CN112067334A - Fault identification method for air conditioner external unit - Google Patents

Fault identification method for air conditioner external unit Download PDF

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CN112067334A
CN112067334A CN202010894731.0A CN202010894731A CN112067334A CN 112067334 A CN112067334 A CN 112067334A CN 202010894731 A CN202010894731 A CN 202010894731A CN 112067334 A CN112067334 A CN 112067334A
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value
normal
domain signal
time domain
air conditioner
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CN112067334B (en
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唐樟春
吴文锋
李征泰
杨宗承
曾鑫
丁林飞
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University of Electronic Science and Technology of China
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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
    • G01M99/00—Subject matter not provided for in other groups of this subclass
    • G01M99/005—Testing of complete machines, e.g. washing-machines or mobile phones
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
    • F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M1/00—Testing static or dynamic balance of machines or structures
    • G01M1/14—Determining imbalance
    • 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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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

The invention discloses a fault identification method for an air conditioner external unit, and belongs to the field of fault diagnosis of air conditioning equipment and mechanical equipment. According to the invention, the vibration signals and the torsion angle signals of the air conditioner in multiple spatial dimensions are obtained, and the obtained multiple signals are processed and analyzed to obtain the result of whether the air conditioner is normal or whether the air conditioner is in any fault state. Compared with the original fault identification method for the air conditioner external unit, the fault identification method for the air conditioner external unit can effectively detect various common working conditions of the air conditioner external unit, and improves the accuracy and efficiency of detection.

