CN117309349A - Method for determining equipment frequency conversion, method and device for diagnosing equipment fault type - Google Patents
Method for determining equipment frequency conversion, method and device for diagnosing equipment fault type Download PDFInfo
- Publication number
- CN117309349A CN117309349A CN202311256616.0A CN202311256616A CN117309349A CN 117309349 A CN117309349 A CN 117309349A CN 202311256616 A CN202311256616 A CN 202311256616A CN 117309349 A CN117309349 A CN 117309349A
- Authority
- CN
- China
- Prior art keywords
- frequency
- equipment
- determining
- spectrum
- fault
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 94
- 238000006243 chemical reaction Methods 0.000 title claims abstract description 91
- 230000001133 acceleration Effects 0.000 claims abstract description 136
- 238000001228 spectrum Methods 0.000 claims abstract description 125
- 238000003745 diagnosis Methods 0.000 claims abstract description 29
- 230000010354 integration Effects 0.000 claims abstract description 16
- 238000005070 sampling Methods 0.000 claims description 82
- 239000003990 capacitor Substances 0.000 claims description 27
- 239000013078 crystal Substances 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 10
- 238000012800 visualization Methods 0.000 claims description 9
- 239000000284 extract Substances 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims description 4
- 230000001131 transforming effect Effects 0.000 claims description 3
- 230000017105 transposition Effects 0.000 claims 2
- 238000004458 analytical method Methods 0.000 abstract description 16
- 238000001514 detection method Methods 0.000 abstract description 6
- 238000004891 communication Methods 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000009776 industrial production Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004092 self-diagnosis Methods 0.000 description 4
- 230000009466 transformation Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000010365 information processing Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000001845 vibrational spectrum Methods 0.000 description 1
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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
- G01P15/02—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses
- G01P15/08—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses with conversion into electric or magnetic values
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/02—Arrangements for measuring frequency, e.g. pulse repetition rate; Arrangements for measuring period of current or voltage
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The application provides a method for determining equipment frequency conversion, a method and a device for diagnosing equipment fault types, wherein the method for determining equipment frequency conversion comprises the steps of responding to equipment frequency conversion determination requests and acquiring acceleration time domain signals of equipment uploaded by a sensor; performing Fourier transform on the acceleration time domain signal to determine an acceleration frequency spectrum of the equipment; performing frequency domain integration on the acceleration frequency spectrum to determine a speed frequency spectrum of the equipment; determining a target speed spectrum of the equipment according to a preset rotating speed range and a preset speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum. According to the method and the device, manual detection and analysis of the device are not needed, labor cost is saved, intelligent determination of the frequency conversion of the device is achieved by utilizing the speed spectrum self-diagnosis program, and then the fault type of the device is diagnosed by analyzing the frequency conversion and the fault parameters, so that the fault diagnosis efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent diagnosis of equipment faults, in particular to a method for determining equipment frequency conversion and a method and device for diagnosing equipment fault types.
Background
Mechanical equipment vibration monitoring and fault diagnosis techniques were proposed in the united states in the middle and late 60 s of the last century, but none of the techniques developed significantly before the last 70 s of the century due to insufficient knowledge of vibration monitoring and fault diagnosis and the lack of the ability to collect analytical instrumentation.
With rapid development of computer technology, digital signal processing technology and fault diagnosis theoretical knowledge, equipment vibration monitoring and fault diagnosis technology are coming into rapid development period. After 90 s of the last century, related mechanical equipment vibration monitoring and fault diagnosis technologies in China are widely applied to large-scale high-speed rotating equipment in the industries of electric power, petrochemical industry, cement, steel and the like.
In conventional industrial production, vibration analyzers continuously monitor and diagnose equipment manually, they need to collect data of the equipment, analyze the frequency conversion in the vibration spectrum, and further analyze the fault frequency by the frequency conversion to obtain a final analysis conclusion: resonance, imbalance, misalignment, looseness, bearing failure, gear failure, and the like.
In modern industrial production, intelligent manufacturing and the continuous development of internet of things technology, intelligent diagnosis technology has become a major trend of future industrial production. The traditional vibration analyst can also realize the monitoring and diagnosis of equipment to a certain extent, but compared with the intelligent diagnosis technology, the manual detection and diagnosis have the problems of low efficiency, inaccurate result, low response speed and the like, and are not beneficial to improving the efficiency of industrial production.
Disclosure of Invention
The method and the device for determining the equipment frequency conversion and the method and the device for diagnosing the equipment fault type realize the intelligent determination of the frequency conversion, and further improve the efficiency of fault diagnosis by analyzing the frequency conversion and fault parameters to diagnose the equipment fault type.
The first aspect of the application discloses a method for determining equipment frequency conversion, which is applied to a processing module in an internet of things system, wherein the internet of things system comprises a sensor and the processing module, and the method for determining equipment frequency conversion comprises the following steps:
responding to a device frequency conversion determining request, and acquiring an acceleration time domain signal of the device uploaded by a sensor;
performing Fourier transform on the acceleration time domain signal to determine an acceleration frequency spectrum of the equipment;
performing frequency domain integration on the acceleration frequency spectrum to determine a speed frequency spectrum of the equipment;
determining a target speed spectrum of the equipment according to a preset rotating speed range and a preset speed spectrum;
and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum.
Preferably, the sampling frequency of the sensor is configured to be a preset sampling frequency;
the method for determining the device frequency conversion further comprises the following steps:
when the speed spectrum does not accord with the preset state, correcting the preset sampling frequency to determine a corrected sampling frequency, and configuring the sampling frequency of the sensor as the corrected sampling frequency;
Correcting the preset sampling frequency to determine a corrected sampling frequency includes:
acquiring an experimental acceleration time domain signal of standard experimental equipment through a sensor;
filtering direct current components in the experimental acceleration time domain signals, carrying out Fourier transform on the experimental acceleration time domain signals, and determining experimental acceleration frequency spectrums of standard experimental equipment;
determining the frequency with the maximum amplitude in the experimental acceleration frequency spectrum as the experimental vibration frequency of standard experimental equipment;
and determining the corrected sampling frequency of the sensor according to the fixed vibration frequency of the standard experimental equipment, the experimental vibration frequency and the preset sampling frequency of the sensor.
Preferably, the method is characterized in that determining the corrected sampling frequency of the sensor based on the fixed vibration frequency of the standard experimental equipment, the experimental vibration frequency and the preset sampling frequency of the sensor comprises:
calculating a corrected sampling frequency of the sensor according to the formula (1);
F s ＝a*f 0 /f 1 formula (1)
Wherein a is the fixed vibration frequency of standard experimental equipment, f 0 For a preset sampling frequency of the sensor, f 1 For the experimental vibration frequency of the standard experimental equipment obtained by the sensor, F s For the corrected sampling frequency of the sensor.
