CN116577656A - Low-delay high-speed dryer zero-crossing detection system - Google Patents
Low-delay high-speed dryer zero-crossing detection system Download PDFInfo
- Publication number
- CN116577656A CN116577656A CN202310853159.7A CN202310853159A CN116577656A CN 116577656 A CN116577656 A CN 116577656A CN 202310853159 A CN202310853159 A CN 202310853159A CN 116577656 A CN116577656 A CN 116577656A
- Authority
- CN
- China
- Prior art keywords
- air duct
- motor
- speed air
- zero
- abnormal
- 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.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 85
- 230000002159 abnormal effect Effects 0.000 claims abstract description 72
- 238000004458 analytical method Methods 0.000 claims abstract description 25
- 230000005856 abnormality Effects 0.000 claims description 20
- 238000010586 diagram Methods 0.000 claims description 20
- 230000003595 spectral effect Effects 0.000 claims description 13
- 239000003990 capacitor Substances 0.000 claims description 10
- 238000000034 method Methods 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 9
- 230000011664 signaling Effects 0.000 claims description 8
- 238000001228 spectrum Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000012935 Averaging Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010923 batch production Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000013079 data visualisation Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 3
- 238000007619 statistical method Methods 0.000 abstract description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 125000005842 heteroatom Chemical group 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/346—Testing of armature or field windings
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/003—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring position, not involving coordinate determination
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/175—Indicating the instants of passage of current or voltage through a given value, e.g. passage through zero
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/14—Electronic commutators
- H02P6/16—Circuit arrangements for detecting position
- H02P6/17—Circuit arrangements for detecting position and for generating speed information
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
Abstract
The invention discloses a low-delay high-speed air duct zero-crossing detection system, relates to the technical field of fault detection, and aims to solve the problems that the function of the high-speed air duct zero-crossing detection system is too single, and good management and abnormal early warning effects cannot be realized for batch detection in the prior art; the system comprises a detection module, an analysis module, a fault determination module, a management module and a statistics module, wherein the detection module is used for carrying out zero-crossing detection on a high-speed air duct motor; the high-speed wind drum motor is subjected to batch zero-crossing detection, detection data are obtained for statistical analysis and coding, management staff can intuitively judge the zero-crossing detection quality of the high-speed wind drum motor in different batches, automatic early warning can be realized, the occurrence of problems in the zero-crossing detection of the high-speed wind drum motor is avoided, and the classification efficiency of the detection data is greatly improved.
Description
Technical Field
The invention relates to the technical field of fault detection, in particular to a low-delay high-speed wind tunnel zero-crossing detection system.
Background
The high-speed wind tube motor is a key performance element widely applied to the fields of industrial manufacture, aerospace, automobiles and the like. Its advantages are high rotating speed, high efficiency, low noise and small size.
However, an important issue in the design and manufacture of high speed wind tunnel motors is how to accurately detect the motor rotor position. Accurate position detection is critical to controlling the speed and direction of operation of the motor. In high speed motors, zero crossing detection systems are a common approach that can determine the position of the rotor by detecting an electrical signal in the motor.
However, the function of the high-speed air duct zero-crossing detection system in the prior art is too single, and the high-speed air duct zero-crossing detection system cannot realize good management and early warning effects on batch detection, so that a manager cannot clean and intuitively know the fault frequency and trend of the high-speed air duct motor zero-crossing detection.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
The invention aims to solve the problems that the function of the high-speed air duct zero-crossing detection system is too single and the good management and early warning effects cannot be realized for batch detection in the prior art, and provides a low-delay high-speed air duct zero-crossing detection system.
