operating age of the device, the state of the device, the characteristic frequency spectrum range of the device, and the like. The knowledge base can be dynamically updated and supplemented according to factors such as the model of equipment, the age and the like.
Example two
An object of the present embodiment is to provide a computing device.
A knowledge-base based mechanical device fault diagnosis apparatus comprising a memory and a processor, the memory storing a device base, a characteristic frequency knowledge base, a state knowledge base, and a computer program operable on the processor, the processor implementing the following steps when executing the program, comprising:
receiving the equipment name of the equipment to be monitored, and calling corresponding structural parameters from an equipment library;
receiving a vibration signal measurement result of equipment to be monitored;
calculating a characteristic frequency spectrum of the device based on a characteristic frequency knowledge base;
performing knowledge reasoning on the characteristic spectrum based on a state knowledge base to acquire the running state of the equipment;
wherein the characteristic frequency knowledge base stores a calculation method of the characteristic frequency;
the state knowledge base stores the relationship between different operating states of the device and the characteristic frequency.
EXAMPLE III
An object of the present embodiment is to provide a computer storage medium.
A computer storage medium comprising a device library, a feature frequency knowledge library, a state knowledge library, and a computer program operable on a processor, the program when executed by the processor implementing the steps comprising:
receiving the equipment name of the equipment to be monitored, and calling corresponding structural parameters from an equipment library;
receiving a vibration signal measurement result of equipment to be monitored;
calculating a characteristic frequency spectrum of the device based on a characteristic frequency knowledge base;
performing knowledge reasoning on the characteristic spectrum based on a state knowledge base to acquire the running state of the equipment;
wherein the characteristic frequency knowledge base stores a calculation method of the characteristic frequency;
the state knowledge base stores the relationship between different operating states of the device and the characteristic frequency.
The invention has the beneficial effects that:
1. the method mainly aims at bearing faults and motor faults commonly existing in general mechanical equipment, fault diagnosis modeling and algorithm design are carried out, and identification and fault grade analysis of various types of mechanical faults are achieved. The method can determine 20-30 different characteristic frequencies according to specific composition parameters of mechanical equipment, such as the inner diameter and the outer diameter of a bearing, and greatly increases the information content according to diagnosis, so that the method has the characteristic of accurate diagnosis result.
2. The invention adopts an automatic diagnosis technology based on a knowledge base, wherein the knowledge base comprises a characteristic frequency knowledge base and an equipment state knowledge base. The characteristic frequency knowledge base records the model of the equipment and the characteristic frequency set, and the equipment state knowledge base records the content such as the equipment state, the equipment characteristic frequency spectrum range and the like. For different mechanical equipment, the characteristic frequency can be determined according to specific composition parameters of the equipment instead of simply according to the rotating speed of the equipment, so that the characteristic of strong adaptability is realized, and the reliability and maintainability are high.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (3)
1. A mechanical equipment fault diagnosis method based on a knowledge base is characterized by comprising the following steps:
step 1: receiving structural parameters of the rotating equipment;
the structural parameters include: motor parameters, bearing parameters and specific mechanical parameters; in particular, the amount of the solvent to be used,
1) the motor parameters comprise: the motor main frequency, the motor rotating speed, the number of teeth of a motor stator, the number of teeth of a motor rotor and the number of pole pairs of the motor;
2) the bearing parameters include: the diameter of a bearing inner ring, the diameter of a bearing outer ring, the diameter of bearing balls, the radius of a bearing inner circle, the number of the bearing balls, the number of bearing rows and the number of bearing spiral lines;
3) the device parameters include: the device comprises a device rotating speed, a device bearing number, a device blade number, a device rotor stage number, a device primary rotor tooth number, a device secondary rotor tooth number and a device gear ratio;
step 2: establishing a characteristic frequency knowledge base, wherein the characteristic frequency knowledge base comprises characteristic frequency calculation formulas required in fault diagnosis, and characteristic frequency sets corresponding to various rotary devices and obtained according to the formulas to generate characteristic frequency spectrums;
the characteristic frequencies T1-T26 are calculated according to the formula:
t1 ═ motor speed/60.0;
T2＝2.0*T1；
t3 ═ T1 bearing inner circle radius ═ number of rows of bearings)/(2.0 × (Z1+ (bearing ball diameter/2.0));
t4 ═ T3 ═ number of bearing balls × -number of bearing rows;
t5 ═ T4 ═ Z4 ═ number of rows of bearings;
t6 ═ (T1 bearing inner circle radius × (number of rows of bearings)/bearing ball diameter;
t7 ═ T6 ═ number of bearing balls × -number of bearing rows;
t8 ═ (T6-T1) number of rows of bearings;
t9 ═ T8 ═ number of bearing balls × -number of bearing rows;
t10 ═ (T1-T3) number of rows of bearings;
t11 ═ T10 ═ number of bearing balls × -number of bearing rows;
t12 ═ T1 ═ motor stator tooth counts;
t13 ═ T1 motor rotor tooth counts;
t14 ═ T1 (motor rotor teeth count + motor stator teeth count);
t15 ═ T1 equipment leaf count;
t16 ═ T1 equipment rotor progression;
t17 ═ 2.0 × motor dominant frequency;
t18 ═ 4.0 × motor dominant frequency;
t19 ═ 2.0 ═ motor dominant frequency Z3;
t20 ═ motor dominant frequency (1-Z3);
t21 ═ motor dominant frequency (1-2.0 × Z3);
t22 ═ 2.0 motor primary frequency (1-2.0 × Z3);
T23＝2300；
T24＝3150；
T25＝4500；
t26 is the main frequency of the motor;
the intermediate parameters involved include:
z1 ═ 4.0- (bearing inner race diameter + bearing outer race diameter)/2.0- (bearing ball diameter);
z2 ═ (motor dominant frequency 60.0)/(motor pole pair number);
z3 ═ (Z2-motor speed)/Z2;
z4 ═ (Z1+2 bearing ball diameter)/(bearing ball diameter/2.0);
z5-bearing ball diameter;
and step 3: establishing a state knowledge base, wherein the state knowledge base comprises the relation between different operation states of each device and characteristic frequency spectrums; the operation state is normal operation, or a fault type and a fault grade;
the state knowledge base establishing method comprises the following steps:
the expert marks the running state of the historical vibration data of the surface of each device, and marks the normal running or fault type;
obtaining the maximum value and the minimum value of each characteristic frequency corresponding to each device according to the historical measurement record, thereby determining the normal value range of each characteristic frequency;
further marking the fault grade of each device according to the normal value range;
generating the relation between the characteristic spectral line and different operation states of each device according to the marks;
calculating the amplitude ranges at the characteristic frequencies corresponding to different running states of each device to form a state knowledge base;
and 4, step 4: and (3) measuring vibration signals of the equipment to be monitored, calculating a characteristic frequency spectrum according to the characteristic frequency set generated in the step (2), and performing knowledge reasoning on the characteristic frequency spectrum according to the state knowledge base in the step (3) to obtain a corresponding operation state.
2. A knowledge-base-based mechanical equipment fault diagnosis apparatus comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and the processor implements the knowledge-base-based mechanical equipment fault diagnosis method according to claim 1 when executing the program.
3. A computer storage medium having stored thereon a computer program which, when executed by a processor, performs the knowledge-base based mechanical device fault diagnosis method of claim 1.
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| US6560552B2 (en) * | 2001-03-20 | 2003-05-06 | Johnson Controls Technology Company | Dynamically configurable process for diagnosing faults in rotating machines |
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