Preferably, the sensor comprises an acceleration sensor, the acceleration sensor comprising an analog-to-digital converter; the outside of the analog-to-digital converter is provided with a calibration circuit;
The calibration circuit comprises a power supply, a crystal oscillator, an inductor, a resistor and a capacitor; the crystal oscillator is electrically connected with the analog-to-digital converter;
the crystal oscillator comprises a power supply pin, an output pin and a grounding pin;
the capacitor comprises a first capacitor, a second capacitor and a third capacitor;
the analog-to-digital converter comprises a clock input pin;
the power supply series inductor is grounded after being connected with the second capacitor and the third capacitor in parallel, and is electrically connected with the power supply pin; the output pin series resistor is electrically connected with the clock input pin, and is grounded after being connected with the first capacitor in series; the grounding pin is grounded.
Preferably, the method for determining the preset rotation speed range includes:
acquiring parameters corresponding to equipment in the equipment model;
inputting parameters into an equipment model, and outputting the rotating speed of equipment after the equipment model is analyzed;
determining a preset rotation speed range as rps×p by using rotation speed 1 To rps p 2 ；
Wherein rps is the rotational speed; p is p 1 For the first threshold parameter, p 2 Is a second threshold parameter, and p is more than or equal to 0.9 1 <p 2 ≤1.1。
Preferably, before fourier transforming the acceleration time domain signal, the method for determining the device frequency conversion further comprises:
eliminating noise interference of acceleration time domain signals uploaded by a sensor;
Demodulating the acceleration time domain signal after noise interference is eliminated, including: demodulating a high-frequency signal in the acceleration time domain signal after noise interference is eliminated into a low-frequency signal;
and filtering the direct current component in the demodulated acceleration time domain signal.
A second aspect of the present application discloses a method of diagnosing a fault type of a device for diagnosing a fault type of a faulty device, comprising:
determining a frequency conversion of the fault device based on the method for determining the frequency conversion of the device of the first aspect;
comparing the frequency of the rotation with the fault characteristic frequency of the fault equipment to diagnose the fault type of the equipment.
Preferably, the method for acquiring the fault characteristic frequency comprises the following steps:
extracting an envelope curve from the acceleration time domain signal;
fourier transformation and visualization operation are carried out on the envelope curve, and then an envelope spectrum is drawn;
and identifying and extracting frequencies corresponding to the amplitude values meeting preset conditions from the envelope spectrum as fault characteristic frequencies.
A third aspect of the present application discloses a frequency conversion determining apparatus for performing the method of determining a frequency conversion of a device of the first aspect, the frequency conversion determining apparatus comprising:
the information acquisition unit is configured to acquire acceleration time domain signals of the equipment uploaded by the sensor;
a fourier transform unit configured to fourier transform the acceleration time domain signal, determining an acceleration spectrum of the device;
A frequency domain integration unit configured to perform frequency domain integration on the acceleration spectrum, and determine a velocity spectrum of the device;
a frequency conversion determining unit configured to determine a target speed spectrum of the device according to a preset rotation speed range and speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum.
A fourth aspect of the present application discloses a fault type diagnosis apparatus for performing the method of diagnosing a fault type of a device of the second aspect, the fault type diagnosis apparatus comprising:
the fault characteristic frequency acquisition unit is configured to extract an envelope curve from the acceleration time domain signal, draw an envelope spectrum after carrying out Fourier transform and visualization operation on the envelope curve, and identify and extract a frequency corresponding to an amplitude conforming to a preset condition from the envelope spectrum as a fault characteristic frequency;
and a comparison unit configured to compare the rotation frequency with a failure characteristic frequency of the failed device to diagnose a failure type of the device.
According to a specific embodiment provided by the application, the application discloses the following technical effects:
the application provides a method for determining equipment frequency conversion, a method and a device for diagnosing equipment fault types, wherein the method for determining equipment frequency conversion comprises the steps of responding to equipment frequency conversion determination requests and acquiring acceleration time domain signals of equipment uploaded by a sensor; performing Fourier transform on the acceleration time domain signal to determine an acceleration frequency spectrum of the equipment; performing frequency domain integration on the acceleration frequency spectrum to determine a speed frequency spectrum of the equipment; determining a target speed spectrum of the equipment according to a preset rotating speed range and a preset speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum. According to the method and the device, manual detection and analysis of the device are not needed, labor cost is saved, intelligent determination of the frequency conversion of the device is achieved by utilizing the speed spectrum self-diagnosis program, and then the fault type of the device is diagnosed by analyzing the frequency conversion and the fault parameters, so that the fault diagnosis efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of determining device transitions provided in one embodiment of the present application;
fig. 2 is a flowchart of a method for determining a preset rotation speed range in a method for determining a rotation frequency of an apparatus according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for correcting a sampling frequency in a method for determining a device transition frequency according to one embodiment of the present application;
FIG. 4 is one of Matlab simulation diagrams of a method of determining device switching frequency provided in one embodiment of the present application;
FIG. 5 is a second Matlab simulation diagram of a method for determining device switching frequency provided in one embodiment of the present application;
FIG. 6 is a schematic diagram of a calibration circuit in a method of determining device switching frequency provided in one embodiment of the present application;
FIG. 7 is a flowchart of a method for obtaining a fault signature in a method for diagnosing a type of device fault provided in one embodiment of the present application;
FIG. 8 is one of Matlab simulation graphs of a method of diagnosing device fault types provided in one embodiment of the present application;
FIG. 9 is a second Matlab simulation diagram of a method for diagnosing a type of device fault provided in one embodiment of the present application;
FIG. 10 is a third Matlab simulation diagram of a method of diagnosing a device fault type provided in one embodiment of the present application;
FIG. 11 is a schematic diagram of a computer device provided in one embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.
The application provides a method for determining equipment frequency conversion, a method and a device for diagnosing equipment fault types, wherein the method for determining equipment frequency conversion comprises the steps of responding to equipment frequency conversion determination requests and acquiring acceleration time domain signals of equipment uploaded by a sensor; performing Fourier transform on the acceleration time domain signal to determine an acceleration frequency spectrum of the equipment; performing frequency domain integration on the acceleration frequency spectrum to determine a speed frequency spectrum of the equipment; determining a target speed spectrum of the equipment according to a preset rotating speed range and a preset speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum. According to the method and the device, manual detection and analysis of the device are not needed, labor cost is saved, intelligent determination of the frequency conversion of the device is achieved by utilizing the speed spectrum self-diagnosis program, and then the fault type of the device is diagnosed by analyzing the frequency conversion and the fault parameters, so that the fault diagnosis efficiency is improved.