The aim of the invention can be achieved by the following technical scheme:
a low-latency high-speed wind tunnel zero-crossing detection system comprising:
the detection module is used for carrying out zero-crossing detection on the high-speed air duct motor;
the analysis module is used for acquiring and analyzing a current or voltage signal obtained by the zero-crossing detection of the detection module to the high-speed air duct motor;
comparing the three indexes of the frequency component, the power spectral density and the signal to noise ratio obtained by analysis with a preset standard interval, directly generating an abnormal signaling when one of the indexes is abnormal, and marking the high-speed air duct motor;
the fault determining module is used for receiving the abnormal signaling and determining faults caused by the occurrence of the abnormality of the marked high-speed air duct motor; the fault type comprises that a motor rotor is broken in shaft or wire, a motor driving chip does not work normally, a motor capacitor does not work normally, and the connection of a motor tube core plate is abnormal;
the coding module is used for coding the marked abnormal high-speed air duct motor;
the code content comprises fault type, model, batch, position, abnormal time and risk grade;
the risk level obtaining step comprises the following steps:
when the abnormal high-speed air duct motor is determined to be abnormal, collecting the environmental parameters of the high-speed air duct motor;
the environmental parameters of the high-speed air duct motor comprise temperature, humidity, pressure, voltage and current;
meanwhile, acquiring environment parameters of the high-speed air duct motor without abnormality, adopting intermittent acquisition, averaging the acquired environment parameters of the plurality of high-speed air duct motors without abnormality, and removing invalid or abnormal data before averaging to obtain normal values of temperature, humidity, pressure, voltage and current in the environment parameters;
respectively calculating differences between the temperature, humidity, pressure, voltage and current in the environment parameters acquired in real time by the abnormal high-speed air duct motor and normal values of the temperature, humidity, pressure, voltage and current in the environment parameters obtained through calculation so as to obtain temperature differences, humidity differences, pressure differences, voltage differences and current differences; and (3) carrying out normalization processing on the obtained five differences, and substituting the obtained five differences into a formula: to obtain a ring outlier;
comparing the calculated ring outlier with three preset continuous ring outlier intervals to determine which preset ring outlier interval the calculated ring outlier is located in, wherein the three continuous ring outlier intervals correspond to three different risk grades and are divided into low risk, medium risk and high risk;
the management module is used for receiving and analyzing the high-speed air duct motor codes with abnormal marks in the coding module;
the statistics module is used for carrying out statistics and analysis on the data change trend of the zero-crossing detection abnormality of the high-speed air duct motor and realizing data visualization.
Further, the specific operation steps of the detection module for carrying out zero crossing detection on the high-speed air duct motor are as follows:
determining a sensor: selecting a sensor according to the type of a signal to be monitored of the high-speed air duct motor, wherein a Hall sensor, a photoelectric sensor or a current transformer is used in the high-speed air duct motor;
sampling and filtering: sampling the signal output by the sensor using a sampler and converting it into a digital signal, wherein the sampling frequency should be high enough to capture high frequency components;
carrying out digital filtering processing on the signals to eliminate noise and interference; the method comprises the steps of preprocessing by using an analog filter before digital filtering, removing high-frequency noise and baseline drift, further removing noise and interference by using a digital filter, and highlighting interesting signal characteristics;
detecting zero crossing points: comparing the sampled current or voltage signal with a reference level, and detecting a zero crossing point through a comparator when the signal passes through the reference level;
calculating time difference and rotating speed: and recording the time difference between the two zero points by using a microprocessor or an FPGA, and calculating the rotating speed of the high-speed air duct motor.
Further, the specific operation steps of the analysis process of the analysis module are as follows:
converting the acquired current or voltage signal into a waveform diagram, analyzing the waveform diagram, and judging whether an abnormality exists or not;
the specific analysis process of the waveform diagram comprises the following steps:
amplifying and smoothing the converted waveform diagram;
analyzing the frequency components, power spectral density, signal to noise ratio of the waveform using waveform analysis tools such as FFT, wavelet transform;
analyzing the frequency component, the power spectrum density and the signal to noise ratio to judge whether abnormality exists;
frequency components: analyzing whether the frequency component is abnormal or not by analyzing the frequency domain image or the power spectrum curve;
power spectral density: according to the distribution data of the power spectral density, determining the energy in each frequency band in the signal, and if the energy in a certain frequency band is higher than the energy in other frequency bands by more than a preset value, abnormal conditions exist;
meanwhile, the total power value can be calculated and compared with the expected value;
signal-to-noise ratio: determining whether the signal quality is normal or not by calculating the ratio between the signal and the noise, wherein when the signal-to-noise ratio is lower than a preset signal-to-noise ratio threshold value, the abnormal condition of more noise or low signal intensity exists;
when one of the three indexes of the frequency component, the power spectral density and the signal to noise ratio is abnormal, an abnormal signaling is directly generated, and the high-speed air duct motor is marked.
Further, the encoding module encodes other parameters according to the following rules:
obtaining the fault type of the marked abnormal high-speed wind barrel motor by the fault determining module, and marking the broken shaft or broken wire of the rotor of the high-speed wind barrel motor as DD; the abnormal operation of the high-speed air duct motor driving chip is marked as QX; the abnormal operation of the capacitor of the motor of the high-speed wind barrel is marked as DR; the abnormal connection mark of the tube core plate of the high-speed air tube motor is GL;
recording the abnormal time point of the high-speed air duct motor, wherein the time is specific to minutes;
recording the abnormal position of the high-speed air duct motor, dividing the zero-crossing detection position of the high-speed air duct motor into a plurality of areas, and replacing each area by letters.