Description

Fault identification method for air conditioner external unit
Technical Field
The invention belongs to the field of fault diagnosis of air-conditioning equipment and mechanical equipment, and particularly relates to a fault identification method of an air-conditioning outdoor unit.
Background
At present, the method for detecting the vibration of the air conditioner during production by home appliance manufacturers in China is mainly completed by detecting the touch of personnel, and generally, the magnitude of the vibration amplitude of the air conditioner is sensed by touching with hands, and whether noise is strange or not is heard by ears. This not only requires a lot of effort from the inspector, but also requires the inspector to make a correct subjective judgment based on his own experience, which is obviously difficult and uneconomical for large enterprises.
Disclosure of Invention
The invention designs a fault identification method of an air conditioner outdoor unit aiming at the defects of the background technology.
The invention discloses a fault identification method for an air conditioner outdoor unit, which comprises the following steps:
step 1: respectively collecting vibration signals F in X, Y and Z three-axis directions of an air conditioner outdoor unit with normal working conditionsX,FY,FZAnd torsion angle theta around three axes X, Y and ZX,θY,θZObtaining 6 time domain signals;
step 2: establishing a qualification identification model;
step 2.1: according to the peak value x in a period of time of each time domain signal obtained in the step 1maxValley value xminRoot mean square value xrmsSum variance σ2；
Step 2.2: the histograms of four characteristic values in six dimensional directions can be counted by performing the calculation of step 2.1 on a plurality of samples;
step 2.2.1: when the statistical result is normal distribution, calculating the mean value mu and the standard deviation sigma of the time domain signal, and directly obtaining the maximum value X of the corresponding characteristic value thresholdmaxMinimum value Xmin；
Step 2.2.2: when the time domain signal belongs to the off-normal distribution in the non-normal distribution, firstly calculating the median m and the standard deviation sigma of the time domain signal to obtain the asymmetric coefficient k of the time domain signal, and then calculating the maximum of the threshold value by adopting the following formulaValue XmaxMinimum value Xmin；
Figure BDA0002658078540000011
Figure BDA0002658078540000012
Step 2.2.3: when the time domain signal belongs to other non-normal distributions, the mean value of the time domain signal is calculated to be mu, and the variance is calculated to be sigma2The mean value of the time-domain signal sample is determined to be mu and the variance is determined to be
Figure BDA0002658078540000021
The normal distribution of (a), wherein n is the number of time domain signal acquisition samples; the dispersion range of the time domain signal sample mean is
Figure BDA0002658078540000022
To obtain
Figure BDA0002658078540000023
And
Figure BDA0002658078540000024
then according to
Figure BDA0002658078540000025
And
Figure BDA0002658078540000026
calculating the maximum value X of the thresholdmaxMinimum value Xmin；
And step 3: if the peak value x of 6 time domain signalsmaxValley value xminRoot mean square value xrmsSum variance σ2All fall within respective corresponding (X)max，Xmin) Within the range, the air conditioner external unit is considered to be normal;
and 4, step 4: fourier transform is carried out on the 6 time domain signals, when FXAnd thetaYAxial flow fanWhen the amplitudes of the rotating triple frequency are all larger than the amplitudes of the corresponding fundamental frequencies, the fault of the axial flow fan crack is determined to occur;
when F is presentX，FY，FZ，θYIs greater than normal at an amplitude of 14.25 Hz; confirming the occurrence of the unbalance fault of the axial flow fan;
when F is presentX，FY，FZThe amplitude of the frequency domain signal at 14.25Hz is larger than the normal value, and theta is simultaneously larger than the normal valueXThe amplitude of the frequency domain signal at 42.25Hz is larger than the normal value, and the fault of loosening of the fan base is determined to occur;
when F is presentZ，θX，θYThe amplitude of the frequency domain signal at 50Hz is larger than the normal value, and the fault of lacking the damping block is determined to occur;
when F is presentZHas an amplitude of 50Hz greater than normal and thetaXAnd thetaYThe amplitude of the frequency domain signal at 50Hz is smaller than the normal value, and the fault of lacking soundproof cotton is determined to occur.
The invention has the beneficial effects that: through experimental analysis, compared with the original fault identification method for the air conditioner outdoor unit, the method provided by the invention can effectively detect various common working conditions of the air conditioner outdoor unit, improve the accuracy and efficiency of detection, and provide ideas for various fault identification methods in other fields.
Drawings
FIG. 1 is a diagram illustrating a probability of a normal distribution according to the present invention.
Fig. 2 is a flowchart for constructing a fault identification model of an air conditioner external unit.
Fig. 3 is a diagram of a system constructed by the method.
FIG. 4 is a graph showing the results of various condition diagnostics.
Detailed Description
The invention discloses a fault identification method for an air conditioner outdoor unit, which comprises the following steps of:
step 1, respectively acquiring X, Y and Z three-axis directions of an air conditioner external unit under six working conditions (including normal, axial fan cracks, unbalanced fan, loose fan bracket base, lack of damping block and lack of soundproof cotton) by using a six-dimensional vibration detection methodVibration signal F ofX,FY,FZAnd torsion angle theta around three axes X, Y and ZX,θY,θZAnd the detection of the air conditioner vibration signal is realized by signals in six dimensions.
And 2, collecting 1500 groups of data of six working conditions, wherein 9000 groups of data are collected, taking 700 groups of data with normal working conditions and 100 groups of data with faults of five working conditions, establishing a fault identification model, and using the remaining data for feasibility and accuracy of the subsequent detection and identification model.
And 3, establishing a qualification identification model based on a +/-3 sigma criterion. The method comprises the following steps:
step 3.1: by carrying out comparative analysis on each characteristic parameter of time domain signals of various working condition signals in six dimensional directions, 4 sensitive characteristic parameters are finally selected, wherein the sensitive characteristic parameters are respectively a peak value, a valley value, a root mean square value and a variance, and formulas are respectively shown in (1) to (4). And (3) calculating the 4 characteristic parameters of the 700 groups of data with normal working conditions to establish a qualification recognition model.
xmax＝max(|x(i)|) (1)
xmin＝min(|x(i)|) (2)
Figure BDA0002658078540000031
Figure BDA0002658078540000032
Wherein x (i) is a signal obtained by sampling and discretizing the vibration signal, N is the number of sampling points, and x in formula (4)MVIs a sample mean value of the formula
Figure BDA0002658078540000033
Step 3.2: and carrying out statistical analysis on the calculation results of the plurality of samples. The "+ -3 σ" criterion considers the probability of the data falling on (μ -3 σ, μ +3 σ) to be 99.7%, with the mean value
Figure BDA0002658078540000034
Standard deviation of
Figure BDA0002658078540000035
Wherein n is the number of samples, XiCalculating the interval of each characteristic parameter with the working condition as the normal working condition based on the +/-3 sigma criterion for a certain characteristic parameter value on a certain dimension of a plurality of samples; and judging the four characteristic parameters of the test data to be qualified once in each interval, otherwise, judging the four characteristic parameters to be unqualified.
Step 3.2.1: when the statistical distribution is normal distribution, let X-mu be + -3 sigma, then obtain the threshold value X of the characteristic parametermaxAnd Xmin。
Step 3.2.2: when the distribution obtained by statistics is in abnormal distribution and belongs to the biased distribution in abnormal distribution, the median m and standard deviation sigma of the sample can be calculated, the relative asymmetric coefficient k is found, and then the X of the characteristic parameter can be obtained by applying the +/-3 sigma criterionmaxAnd XminThe formulas are shown as (5) and (6).
Figure BDA0002658078540000041
Figure BDA0002658078540000042
Step 3.2.3: for other non-normal distributions, it can be deduced from the central limit theorem of mathematical statistics that, even if not normal, if the sample mean is μ and the variance is σ2Mean of then sample
Figure BDA0002658078540000043
Also approximately obey a mean of mu and a variance of
Figure BDA0002658078540000044
Is normally distributed, where n is the number of samples, i.e.
Figure BDA0002658078540000045
Sample mean
Figure BDA0002658078540000046
In a dispersion range of
Figure BDA0002658078540000047
At this time, can obtain
Figure BDA0002658078540000048
And
Figure BDA0002658078540000049
then according to
Figure BDA00026580785400000410
And
Figure BDA00026580785400000411
can finally obtain XmaxAnd Xmin。
Step 3.2.4 from the above theory, when the working condition is normal, the intervals of the four characteristic parameters in the six-dimensional direction are shown in table 1, and the unit is (mum/s)2)。
TABLE 1 threshold values of 4 normal characteristic parameters in six-dimensional direction
Figure BDA00026580785400000412
Figure BDA0002658078540000051
And 4, performing qualification detection in the steps, and further constructing an air conditioner external unit fault identification model according to the Fourier spectrum characteristics of all working conditions in six dimensions. By comparison with the normal signal, when an axial fan crack occurs, at F of the waveform of Fourier spectrumXAnd thetaYThe amplitudes of the tripled frequencies (42.5Hz) of the directional fans are all larger than the amplitude of the fundamental frequency (14.25 Hz); when axial flow fanWhen not balanced, FX，FY，FZ，θXThe amplitude at 14.25Hz is greater than normal; when the fan base is loosened, FX，FY，FZThe amplitude at 14.25Hz is larger than normal, while theta is largerXThe amplitude at 42.25Hz is greater than normal; when damping mass is absent, FZ，θX，θYThe amplitude at 50Hz is greater than normal; in the absence of soundproof cotton, FZThe amplitude at 50Hz is greater than normal, while the amplitudes at 50Hz of θ X and θ Y are less than normal.
And 5, programming based on LabView according to the characteristics of time domains and frequency domains of the vibration signals of the outdoor unit of the air conditioner in six dimensions, wherein the front panel is shown in figure 3. Wherein, the time domain and frequency domain waveforms are displayed in the center of software, and the feature calculation values of the right six dimensions are calculated. When the detection result is normal, a green light in a column of the detection result at the lower right corner is lightened and a word of prompting qualification is given; when the detection result is a fault, the green light is in an off state and gives a specific fault prompt, as shown in fig. 4.