Example 1
In the traditional industrial production, vibration analyzers continuously monitor and analyze equipment manually, and the vibration analyzers need to collect data of the equipment, analyze the frequency conversion in the vibration frequency spectrum, and further analyze the fault frequency according to the frequency conversion to obtain an analysis conclusion. Although the manual analysis can realize the diagnosis of the equipment, the efficiency of the manual analysis is lower, the diagnosis result is inaccurate, and the improvement of the efficiency of industrial production is not facilitated, so the embodiment 1 of the application provides a method for determining the equipment frequency conversion to realize the determination of the equipment frequency conversion, and further the fault type of the equipment is diagnosed by analyzing the frequency conversion and the fault parameters, so that the efficiency of the fault analysis and the accuracy of the analysis result are improved.
A method of determining device switching frequency, as shown in fig. 1, comprising: responding to a device frequency conversion determining request, and acquiring an acceleration time domain signal of the device uploaded by a sensor; performing Fourier transform on the acceleration time domain signal to determine an acceleration frequency spectrum of the equipment; performing frequency domain integration on the acceleration frequency spectrum to determine a speed frequency spectrum of the equipment; determining a target speed spectrum of the equipment according to a preset rotating speed range and a preset speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum.
Specifically, in response to a device frequency conversion determination request, acquiring an acceleration time domain signal of the device uploaded by the sensor; in one embodiment of the application, the acceleration sensor is adopted, and has the advantages of small volume, light weight, high sensitivity, large dynamic range, wide frequency range and the like compared with the speed sensor and the displacement sensor; meanwhile, the acceleration sensor is suitable for high-frequency band analysis, and has better effect when detecting signals transmitted by impact damage faults; the acceleration time domain signal can reflect the vibration information of the device and can be expressed as a vibration signal.
Performing Fourier transform on the acceleration time domain signal by using a speed spectrum self-diagnosis frequency conversion program, and converting the acceleration time domain signal into an acceleration frequency spectrum of a frequency domain so as to determine the acceleration frequency spectrum of the equipment; preferably, the velocity spectrum self-diagnostic frequency conversion procedure includes a Discrete Fourier Transform (DFT) algorithm with which the acceleration time domain signal subjected to the window function processing is fourier-transformed. The discrete Fourier transform algorithm converts the time domain signal into a frequency domain signal, and obtains a complex array, wherein each element in the array represents the amplitude and phase information of different frequency components.
The acceleration frequency spectrum is subjected to frequency domain integration by utilizing a speed spectrum self-diagnosis frequency conversion program, and the frequency domain acceleration frequency spectrum is converted into a frequency domain speed frequency spectrum so as to determine the speed frequency spectrum of the equipment; preferably, the velocity spectrum self-diagnosis program includes a frequency domain integration algorithm in which the acceleration spectrum is divided by a frequency corresponding to the acceleration spectrum as a one-dimensional array to perform a frequency domain integration operation, thereby obtaining the velocity spectrum. Wherein the frequency does not include 0 frequency to avoid the error of dividing by 0.
The speed spectrum self-diagnosis frequency conversion program determines a target speed spectrum of the equipment in the speed spectrum according to a preset rotating speed range, and extracts a frequency corresponding to an amplitude meeting a preset condition from the target speed spectrum as the frequency conversion of the equipment; in one embodiment of the present application, the frequency corresponding to the amplitude meeting the preset condition is the frequency having the maximum amplitude; wherein, the rotation frequency refers to the number of rotations per minute or per second of the rotating object, which is related to the mechanical vibration; the frequency of the mechanical vibration with the largest amplitude is called the main frequency, i.e. the frequency with the largest amplitude; in a rotating object, vibration with different frequencies can be generated due to different rotating speeds of different parts; of these vibrations, the primary frequency is the most remarkable feature of the vibration generated by the device, and the primary frequency is used as the frequency of the device and is analyzed in the next step, which plays a key role in diagnosing the failure of the device. Meanwhile, a frequency interval is determined by using the rotating speed of the equipment so as to reduce the range of the speed spectrum, thereby being convenient for extracting the rotating frequency from the reduced speed spectrum range and improving the accuracy of the rotating frequency extraction.
Preferably, the method for determining equipment frequency conversion is applied to a processing module of an internet of things system; the internet of things system comprises a sensor and a processing module. In one embodiment of the present application, the processing module is configured to process and analyze data collected from sensors and other devices, and the internet of things system includes, but is not limited to, a sensor module, a processing module, a storage module, a communication module, a power management module, and an application interface module.
Further, after the frequency conversion of the device is determined, the frequency conversion and the fault parameters of the device are combined and analyzed to diagnose the fault type of the device. The fault parameters include a fault signature frequency.
The frequency of the rotation is compared with the characteristic frequency of the fault to diagnose the fault type of the equipment. Wherein, when the equipment has fault or abnormal condition, the frequency spectrum of the vibration signal changes, and the changes may include the newly added frequency peak value or the original frequency peak value. The fault characteristic frequency is the newly added frequency peak value or the change of the original frequency peak value. In some cases, when a problem exists with the device, a frequency peak in the velocity spectrum of the vibration signal related to the frequency of the rotation may be caused, because the vibration signal caused by the fault is related to the rotation of the device. Therefore, in performing fault diagnosis, the frequency conversion of the device and the fault characteristic frequency need to be compared and analyzed to determine the fault type. The fault types include resonance, unbalance, misalignment, looseness, bearing faults, gear faults, and the like.
Example 2
The rotation speed of the device is calculated by using a device model, different devices have different device models and device parameters, and embodiment 2 of the application provides a method for determining the rotation frequency of the device, and on the basis of embodiment 1, a method for determining a preset rotation speed range is provided, and the rotation speed of the device is calculated by using different device models.
In a method for determining a rotation frequency of a device, as shown in fig. 2, a method for determining a preset rotation speed range includes:
acquiring parameters corresponding to equipment in the equipment model; different equipment corresponds to different equipment models and parameters, for example, a motor corresponds to a motor model, and the motor parameters comprise alternating current frequency, electrode number and the like; the gear box corresponds to the gear box model, and the gear box parameters comprise motor rotation speed, gear number and the like; the belt pulley corresponds to the belt pulley model, and the belt pulley parameters comprise the rotation speed, the diameter and the like of the driving wheel; the roller corresponds to the roller model, and the roller parameters comprise vehicle speed, radius and the like.