Further, the analysis process of the management module is as follows:
classifying the marked abnormal high-speed wind barrel motor codes according to different classification criteria; wherein the classification criteria include: fault type classification criteria, batch classification criteria, and risk level classification criteria;
when the fault type classification criteria are used for classifying, the codes of the same fault type are counted, and the fault types are respectively identified as the number of codes of the fault types of four categories, namely, the fault type is that a rotor of the high-speed air duct motor is broken, namely, a broken shaft or broken wire exists, namely, DD, a driving chip of the high-speed air duct motor does not work normally, namely, QX, a capacitor of the high-speed air duct motor does not work normally, namely, DR and a core board of the high-speed air duct motor is connected abnormally;
respectively calculating the total duty ratio of four fault type categories, comparing to obtain a fault type with the highest duty ratio, and marking the fault type with the highest duty ratio;
after sorting according to the high-speed wind barrel motor batch sorting criteria, counting the codes of each batch, respectively identifying the total code quantity of each batch, comparing the high-speed wind barrel motor batches with the highest code quantity, and marking the batches;
after classifying according to a high-speed wind drum motor risk level classification criterion, collecting codes of different risk levels, counting the number of codes of the high risk level, continuously acquiring each position of the codes in the codes of the high risk level, and marking one position with the maximum number of codes, namely an area;
acquiring the position real-time monitoring of the mark, and monitoring the batch production time of the high-risk code at multiple angles;
and then compressing the data of the fault type with the highest mark duty ratio, the high-speed air duct motor batch with the highest code number and the acquired real-time multi-angle monitoring, and transmitting the compressed data to a manager terminal for warning.
Furthermore, the specific operation steps of the statistics module for carrying out statistics and analysis on the data change trend of the abnormal zero-crossing detection of the high-speed air duct motor are as follows:
acquiring a time point of occurrence of faults of zero-crossing detection of each high-speed air duct motor, and presetting a plurality of time periods; converting the time point of the fault of zero-crossing detection of each high-speed air duct motor into a time stamp format;
grouping the preset time periods and summarizing the fault times of each time period;
drawing a line graph by taking a plurality of time periods as an abscissa and the fault times of each time period as an ordinate;
continuously acquiring failure frequency data of a preset time period, and importing the data into the drawn line graph to realize real-time updating of the line graph;
when the included angle between every two fold lines in the line graph is a positive value and three groups of adjacent fold lines continuously appear, generating warning information and sending the warning information to an administrator terminal;
when the included angle of the connection of the two folding lines in the folding line diagram is a positive value and the included angle is larger than 60 degrees, generating early warning information and sending the early warning information to an administrator terminal;
after receiving a receipt of stopping detection by the manager terminal, controlling the whole detection line to stop detection, starting fault alarm, and reserving space for manual fault investigation;
wherein the included angle formed by the broken line in the line diagram by taking the horizontal line as a reference takes positive value and negative value respectively in the upward direction and the downward direction.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, batch zero-crossing detection is carried out on the high-speed air duct motor, statistical analysis is carried out on detection data, and codes are formed, so that management staff can intuitively judge the zero-crossing detection quality of the high-speed air duct motor in different batches, automatic early warning can be realized, the problem of zero-crossing detection of the high-speed air duct motor is avoided, the classification efficiency of the detection data is greatly improved, and convenience is provided for the management staff to the zero-crossing detection of the high-speed air duct motor in a large number;
according to the invention, the important attention objects in the high-speed air duct batch can be marked by classifying the data obtained after the zero-crossing detection of the large-batch high-speed air duct motor according to different classification criteria, so as to provide the reference value of detection for the manager;
according to the invention, the statistics module is used for carrying out statistics analysis on data obtained after zero crossing detection of a large number of high-speed wind tunnel motors, a visual effect of the data is realized for management staff, early warning is carried out on the first time of abnormal trend, and damage is stopped in time.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a general block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present disclosure is for the purpose of describing particular embodiments only, and is not intended to be limiting of the disclosure. As used in the specification and claims of this disclosure, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the term "and/or" as used in the present disclosure and claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As shown in FIG. 1, the low-delay high-speed wind tunnel zero-crossing detection system comprises a detection module, an analysis module, a fault determination module, a management module and a statistics module;
the detection module is used for carrying out zero-crossing detection on the high-speed air duct motor, and the specific detection process comprises the following steps of:
determining a sensor: selecting a proper sensor according to the type of a signal to be monitored of the high-speed air duct motor, wherein a Hall sensor, a photoelectric sensor or a current transformer is used in the high-speed air duct motor;
sampling and filtering: sampling the signal output by the sensor using a sampler and converting it into a digital signal, wherein the sampling frequency should be high enough to capture high frequency components; carrying out digital filtering processing on the signals to eliminate noise and interference; the method comprises the steps of preprocessing by using an analog filter before digital filtering, removing high-frequency noise and baseline drift, and then further removing noise and interference by using a digital filter and highlighting interesting signal characteristics;
detecting zero crossing points: comparing the sampled current or voltage signal with a reference level, and detecting a zero crossing point through a comparator when the signal passes through the reference level;
calculating time difference and rotating speed: and recording the time difference between the two zero points by using a microprocessor or an FPGA, and calculating the rotating speed of the high-speed air duct motor.