Claims (1)

1. A fault identification method for an air conditioner outdoor unit comprises the following steps:
step 1: respectively collecting vibration signals F in X, Y and Z three-axis directions of an air conditioner outdoor unit with normal working conditionsX,FY,FZAnd torsion angle theta around three axes X, Y and ZX,θY,θZObtaining 6 time domain signals;
step 2: establishing a qualification identification model;
step 2.1: according to the peak value x in a period of time of each time domain signal obtained in the step 1maxValley value xminRoot mean square value xrmsSum variance σ2；
Step 2.2: the histograms of four characteristic values in six dimensional directions can be counted by performing the calculation of step 2.1 on a plurality of samples;
step 2.2.1: when the statistical result is normal distribution, the mean value mu and the standard deviation sigma of the time domain signal are calculated, and the maximum value of the corresponding characteristic value threshold is directly obtainedXmaxMinimum value Xmin；
Step 2.2.2: when the time domain signal belongs to the off-normal distribution in the non-normal distribution, firstly calculating the median m and the standard deviation sigma of the time domain signal to obtain the asymmetric coefficient k of the time domain signal, and then calculating the maximum value X of the threshold value by adopting the following formulamaxMinimum value Xmin；
Figure FDA0002658078530000011
Figure FDA0002658078530000012
Step 2.2.3: when the time domain signal belongs to other non-normal distributions, the mean value of the time domain signal is calculated to be mu, and the variance is calculated to be sigma2The mean value of the time-domain signal sample is determined to be mu and the variance is determined to be
Figure FDA0002658078530000013
The normal distribution of (a), wherein n is the number of time domain signal acquisition samples; the dispersion range of the time domain signal sample mean is
Figure FDA0002658078530000014
To obtain
Figure FDA0002658078530000015
And
Figure FDA0002658078530000016
then according to
Figure FDA0002658078530000017
And
Figure FDA0002658078530000018
calculating the maximum value X of the thresholdmaxMinimum value Xmin；
And step 3: if the peak value x of 6 time domain signalsmaxValley value xminRoot mean square value xrmsSum variance σ2All fall within respective corresponding (X)max，Xmin) Within the range, the air conditioner external unit is considered to be normal;
and 4, step 4: fourier transform is carried out on the 6 time domain signals, when FXAnd thetaYWhen the amplitudes of the rotational frequency triples of the axial flow fan are all larger than the amplitudes of the corresponding fundamental frequencies, the fault of the axial flow fan crack is determined to occur;
when F is presentX，FY，FZ，θYIs greater than normal at an amplitude of 14.25 Hz; confirming the occurrence of the unbalance fault of the axial flow fan;
when F is presentX，FY，FZThe amplitude of the frequency domain signal at 14.25Hz is larger than the normal value, and theta is simultaneously larger than the normal valueXThe amplitude of the frequency domain signal at 42.25Hz is larger than the normal value, and the fault of loosening of the fan base is determined to occur;
when F is presentZ，θX，θYThe amplitude of the frequency domain signal at 50Hz is larger than the normal value, and the fault of lacking the damping block is determined to occur;
when F is presentZHas an amplitude of 50Hz greater than normal and thetaXAnd thetaYThe amplitude of the frequency domain signal at 50Hz is smaller than the normal value, and the fault of lacking soundproof cotton is determined to occur.
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