Inputting parameters into an equipment model, and outputting the rotating speed of equipment after the equipment model is analyzed; and calculating the rotating speed of the equipment by using the model, and providing data for subsequent further analysis.
Determining a preset rotation speed range as rps×p by using rotation speed 1 To rps p 2 The method comprises the steps of carrying out a first treatment on the surface of the The rotation speed is limited in a preset rotation speed range, so that the running state of the equipment can be more comprehensively estimated, meanwhile, in the actual model calculation, errors and fluctuation exist, and therefore, the influence of calculation errors on a result can be eliminated by setting an appropriate rotation speed interval, and the accuracy of the result is improved.
Wherein rps is the rotational speed, representing revolutions per second; p is p 1 For the first threshold parameter, p 2 Is a second threshold parameter, and p is more than or equal to 0.9 1 <p 2 ≤1.1。
In one embodiment of the present application, a motor model is used to determine a preset rotational speed range of a motor, and the process is as follows:
note that the motor in this embodiment is a synchronous rotor motor;
the motor model is rps 1 ＝2*F L /n 1 The method comprises the steps of carrying out a first treatment on the surface of the Wherein rps 1 F is the rated rotation speed of the motor L For alternating current frequency, n 1 Is the number of poles of the motor.
For example, when F L Is the alternating current frequency of China, n 1 When the number of the samples is =4,
p 1 ＝0.95，p 2 =1.05, the preset rotational speed range of the motor is (25×0.95, 25×1.05), i.e., (23.75, 26.25).
When F L For the alternating current frequency of a country in foreign countries, n 1 When the number of the samples is =8,
p 1 ＝0.95，p 2 =1.05, the preset rotational speed range of the motor is (15×0.95, 15×1.05), i.e., (14.25, 15.75).
Wherein, the rps is expressed in units of revolutions per second.
In one embodiment of the present application, a gearbox model is utilized to determine a preset rotational speed range of a gearbox, as follows:
the gearbox model is rps 2 ＝rps 1 *G 1 /G 2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein rps 2 Rps is the rated rotational speed of the gearbox 1 G is the rated rotation speed of the motor 1 For the number of teeth of the input shaft gear G 2 Is the number of teeth of the output shaft gear.
For example, when rps 1 ＝25rps，G 1 ＝22，G 2 When the number of the samples is =80,
p 1 ＝0.95，p 2 =1.05, the preset rotational speed range of the gearbox is (6.875×0.95,6.875×1.05), i.e. (6.53,7.22).
In one embodiment of the present application, a predetermined rotational speed range of the non-drive wheel of the pulley is determined using a pulley model as follows:
the pulley model was rps 3 ＝rps 1 *D 1 /D 2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein rps 3 For nominal rotation speed of non-driving wheel of pulley, rps 1 For rated rotation speed of motor, D 1 For driving wheel diameter D 2 Is a non-drive wheel diameter.
For example, when rps 1 ＝25rps，D 1 ＝300mm，D 2 When the value of =600mm,
p 1 ＝0.95，p 2 =1.05, the preset rotation speed range of the non-driving wheel of the pulley is (12.5×0.95, 12.5×1.05), i.e., (11.88, 13.13).
In another embodiment of the present application, a preset rotational speed range of the roller is determined using a roller wheel model, as follows:
the roller model was rps 4 =v/2 pi r; wherein rps 4 The rotation speed of the roller is v, the vehicle speed is v, and r is the radius of the roller.
For example, when v=1500 m/min, r=300 mm,
p 1 ＝0.95，p 2 =1.05, the preset rotation speed range of the roller is (13.27×0.95, 13.27×1.05), i.e., (12.61, 13.93).
According to the preset rotating speed range of the device in the embodiment, a target speed spectrum is determined in the speed spectrum, and the frequency with the largest amplitude is extracted from the target speed spectrum to serve as the rotating frequency of the device.
In one embodiment of the present application, p 1 ＝0.95，p 2 The motor of the standard experimental equipment is set to 1500rpm, and the rotation speed obtained by analyzing the motor by using the speed spectrum self-diagnosis frequency conversion program is 1440rpm, which accords with the preset rotation speed range (1425, 1575), so that the motor is consistent with the actual rotation speed.
Wherein, the rpm is expressed as a unit of revolutions per minute.
Example 3
The acceleration time domain signal of the equipment obtained by the sensor has large noise interference, and the signal is interfered by nonlinear factors such as temperature change, mechanical stress and the like, so that the signal is distorted, the subsequent operations such as Fourier transformation, frequency domain integration and the like are influenced, the measurement result is offset, the actual situation is not met, and the embodiment 3 of the application provides a method for determining the equipment frequency conversion, which is used for preprocessing the acceleration time domain signal uploaded by the sensor on the basis of the embodiment 2, so that the accuracy of the signal quality is improved.
In the method for determining the equipment frequency conversion, before performing fourier transform on the acceleration time domain signal, the method for determining the equipment frequency conversion further comprises the following steps:
preprocessing an acceleration time domain signal; common pretreatment methods include filtering, mean removing, digital compensation, data smoothing, waveform analysis, normalization and the like.
The preprocessing comprises the steps of eliminating noise interference in the acceleration time domain signal by utilizing a band-pass filter, demodulating a high-frequency signal in the acceleration time domain signal into a low-frequency signal, and filtering a direct current component in the demodulated acceleration time domain signal. In the embodiment, a filtering method is adopted to preprocess the acceleration time domain signal; the acceleration time domain signal can be interfered by noise of environment, power supply or other equipment, and the band-pass filter can selectively pass signals in a certain frequency range and inhibit noise of other frequencies, so that the interference on the acceleration time domain signal is reduced; in the subsequent operation of preprocessing, slowly-changing acceleration time domain signals are required to be obtained, and the signals originally containing high-frequency components are demodulated into signals only containing low-frequency components by utilizing a band-pass filter, so that the low-frequency signals can be more obviously displayed; meanwhile, a direct current component exists in the acceleration time domain signal, and the direct current component is caused by factors such as the characteristics, the position or the external temperature change of the sensor, and the like, and the mean value of the signal can be 0 after the direct current component is removed, so that the requirement of subsequent operation is met.
Fourier transforming the acceleration time domain signal comprises: and carrying out Fourier transform on the acceleration time domain signal after the direct current component is removed.