The analysis module is used for acquiring and analyzing a current or voltage signal obtained by the zero-crossing detection of the detection module to the high-speed air duct motor;
converting the acquired current or voltage signal into a waveform diagram, analyzing the waveform diagram, and judging whether an abnormality exists or not;
the specific analysis process of the waveform diagram is as follows:
the converted waveform diagram is amplified and smoothed, so that more detailed observation and analysis are facilitated;
analyzing the frequency components, power spectral density, signal to noise ratio of the waveform using waveform analysis tools such as FFT, wavelet transform; analyzing the frequency component, the power spectrum density and the signal to noise ratio to judge whether abnormality exists;
frequency components: analyzing whether obvious frequency component abnormality exists or not by analyzing the frequency domain image or the power spectrum curve;
power spectral density: according to the distribution condition of the power spectrum density, determining the energy in each frequency band in the signal, and if the energy in some frequency bands is far higher than that in other frequency bands, abnormal conditions exist; meanwhile, the total power value can be calculated and compared with an expected value;
signal-to-noise ratio: determining whether the signal quality is normal or not by calculating the ratio between the signal and the noise, wherein when the signal-to-noise ratio is low, abnormal conditions such as excessive noise or insufficient signal strength exist;
when one of the three indexes of the frequency component, the power spectral density and the signal to noise ratio is abnormal, an abnormal signaling is directly generated, and the high-speed air duct motor is marked.
The fault determining module is used for determining faults caused by the occurrence of abnormality of the marked high-speed air duct motor;
when an abnormal signaling generated by the detection module is received, determining the fault type of the marked high-speed air duct motor;
wherein the fault types include:
the rotor of the high-speed wind barrel motor is broken or broken; when the motor rotor is disconnected, the sensor cannot detect the magnetic field change and cannot trigger the zero crossing signal;
the motor driving chip of the high-speed air duct does not work normally; when the driving chip is damaged or abnormal, the output PWM signal is distorted or the zero crossing signal is lost, so that the normal operation of the motor is influenced;
the capacitor of the high-speed air duct motor does not work normally; the capacitor failure can cause instability of the zero-crossing detection circuit, so that zero-crossing signals of the motor cannot be detected correctly;
the tube core plate of the high-speed air tube motor is abnormally connected; the connection of the tube core plate is poor, so that the zero-crossing detection circuit is unstable, and a zero-crossing signal of the motor cannot be detected correctly;
marking the broken shaft or broken line of the rotor of the high-speed wind barrel motor as DD; the abnormal operation of the high-speed air duct motor driving chip is marked as QX; the abnormal operation of the capacitor of the motor of the high-speed wind barrel is marked as DR; and the abnormal connection mark of the tube core plate of the high-speed air tube motor is GL.
The coding module is used for coding the marked abnormal high-speed air duct motor;
obtaining the fault type of the marked abnormal high-speed wind cylinder motor by the fault determining module;
when the abnormal high-speed air duct motor is determined to be abnormal, collecting the environmental parameters of the high-speed air duct motor;
the environmental parameters of the high-speed air duct motor comprise temperature, humidity, pressure, voltage and current;
meanwhile, the environment parameters of the high-speed air duct motor without abnormality are collected, intermittent collection is adopted, the collected environment parameters of the high-speed air duct motor without abnormality are averaged, invalid or abnormal data are removed before the average is obtained, and the accuracy of average calculation is improved, so that the normal values of temperature, humidity, pressure, voltage and current in the environment parameters are obtained;
respectively calculating differences between the temperature, humidity, pressure, voltage and current in the environment parameters acquired in real time by the abnormal high-speed air duct motor and normal values of the temperature, humidity, pressure, voltage and current in the environment parameters obtained through calculation to obtain a temperature difference CW, a humidity difference SC, a pressure difference YC, a voltage difference DC and a current difference LC; and (3) carrying out normalization processing on the obtained five differences, and substituting the obtained five differences into a formula:to obtain a ring hetero value FXC;
comparing the calculated ring outlier FXC with three preset continuous ring outlier intervals, determining which preset ring outlier interval the calculated ring outlier FXC is positioned in, wherein the three continuous ring outlier intervals correspond to three different risk grades respectively and are divided into low risk, medium risk and high risk, and the three risk grades are respectively denoted by numbers 1, 2 and 3;
recording the abnormal time of the high-speed air duct motor, wherein the time is specific to minutes;
recording the position of the abnormality of the high-speed air duct motor, dividing the zero-crossing detection position of the high-speed air duct motor into a plurality of areas, and replacing each area by A1, A2 and A3;
and counting the data, so that the marked abnormal high-speed air duct motor is encoded, wherein the encoded content comprises fault types, high-speed air duct motor types, batches, positions, and abnormal time and risk grades of the high-speed air duct motor.