Example 4
A wide variety of chips have emerged in modern integrated circuits, where sampling frequency is a very critical indicator that is commonly used to control timing and data transfer in digital systems. However, in practical application, the sampling frequency of many chips is not accurate, resulting in distortion of digital signals, and embodiment 4 of the present application proposes a method for determining the frequency conversion of a device, and on the basis of embodiment 3, the sampling frequency of a sensor is corrected, so that the sensor can acquire accurate frequency information, and the accuracy of the acquired frequency conversion is improved.
The sampling frequency is an inherent property of the acceleration sensor, and in an operating state, the sampling frequency of the acceleration sensor is configured to be a preset sampling frequency.
The method for determining the device frequency conversion further comprises the following steps:
when the speed spectrum accords with a preset state, the sampling frequency of the acceleration sensor keeps the preset sampling frequency unchanged;
when the speed spectrum does not accord with the preset state, correcting the preset sampling frequency to determine a corrected sampling frequency, and configuring the sampling frequency of the sensor as the corrected sampling frequency. After the acceleration sensor adopts the corrected sampling frequency, the information in the acceleration time domain signal can be accurately captured, the acceleration time domain signal is converted by a program to obtain an accurate noiseless speed spectrum, the accurate conversion frequency is further extracted from the speed spectrum, and the failure type of the equipment is diagnosed by analyzing the conversion frequency and the failure parameters.
When the sampling frequency is larger than the preset sampling frequency, the speed spectrum is distorted; when the sampling frequency is smaller than the preset sampling frequency, the frequency spectrum of the speed spectrum is distorted, aliased and the like. The preset state is the phenomena of distortion, aliasing and the like.
A method for determining a device turn frequency, as shown in fig. 3, correcting a preset sampling frequency to determine a corrected sampling frequency includes: acquiring an experimental acceleration time domain signal of standard experimental equipment through a sensor; filtering direct current components in the experimental acceleration time domain signals, carrying out Fourier transform on the experimental acceleration time domain signals, and determining experimental acceleration frequency spectrums of standard experimental equipment; determining the frequency with the maximum amplitude in the experimental acceleration frequency spectrum as the experimental vibration frequency of standard experimental equipment; and determining the corrected sampling frequency of the sensor according to the fixed vibration frequency of the standard experimental equipment, the experimental vibration frequency and the preset sampling frequency of the sensor.
Specifically, acquiring an experimental acceleration time domain signal of standard experimental equipment uploaded by an acceleration sensor; preferably, the acceleration sensor is installed on standard experimental equipment, and vibration parameters of the standard experimental equipment are set according to the requirement; vibration parameters include amplitude and frequency.
Filtering direct current components in the experimental acceleration time domain signals by using a sampling frequency correction program, carrying out Fourier transform on the experimental acceleration time domain signals, and converting the experimental acceleration time domain signals into experimental acceleration frequency spectrums of frequency domains so as to determine the experimental acceleration frequency spectrums of standard experimental equipment; preferably, the direct current component in the experimental acceleration time domain signal is removed so that the average value of the experimental acceleration time domain signal is 0, the experimental acceleration time domain signal is converted from the time domain to the frequency domain through Fourier transformation, and the components of the signal on different frequencies are obtained, so that an experimental acceleration frequency spectrum is obtained for further analysis.
Extracting the frequency with the maximum amplitude in the experimental acceleration frequency spectrum as the experimental vibration frequency of standard experimental equipment acquired by an acceleration sensor by using a sampling frequency correction program; the main vibration frequency of the standard experimental equipment is determined and used as key data of the correction sampling frequency of the acceleration sensor.
And determining the corrected sampling frequency of the acceleration sensor according to the fixed vibration frequency of the standard experimental equipment, the experimental vibration frequency of the standard experimental equipment and the preset sampling frequency of the acceleration sensor. After the sampling frequency of the acceleration sensor is corrected, the accuracy of the digital circuit is further improved, and the problems of distortion, jitter, aliasing and the like are avoided.
Preferably, determining the corrected sampling frequency of the acceleration sensor according to the fixed vibration frequency of the standard experimental device, the experimental vibration frequency of the standard experimental device, and the preset sampling frequency of the acceleration sensor includes:
calculating a corrected sampling frequency of the acceleration sensor according to the formula (1);
F s ＝a*f 0 /f 1 formula (1)
Wherein a is the fixed vibration frequency of standard experimental equipment, f 0 For the preset sampling frequency f of the acceleration sensor 1 For the experimental vibration frequency of the standard experimental equipment obtained by the acceleration sensor, F s The sampling frequency is corrected for the acceleration sensor.
In one embodiment of the present application, a=1000hz, f 0 ＝6660HZ，f 1 = 967.05HZ, then Finally, as shown in fig. 4 and 5, when the sampling frequency is not corrected, the preset sampling frequency of the acceleration sensor is 6660HZ, the experimental vibration frequency of the standard experimental device acquired by the acceleration sensor is 967.05HZ, and when the sampling frequency is corrected, the corrected sampling frequency of the acceleration sensor is 6887HZ, and the vibration frequency of the standard experimental device acquired by the acceleration sensor is 1000HZ, which is consistent with the natural vibration frequency of the standard experimental device.
Preferably, the acceleration sensor comprises an analog-to-digital converter, and a calibration circuit is arranged outside the analog-to-digital converter; the calibration circuit comprises a power supply, a crystal oscillator, an inductor, a resistor and a plurality of capacitors; the crystal oscillator is electrically connected with the analog-to-digital converter. The crystal oscillator provides stable clock signals for the analog-to-digital converter so as to drive the analog-to-digital converter, ensure that the analog-to-digital converter has accurate clock frequency in the working process, and further improve the precision and accuracy of the sampling frequency.
As shown in fig. 6, the crystal oscillator 1000 includes a power supply pin 10, an output pin 20, and a ground pin 30;
the capacitors include a first capacitor 40, a second capacitor 50, and a third capacitor 60;
the analog-to-digital converter includes a clock input pin 70 that receives a clock signal from the crystal oscillator 1000 for synchronous sampling and converting the analog input signal;
the power supply 90 is connected with the inductor 80 in series and the second capacitor 50 and the third capacitor 60 which are connected in parallel and then grounded, and the power supply 90 is connected with the inductor 80 in series and is electrically connected with the power supply pin 10; the output pin 20 is electrically connected with the clock input pin 70 in series with the resistor 100, and the output pin 20 is grounded after being connected with the resistor 100 and the first capacitor 40 in series; the ground pin 30 is grounded.