The management module is used for receiving and analyzing the high-speed air duct motor codes with abnormal marks in the coding module;
classifying the marked abnormal high-speed wind barrel motor codes according to different classification criteria;
after classification according to fault type classification criteria, counting the codes of the same fault type, and respectively identifying the fault types as the number of codes of the fault types of four categories, namely broken shaft or broken line, namely DD, of a rotor of the high-speed air duct motor, QX, abnormal operation of a driving chip of the high-speed air duct motor, DR, abnormal operation of a capacitor of the high-speed air duct motor and abnormal connection of a core board of the high-speed air duct motor;
respectively calculating the total quantity proportion of four fault type categories, and comparing to obtain a fault type with the highest proportion; marking the fault type with the highest duty ratio;
after sorting according to the high-speed wind barrel motor batch sorting criteria, counting the codes of each batch, respectively identifying the total code quantity of each batch, comparing the high-speed wind barrel motor batches with the highest code quantity, and marking the batches;
after classifying according to a high-speed wind drum motor risk level classification criterion, collecting codes of different risk levels, counting the number of codes of the high risk level, continuously acquiring each position of the codes in the codes of the high risk level, and marking one position with the maximum number of codes, namely an area;
acquiring the position real-time monitoring of the mark, and monitoring the batch production time of the high-risk code at multiple angles; and compressing data of the fault type with the highest mark duty ratio, the high-speed air duct motor batch with the highest code number and the acquired real-time multi-angle monitoring, and transmitting the compressed data to a manager terminal.
The statistics module is used for carrying out statistics and analysis on the data change trend of the zero-crossing detection abnormality of the high-speed air duct motor and realizing the visualization of data;
acquiring a time point of occurrence of faults of zero-crossing detection of each high-speed air duct motor, and presetting a plurality of time periods; converting the time point of the fault of zero-crossing detection of each high-speed air duct motor into a time stamp format;
grouping the preset time periods and summarizing the fault times of each time period;
drawing a line graph by taking a plurality of time periods as an abscissa and the fault times of each time period as an ordinate;
continuously acquiring failure frequency data of a preset time period, and importing the data into the drawn line graph to realize real-time updating of the line graph;
when the included angle between every two fold lines in the line graph is a positive value and three groups of adjacent fold lines continuously appear, generating warning information and sending the warning information to an administrator terminal; when the included angle of the two fold lines in the fold line diagram is a positive value and is larger than 60 degrees, generating early warning information and sending the early warning information to the manager terminal, and after receiving a receipt of stopping detection by the manager terminal, controlling the whole detection line to stop detection and starting fault alarm, so that a space is reserved for manual fault investigation;
wherein the included angle formed by the broken line in the line diagram by taking the horizontal line as a reference takes positive value and negative value respectively in the upward direction and the downward direction.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The utility model provides a high-speed dryer zero crossing detection system of low delay which characterized in that includes:
the detection module is used for carrying out zero-crossing detection on the high-speed air duct motor;
the analysis module is used for acquiring and analyzing a current or voltage signal obtained by the zero-crossing detection of the detection module to the high-speed air duct motor;
comparing the three indexes of the frequency component, the power spectral density and the signal to noise ratio obtained by analysis with a preset standard interval, directly generating an abnormal signaling when one of the indexes is abnormal, and marking the high-speed air duct motor;
the fault determining module is used for receiving the abnormal signaling and determining faults caused by the occurrence of the abnormality of the marked high-speed air duct motor; the fault type comprises that a motor rotor is broken in shaft or wire, a motor driving chip does not work normally, a motor capacitor does not work normally, and the connection of a motor tube core plate is abnormal;
the coding module is used for coding the marked abnormal high-speed air duct motor;
the risk level obtaining step comprises the following steps:
when the abnormal high-speed air duct motor is determined to be abnormal, collecting the environmental parameters of the high-speed air duct motor;
meanwhile, acquiring environment parameters of the high-speed air duct motor without abnormality, adopting intermittent acquisition, averaging the acquired environment parameters of the plurality of high-speed air duct motors without abnormality, and removing invalid or abnormal data before averaging to obtain normal values of temperature, humidity, pressure, voltage and current in the environment parameters;
respectively calculating differences between the temperature, humidity, pressure, voltage and current in the environment parameters acquired in real time by the abnormal high-speed air duct motor and normal values of the temperature, humidity, pressure, voltage and current in the environment parameters obtained through calculation so as to obtain temperature differences, humidity differences, pressure differences, voltage differences and current differences; normalizing the five obtained difference values to obtain a ring difference value;
comparing the calculated ring outlier with three preset continuous ring outlier intervals to determine which preset ring outlier interval the calculated ring outlier is located in, wherein the three continuous ring outlier intervals correspond to three different risk grades and are divided into low risk, medium risk and high risk;
the management module is used for receiving and analyzing the high-speed air duct motor codes with abnormal marks in the coding module;
the statistics module is used for carrying out statistics and analysis on the data change trend of the zero-crossing detection abnormality of the high-speed air duct motor and realizing data visualization.