The first capacitor 40 is connected in series with the output pin 20, so that a short circuit path to the ground is provided, and a stable working state of the circuit is ensured; the second capacitor 50 and the third capacitor 60 are connected in parallel for filtering noise and spurious signals in the power supply 90; the power supply 90 is connected in series with the inductor 80 to filter noise in the power supply 90; the series resistance 100 of the output pin 20 limits the current of the output pin 20 of the crystal oscillator 1000 and improves the stability of the output signal.
In one embodiment of the present application, the calibration circuit modifies the actual sampling frequency by adjusting the frequency of the clock signal to obtain the target sampling frequency. Specifically, the calibration circuit uses a crystal oscillator as a reference signal, which provides an accurate time reference. The calibration circuit also includes a counter that is used to measure the number of samples that occur per unit time, and the actual sampling frequency is calculated by recording the value of the counter and comparing it to the unit time. The calibration circuit monitors the actual sampling frequency by the counter and compares it with the target sampling frequency, and if the actual sampling frequency deviates from the target sampling frequency, the calibration circuit adjusts the frequency of the clock signal of the crystal oscillator accordingly.
For example, within 1 second. The value recorded by the counter is 9800 times, and the actual sampling frequency is 9800HZ; the target sampling frequency is 10kHZ; it follows that the actual sampling frequency deviates from the target sampling frequency and the calibration circuit adjusts the frequency of the clock signal accordingly such that the actual sampling frequency becomes 10kHZ.
The method for adjusting the clock signal by the calibration circuit comprises the steps of changing the working condition of the crystal oscillator or controlling a feedback element of the crystal oscillator; or alternatively, the first and second heat exchangers may be,
dividing the input clock signal into a lower frequency using a frequency divider, or multiplying the input clock signal into a higher frequency using a frequency multiplier; or alternatively, the first and second heat exchangers may be,
generating a desired clock signal frequency using a frequency synthesizer; or alternatively, the first and second heat exchangers may be,
the frequency of the clock signal is changed by the microprocessor adjusting the corresponding control parameter or the value of the register.
Example 5
After obtaining the frequency conversion of the device, the frequency conversion and some fault parameters (such as fault characteristic frequency and the like) need to be analyzed in combination to diagnose the fault type of the device, and embodiment 5 of the application provides a method for diagnosing the fault type of the device, so as to realize the diagnosis of the fault type of the fault device by analyzing the frequency conversion and the fault parameters.
In an embodiment of the present application, the fault parameter is a fault signature frequency.
The method for determining the equipment frequency conversion provided by the embodiment is used for determining the frequency conversion of the fault equipment, and comparing the frequency conversion with the fault characteristic frequency to diagnose the fault type of the equipment. Wherein, when the equipment has fault or abnormal condition, the frequency spectrum of the vibration signal changes, and the changes may include the newly added frequency peak value or the original frequency peak value. The fault characteristic frequency is the newly added frequency peak value or the change of the original frequency peak value. In some cases, when a problem exists with the device, a frequency peak in the velocity spectrum of the vibration signal related to the frequency of the rotation may be caused, because the vibration signal caused by the fault is related to the rotation of the device. Therefore, in performing fault diagnosis, the frequency conversion of the device and the fault characteristic frequency need to be compared and analyzed to determine the fault type. The fault types include resonance, unbalance, misalignment, looseness, bearing faults, gear faults, and the like.
Preferably, the method for acquiring the fault characteristic frequency comprises the following steps: extracting an envelope curve from the acceleration time domain signal; performing Fourier transform and visualization operation on the envelope curve, and drawing an envelope spectrum; and identifying and extracting frequencies corresponding to the amplitude values meeting preset conditions from the envelope spectrum to serve as the fault characteristic frequencies.
Specifically, as shown in fig. 7, an envelope is extracted from the preprocessed acceleration time domain signal by an envelope fault self-diagnosis program; preferably, the envelope fault self-diagnosis program comprises a Hilbert transform, and an envelope curve representing the amplitude change of the original signal is obtained from the preprocessed acceleration time domain signal by utilizing the Hilbert transform; the envelope is used to show the vibration characteristics of the acceleration time domain signal.
Performing Fourier transformation and visualization operation on an envelope line by using an envelope fault self-diagnosis program, and drawing an envelope spectrum; preferably, the envelope fault self-diagnosis program includes a Fast Fourier Transform (FFT) algorithm, which is used to perform a spectrum analysis on the envelope, convert the time domain signal into a frequency domain signal, and then draw the envelope spectrum using a visualization operation. Wherein the visualization operation may be implemented using a drawing tool library.
And identifying and extracting the frequency corresponding to the amplitude meeting the preset condition in the envelope spectrum by using the envelope fault self-diagnosis program as the fault characteristic frequency. Preferably, the envelope fault self-diagnosis program comprises peak detection, and the peak detection is utilized to analyze the frequency peak value with the largest amplitude in the envelope spectrum, so as to further determine the fault characteristic frequency, and provide guidance for subsequent fault analysis and repair.
In one embodiment of the present application, the SKF6204 is used for the bearing to simulate the failure scenario of the bearing. Wherein, experimental parameters include:
a. the motor speed of the fault standard experimental equipment is 1000rpm, and the actual measured speed is 985rpm;
b. the sampling frequency of the acceleration sensor is 12800HZ;
c. the fourier transform parameter FFTNUM is 65536;
d. the types of bearing failures and their corresponding failure frequency doubling ranges are shown in table 1.
| Fault type | Frequency multiplication | Frequency multiplication range (95% -105%) |
| Cage failure FTF | 0.38 | 0.36～0.40 |
| Rolling element failure BSF | 1.99 | 1.89～2.09 |
| Outer race fault BPFO | 3.05 | 2.89～3.20 |
| Inner ring fault BPFI | 4.95 | 4.70～5.20 |
TABLE 1
As shown in fig. 8, 9 and 10, in the diagnosis process, the fault characteristic frequency of the bearing in the envelope spectrum is 50.59HZ, the rotation frequency of the bearing in the speed spectrum is 16.41HZ, the ratio of the calculated fault characteristic frequency to the rotation frequency is 3.08, and the fault type of the bearing is the outer ring fault as can be obtained from table 1.
In another embodiment of the present application, the failure characteristic frequency of the bearing in the envelope spectrum is 50.59HZ, the rotation frequency of the bearing in the envelope spectrum is 17.19HZ, the ratio of the calculated failure characteristic frequency to the rotation frequency is 2.94, and the failure type of the bearing is an outer ring failure, which is obtained from table 1.
Example 6
Embodiment 6 of the present application provides a frequency conversion determining apparatus, configured to perform the method for determining a frequency conversion of a device in the foregoing embodiment, where the apparatus includes:
The information acquisition unit is configured to acquire acceleration time domain signals of the equipment uploaded by the sensor, and is helpful for collecting vibration information of the equipment and providing a data basis for subsequent analysis;
a fourier transform unit configured to fourier transform the acceleration time domain signal, determine an acceleration spectrum of the device, facilitate a better understanding of frequency characteristics of the signal from the spectrum;
the frequency domain integration unit is configured to perform frequency domain integration on the acceleration frequency spectrum, determine the speed frequency spectrum of the equipment and facilitate further analysis of the frequency characteristics of the signals;
a frequency conversion determining unit configured to determine a target speed spectrum of the device according to a preset rotation speed range and speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum, and using the frequency as the frequency conversion of the equipment for subsequent fault diagnosis and diagnosis.
Embodiment 6 of the present application further provides a fault type diagnosis apparatus for performing the method for diagnosing a fault type of a device in the above embodiment, where the apparatus includes:
the fault characteristic frequency acquisition unit is configured to extract an envelope curve from the acceleration time domain signal, draw an envelope spectrum after carrying out Fourier transform and visualization operation on the envelope curve, and identify and extract a frequency corresponding to an amplitude conforming to a preset condition from the envelope spectrum as a fault characteristic frequency; the fault characteristic frequency of the equipment is obtained, and then the fault characteristic frequency and the frequency conversion are analyzed together to analyze the fault type of the equipment.
And the comparison unit is configured to compare the frequency conversion with the fault characteristic frequency of the fault equipment so as to diagnose the fault type of the equipment, so that the equipment is subjected to predictive maintenance and fault elimination, and the safety and reliability of the equipment are improved.
Through the combined arrangement of the units, the device realizes the intelligent diagnosis of equipment frequency conversion, analyzes the fault type of the equipment and improves the efficiency of fault diagnosis.
Further, the present application also provides a computer device, including a processor and a memory, where the memory stores a computer program executable on the processor, and when the computer program is executed by the processor, the method for determining a device frequency conversion and diagnosing a device fault type provided in the above embodiment is performed.
As shown in FIG. 11, among other things, a computer device may include a processor 1510, a video display adapter 1511, a disk drive 1512, an input-output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, and the memory 1520 may be communicatively connected via a communication bus 1530.
The processor 1510 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided herein.
The Memory 1520 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), a static storage device, a dynamic storage device, or the like. The memory 1520 may store an operating system 1521 for controlling the operation of the electronic device, a basic input output system 1522 for controlling low-level operations of the electronic device. In addition, a web browser 1523, a data storage management system 1524, a device identification information processing system 1525, and the like may also be stored. The device identification information processing system 1525 may be an application program that specifically implements the operations of the foregoing steps in the embodiments of the present application. In general, when the technical solutions provided in the present application are implemented in software or firmware, relevant program codes are stored in the memory 1520 and invoked for execution by the processor 1510.
The input/output interface 1513 is used for connecting with an input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown in the figure) or may be externally connected to the device to provide corresponding functions. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
The network interface 1514 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
The communication bus 1530 includes a path to transfer information between various components of the device (e.g., the processor 1510, the video display adapter 1511, the disk drive 1512, the input-output interface 1513, the network interface 1514, and the memory 1520).
In addition, the electronic device may also obtain information of specific acquisition conditions from the virtual resource object acquisition condition information database, so as to be used for performing condition judgment, and the like.
It is noted that although the above devices illustrate only the processor 1510, the video display adapter 1511, the disk drive 1512, the input-output interface 1513, the network interface 1514, the memory 1520, the communication bus 1530, etc., the device may include other components necessary to achieve proper functioning in a particular implementation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the present application, and not all the components shown in the drawings.
It is to be understood that the above-described embodiments of the present application are merely illustrative of or explanation of the principles of the present application and are in no way limiting of the present application. Accordingly, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present application are intended to be included within the scope of the present application. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (10)
1. The method for determining the equipment frequency conversion is characterized in that the method for determining the equipment frequency conversion is applied to a processing module in an internet of things system, the internet of things system comprises a sensor and the processing module, and the method for determining the equipment frequency conversion comprises the following steps:
responding to a device frequency conversion determining request, and acquiring an acceleration time domain signal of the device uploaded by the sensor;
performing Fourier transform on the acceleration time domain signal to determine an acceleration frequency spectrum of the equipment;
performing frequency domain integration on the acceleration frequency spectrum to determine a speed frequency spectrum of the equipment;
determining a target speed spectrum of the equipment according to a preset rotating speed range and the speed spectrum;
And determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum.
2. The method of determining device turn-around according to claim 1, wherein the sampling frequency of the sensor is configured to be a preset sampling frequency;
the method for determining the equipment frequency conversion further comprises the following steps:
when the speed spectrum does not accord with a preset state, correcting the preset sampling frequency to determine a corrected sampling frequency, and configuring the sampling frequency of the sensor as the corrected sampling frequency;
the correcting the preset sampling frequency to determine a corrected sampling frequency includes:
acquiring an experimental acceleration time domain signal of standard experimental equipment through the sensor;
filtering direct current components in the experimental acceleration time domain signals, carrying out Fourier transform on the experimental acceleration time domain signals, and determining experimental acceleration frequency spectrums of the standard experimental equipment;
determining the frequency with the largest amplitude in the experimental acceleration frequency spectrum as the experimental vibration frequency of the standard experimental equipment;
and determining the corrected sampling frequency of the sensor according to the fixed vibration frequency of the standard experimental equipment, the experimental vibration frequency and the preset sampling frequency of the sensor.
3. The method of determining equipment frequency conversion according to claim 2, wherein the determining the corrected sampling frequency of the sensor based on the fixed vibration frequency of the standard experimental equipment, the experimental vibration frequency, and the preset sampling frequency of the sensor comprises:
calculating a corrected sampling frequency of the sensor according to formula (1);
F s ＝a*f 0 /f 1 formula (1)
Wherein a is the fixed vibration frequency of the standard experimental equipment, f 0 For a preset sampling frequency, f, of the sensor 1 For the experimental vibration frequency of the standard experimental equipment obtained by the sensor, F s For a modified sampling frequency of the sensor.