2. The low-delay high-speed wind drum zero-crossing detection system as claimed in claim 1, wherein the specific operation steps of the detection module for zero-crossing detection of the high-speed wind drum motor are as follows:
determining a sensor: selecting a sensor according to the type of a signal to be monitored of the high-speed air duct motor, wherein a Hall sensor, a photoelectric sensor or a current transformer is used in the high-speed air duct motor;
sampling and filtering: sampling the signal output by the sensor using a sampler and converting it into a digital signal, wherein the sampling frequency should be high enough to capture high frequency components;
carrying out digital filtering processing on the signals to eliminate noise and interference; the method comprises the steps of preprocessing by using an analog filter before digital filtering, removing high-frequency noise and baseline drift, further removing noise and interference by using a digital filter, and highlighting interesting signal characteristics;
detecting zero crossing points: comparing the sampled current or voltage signal with a reference level, and detecting a zero crossing point through a comparator when the signal passes through the reference level;
calculating time difference and rotating speed: and recording the time difference between the two zero points by using a microprocessor or an FPGA, and calculating the rotating speed of the high-speed air duct motor.
3. The low-delay high-speed wind tunnel zero-crossing detection system according to claim 1, wherein the analysis module comprises the following specific operation steps of:
converting the acquired current or voltage signal into a waveform diagram, analyzing the waveform diagram, and judging whether an abnormality exists or not;
the specific analysis process of the waveform diagram comprises the following steps:
amplifying and smoothing the converted waveform diagram;
analyzing the frequency components, the power spectral density and the signal to noise ratio of the wavelet transformation analysis waveform diagram by utilizing a waveform analysis tool;
analyzing the frequency component, the power spectrum density and the signal to noise ratio to judge whether abnormality exists;
frequency components: analyzing whether the frequency component is abnormal or not by analyzing the frequency domain image or the power spectrum curve;
power spectral density: determining the energy in each frequency band in the signal according to the distribution data of the power spectral density, and if the energy in one frequency band is higher than the energy in other frequency bands by more than a preset value, determining that an abnormal condition exists;
meanwhile, the total power value is calculated and compared with the expected value;
signal-to-noise ratio: determining whether the signal quality is normal or not by calculating the ratio between the signal and the noise, wherein when the signal-to-noise ratio is lower than a preset signal-to-noise ratio threshold value, the abnormal condition of more noise or low signal intensity exists;
when one of the three indexes of the frequency component, the power spectral density and the signal to noise ratio is abnormal, an abnormal signaling is directly generated, and the high-speed air duct motor is marked.
4. The low-delay high-speed wind tunnel zero-crossing detection system according to claim 1, wherein the encoding module encodes other parameters according to the following rules:
obtaining the fault type of the marked abnormal high-speed wind barrel motor by the fault determining module, and marking the broken shaft or broken wire of the rotor of the high-speed wind barrel motor as DD; the abnormal operation of the high-speed air duct motor driving chip is marked as QX; the abnormal operation of the capacitor of the motor of the high-speed wind barrel is marked as DR; the abnormal connection mark of the tube core plate of the high-speed air tube motor is GL;
recording the abnormal time point of the high-speed air duct motor, wherein the time is specific to minutes;
recording the abnormal position of the high-speed air duct motor, dividing the zero-crossing detection position of the high-speed air duct motor into a plurality of areas, and replacing each area by letters.
5. The low-delay high-speed wind tunnel zero-crossing detection system according to claim 1, wherein the analysis process of the management module is as follows:
classifying the marked abnormal high-speed wind barrel motor codes according to different classification criteria; wherein the classification criteria include: fault type classification criteria, batch classification criteria, and risk level classification criteria;
when the fault type classification criteria are used for classifying, the codes of the same fault type are counted, and the fault types are respectively identified as the number of codes of the fault types of four categories, namely, the fault type is that a rotor of the high-speed air duct motor is broken, namely, a broken shaft or broken wire exists, namely, DD, a driving chip of the high-speed air duct motor does not work normally, namely, QX, a capacitor of the high-speed air duct motor does not work normally, namely, DR and a core board of the high-speed air duct motor is connected abnormally;
respectively calculating the total duty ratio of four fault type categories, comparing to obtain a fault type with the highest duty ratio, and marking the fault type with the highest duty ratio;
after sorting according to the high-speed wind barrel motor batch sorting criteria, counting the codes of each batch, respectively identifying the total code quantity of each batch, comparing the high-speed wind barrel motor batches with the highest code quantity, and marking the batches;
after classifying according to a high-speed wind drum motor risk level classification criterion, collecting codes of different risk levels, counting the number of codes of the high risk level, continuously acquiring each position of the codes in the codes of the high risk level, and marking one position with the maximum number of codes, namely an area;
acquiring the position real-time monitoring of the mark, and monitoring the batch production time of the high-risk code at multiple angles;
and then compressing the data of the fault type with the highest mark duty ratio, the high-speed air duct motor batch with the highest code number and the acquired real-time multi-angle monitoring, and transmitting the compressed data to a manager terminal for warning.