4. The method of determining a device turn frequency of claim 1 wherein the sensor comprises an acceleration sensor comprising an analog-to-digital converter; a calibration circuit is arranged outside the analog-to-digital converter;
the calibration circuit comprises a power supply, a crystal oscillator, an inductor, a resistor and a capacitor; the crystal oscillator is electrically connected with the analog-to-digital converter;
the crystal oscillator comprises a power supply pin, an output pin and a grounding pin;
the capacitor comprises a first capacitor, a second capacitor and a third capacitor;
The analog-to-digital converter comprises a clock input pin;
the power supply is connected with the second capacitor and the third capacitor in parallel in series and then grounded, and the power supply is connected with the inductor in series and is electrically connected with the power supply pin; the output pin is connected in series with the resistor and the clock input pin in electrical connection, and is grounded after being connected in series with the resistor and the first capacitor; the grounding pin is grounded.
5. The method for determining a rotational frequency of a device according to claim 1, wherein the method for determining a preset rotational speed range comprises:
acquiring a device model and parameters corresponding to the device in the device model;
inputting the parameters to the equipment model, and outputting the rotating speed of the equipment after the equipment model is analyzed;
determining the preset rotating speed range as rps×p by utilizing the rotating speed 1 To rps p 2 ；
Wherein rps is the rotational speed; p is p 1 For the first threshold parameter, p 2 Is a second threshold parameter, and p is more than or equal to 0.9 1 <p 2 ≤1.1。
6. The method of determining device transposition according to claim 1, characterized in that the method of determining device transposition before fourier transforming the acceleration time domain signal further comprises:
Eliminating noise interference of the acceleration time domain signal uploaded by the sensor;
demodulating the acceleration time domain signal after noise interference is eliminated, including: demodulating a high-frequency signal in the acceleration time domain signal after noise interference is eliminated into a low-frequency signal;
and filtering the direct current component in the demodulated acceleration time domain signal.
7. A method of diagnosing a device fault type, the method of diagnosing a device fault type for diagnosing a fault type of a faulty device, comprising:
determining a transition frequency of the malfunctioning device based on the method of determining a transition frequency of a device according to any of claims 1-6;
and comparing the frequency conversion with the fault characteristic frequency of the fault equipment to diagnose the fault type of the equipment.
8. The method for diagnosing a fault type of a device as recited in claim 7, wherein the method for obtaining the fault signature frequency comprises:
extracting an envelope curve from the acceleration time domain signal;
performing Fourier transform and visualization operation on the envelope curve, and drawing an envelope spectrum;
and identifying and extracting frequencies corresponding to the amplitude values meeting preset conditions from the envelope spectrum to serve as the fault characteristic frequencies.
9. A frequency conversion determining apparatus for performing the method of determining a frequency conversion of a device according to any one of claims 1 to 6, characterized in that the frequency conversion determining apparatus comprises:
an information acquisition unit configured to acquire an acceleration time domain signal of the device uploaded by the sensor;
a fourier transform unit configured to fourier transform the acceleration time domain signal, determining an acceleration spectrum of the device;
a frequency domain integration unit configured to perform frequency domain integration on the acceleration spectrum, and determine a velocity spectrum of the device;
a frequency conversion determining unit configured to determine a target speed spectrum of the device according to a preset rotation speed range and the speed spectrum; and determining the frequency corresponding to the amplitude meeting the preset condition as the frequency conversion of the equipment based on the target speed frequency spectrum.
10. A fault type diagnosis apparatus for performing the method of diagnosing a fault type of a device according to any one of claims 7 to 8, characterized in that the fault type diagnosis apparatus comprises:
the fault characteristic frequency acquisition unit is configured to extract the envelope curve from the acceleration time domain signal, draw the envelope spectrum after carrying out Fourier transform and visualization operation on the envelope curve, and identify and extract the frequency corresponding to the amplitude meeting the preset condition from the envelope spectrum as the fault characteristic frequency;
And a comparison unit configured to compare the frequency conversion with a fault characteristic frequency of the fault device to diagnose a fault type of the device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311256616.0A CN117309349A (en) | 2023-09-27 | 2023-09-27 | Method for determining equipment frequency conversion, method and device for diagnosing equipment fault type |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311256616.0A CN117309349A (en) | 2023-09-27 | 2023-09-27 | Method for determining equipment frequency conversion, method and device for diagnosing equipment fault type |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117309349A true CN117309349A (en) | 2023-12-29 |
Family
ID=89296595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311256616.0A Pending CN117309349A (en) | 2023-09-27 | 2023-09-27 | Method for determining equipment frequency conversion, method and device for diagnosing equipment fault type |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117309349A (en) |
-
2023
- 2023-09-27 CN CN202311256616.0A patent/CN117309349A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings | |
| Wang et al. | An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection | |
| Guo et al. | Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition | |
| CN110346591B (en) | Determining machine speed based on vibration spectrogram | |
| Wu et al. | Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization | |
| EP2581724B1 (en) | A method and a system for the purpose of condition monitoring of gearboxes | |
| CN109520738B (en) | Rotating machinery rolling bearing fault diagnosis method based on order spectrum and envelope spectrum | |
| CN110044610B (en) | Gear fault diagnosis method | |
| CN103038620B (en) | The apparatus for diagnosis of abnormality of rolling bearing | |
| CN111665051A (en) | Bearing fault diagnosis method under strong noise variable-speed condition based on energy weight method | |
| CN110987438B (en) | Method for detecting periodical vibration impact signals of hydraulic generator in variable rotating speed process | |
| US11927501B2 (en) | Method and device for monitoring a gear system | |
| CN110163190B (en) | Rolling bearing fault diagnosis method and device | |
| Lin et al. | A review and strategy for the diagnosis of speed-varying machinery | |
| CN111122191B (en) | Equipment health alarm threshold setting method based on EWMA control | |
| Liu et al. | An online bearing fault diagnosis technique via improved demodulation spectrum analysis under variable speed conditions | |
| CN112465068A (en) | Rotating equipment fault feature extraction method based on multi-sensor data fusion | |
| CN103221791A (en) | Method for monitoring a rotary element belonging to a mechanical transmission of a wind turbine | |
| CN117309349A (en) | Method for determining equipment frequency conversion, method and device for diagnosing equipment fault type | |
| CN114486252B (en) | Rolling bearing fault diagnosis method of vector mode maximum envelope | |
| CN115856621A (en) | Asynchronous motor rotor broken bar fault diagnosis method and system | |
| CN116226719A (en) | Bearing fault diagnosis method based on multidimensional steady-state vibration characteristics and related components | |
| Song et al. | Multispectral Balanced Automatic Fault Diagnosis for Rolling Bearings under Variable Speed Conditions | |
| CN115683644A (en) | Double-source beat vibration characteristic identification method for aircraft engine | |
| Cui et al. | Instantaneous Frequency Estimation-Based Order Tracking for Bearing Fault Diagnosis Under Strong Noise |
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 |