6. The low-delay high-speed wind tunnel zero-crossing detection system according to claim 1, wherein the specific operation steps of the statistics module for counting and analyzing the data change trend of the abnormal zero-crossing detection of the high-speed wind tunnel motor are as follows:
acquiring a time point of occurrence of faults of zero-crossing detection of each high-speed air duct motor, and presetting a plurality of time periods; converting the time point of the fault of zero-crossing detection of each high-speed air duct motor into a time stamp format;
grouping the preset time periods and summarizing the fault times of each time period;
drawing a line graph by taking a plurality of time periods as an abscissa and the fault times of each time period as an ordinate;
continuously acquiring failure frequency data of a preset time period, and importing the data into the drawn line graph to realize real-time updating of the line graph;
when the included angle between every two fold lines in the line graph is a positive value and three groups of adjacent fold lines continuously appear, generating warning information and sending the warning information to an administrator terminal;
when the included angle of the connection of the two folding lines in the folding line diagram is a positive value and the included angle is larger than 60 degrees, generating early warning information and sending the early warning information to an administrator terminal;
after receiving the receipt of stopping detection by the manager terminal, controlling the whole detection line to stop detection and starting a fault alarm;
wherein the included angle formed by the broken line in the line diagram by taking the horizontal line as a reference takes positive value and negative value respectively in the upward direction and the downward direction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310853159.7A CN116577656B (en) | 2023-07-12 | 2023-07-12 | Low-delay high-speed dryer zero-crossing detection system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310853159.7A CN116577656B (en) | 2023-07-12 | 2023-07-12 | Low-delay high-speed dryer zero-crossing detection system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116577656A true CN116577656A (en) | 2023-08-11 |
| CN116577656B CN116577656B (en) | 2023-09-15 |
Family
ID=87545644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310853159.7A Active CN116577656B (en) | 2023-07-12 | 2023-07-12 | Low-delay high-speed dryer zero-crossing detection system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116577656B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004226300A (en) * | 2003-01-24 | 2004-08-12 | Toshiba Corp | Device and method for diagnosing deterioration of rotary electric machine |
| CN103344914A (en) * | 2013-06-26 | 2013-10-09 | 中能电力科技开发有限公司 | Wind turbine generation unit fault early warning method based on normalization |
| CN104242774A (en) * | 2014-09-17 | 2014-12-24 | 中国第一汽车股份有限公司 | Motor phase current prediction and diagnosis method |
| CN104280682A (en) * | 2014-05-09 | 2015-01-14 | 浙江大学 | Motor rotor fault diagnosis method based on field-orientated control |
| CN204422723U (en) * | 2014-11-12 | 2015-06-24 | 南方电网科学研究院有限责任公司 | Wind turbine generator system fault diagnosis system |
| CN108761332A (en) * | 2018-05-08 | 2018-11-06 | 郑州轻工业学院 | A kind of set empirical mode decomposition current diagnostic method of motor broken bar fault |
| CN110907822A (en) * | 2019-10-31 | 2020-03-24 | 珠海格力电器股份有限公司 | Alternating current motor locked rotor fault detection system and method and information data processing terminal |
| CN113391207A (en) * | 2021-04-01 | 2021-09-14 | 国网宁夏电力有限公司检修公司 | Motor fault detection method, medium and system |
| CN215170542U (en) * | 2021-03-29 | 2021-12-14 | 润电能源科学技术有限公司 | Early fault diagnosis system for wind driven generator |
| CN114660398A (en) * | 2022-01-29 | 2022-06-24 | 北京合信锐风新能源发展有限公司 | Wind power plant fault detection method and device |
| CN115389928A (en) * | 2022-07-29 | 2022-11-25 | 北京航天控制仪器研究所 | Hysteresis dynamic pressure motor performance test system and test method |
| CN115467787A (en) * | 2022-08-23 | 2022-12-13 | 湖北青云优信科技开发有限公司 | Motor state detection system and method based on audio analysis |
| CN116044802A (en) * | 2023-03-08 | 2023-05-02 | 淮南矿业(集团)有限责任公司张集煤矿 | State fault monitoring and diagnosing system for mine ventilator |
| US20230194612A1 (en) * | 2021-12-17 | 2023-06-22 | Eaton Intelligent Power Limited | Motor winding fault diagnosis |
-
2023
- 2023-07-12 CN CN202310853159.7A patent/CN116577656B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004226300A (en) * | 2003-01-24 | 2004-08-12 | Toshiba Corp | Device and method for diagnosing deterioration of rotary electric machine |
| CN103344914A (en) * | 2013-06-26 | 2013-10-09 | 中能电力科技开发有限公司 | Wind turbine generation unit fault early warning method based on normalization |
| CN104280682A (en) * | 2014-05-09 | 2015-01-14 | 浙江大学 | Motor rotor fault diagnosis method based on field-orientated control |
| CN104242774A (en) * | 2014-09-17 | 2014-12-24 | 中国第一汽车股份有限公司 | Motor phase current prediction and diagnosis method |
| CN204422723U (en) * | 2014-11-12 | 2015-06-24 | 南方电网科学研究院有限责任公司 | Wind turbine generator system fault diagnosis system |
| CN108761332A (en) * | 2018-05-08 | 2018-11-06 | 郑州轻工业学院 | A kind of set empirical mode decomposition current diagnostic method of motor broken bar fault |
| CN110907822A (en) * | 2019-10-31 | 2020-03-24 | 珠海格力电器股份有限公司 | Alternating current motor locked rotor fault detection system and method and information data processing terminal |
| CN215170542U (en) * | 2021-03-29 | 2021-12-14 | 润电能源科学技术有限公司 | Early fault diagnosis system for wind driven generator |
| CN113391207A (en) * | 2021-04-01 | 2021-09-14 | 国网宁夏电力有限公司检修公司 | Motor fault detection method, medium and system |
| US20230194612A1 (en) * | 2021-12-17 | 2023-06-22 | Eaton Intelligent Power Limited | Motor winding fault diagnosis |
| CN114660398A (en) * | 2022-01-29 | 2022-06-24 | 北京合信锐风新能源发展有限公司 | Wind power plant fault detection method and device |
| CN115389928A (en) * | 2022-07-29 | 2022-11-25 | 北京航天控制仪器研究所 | Hysteresis dynamic pressure motor performance test system and test method |
| CN115467787A (en) * | 2022-08-23 | 2022-12-13 | 湖北青云优信科技开发有限公司 | Motor state detection system and method based on audio analysis |
| CN116044802A (en) * | 2023-03-08 | 2023-05-02 | 淮南矿业(集团)有限责任公司张集煤矿 | State fault monitoring and diagnosing system for mine ventilator |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116577656B (en) | 2023-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103499382B (en) | A kind ofly to merge and the Diagnosis Method of Transformer Faults of image recognition based on vibration data | |
| CN114252749B (en) | Transformer partial discharge detection method and device based on multiple sensors | |
| CN115220396B (en) | Intelligent monitoring method and system for numerical control machine tool | |
| CN112666390B (en) | Electric energy detection metering system | |
| CN117250576B (en) | Current sensor real-time abnormality detection method based on multidimensional sensing data | |
| CN110261745A (en) | A kind of DC Line Fault electric arc detecting device and method based on arc light and current signal | |
| CN113098132A (en) | Improved machine learning fault diagnosis system based on group intelligent optimization | |
| CN116577656B (en) | Low-delay high-speed dryer zero-crossing detection system | |
| CN114036973A (en) | Series arc fault identification method of extreme learning machine based on dynamic online sequence | |
| CN116937818B (en) | High-voltage direct-current power distribution cabinet monitoring system for monitoring inside in real time | |
| CN110991821A (en) | Substation live operation and inspection auxiliary analysis method | |
| CN115081491B (en) | Running state fault diagnosis method for sanitary towel production equipment | |
| CN110726539A (en) | Transformer tap switch mechanical fault detection system based on multiple signal analysis and detection method thereof | |
| CN111157812A (en) | Power equipment fault monitoring method based on electromagnetic induction | |
| CN114722862A (en) | Beam-pumping unit motor fault prediction device and method | |
| CN111980900B (en) | Water pump fault diagnosis method based on multi-source data fusion analysis | |
| CN113513304A (en) | Balance degree detection method based on electric diagram of pumping unit | |
| CN111398810A (en) | Slip ring electric spark detection and diagnosis system and detection and diagnosis method | |
| CN114689321A (en) | Bearing fault diagnosis method and device for wind generating set and electronic equipment | |
| CN215866922U (en) | Fan fault detection circuit and device | |
| CN113433456B (en) | Generator fault diagnosis system and method based on current waveform similarity | |
| CN118243018B (en) | Method and system for identifying tightening of compression nut at shaft end of engine rotor | |
| CN113820570B (en) | Arc discharge fault identification method based on triangular wave width ratio and double threshold setting | |
| CN118387726B (en) | Elevator fault detection method and system based on big data | |
| CN218717649U (en) | Fan parameter control system based on WIFI module connectable network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |