CN107948251B - Android-based remote mechanical fault diagnosis system - Google Patents

Android-based remote mechanical fault diagnosis system Download PDF

Info

Publication number
CN107948251B
CN107948251B CN201711078434.3A CN201711078434A CN107948251B CN 107948251 B CN107948251 B CN 107948251B CN 201711078434 A CN201711078434 A CN 201711078434A CN 107948251 B CN107948251 B CN 107948251B
Authority
CN
China
Prior art keywords
signal
judgment result
acquisition
mobile terminal
module
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.)
Expired - Fee Related
Application number
CN201711078434.3A
Other languages
Chinese (zh)
Other versions
CN107948251A (en
Inventor
周凤星
毛海波
沈春鹏
严保康
卢少武
马娅婕
但峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University of Science and Engineering WUSE
Original Assignee
Wuhan University of Science and Engineering WUSE
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University of Science and Engineering WUSE filed Critical Wuhan University of Science and Engineering WUSE
Priority to CN201711078434.3A priority Critical patent/CN107948251B/en
Publication of CN107948251A publication Critical patent/CN107948251A/en
Application granted granted Critical
Publication of CN107948251B publication Critical patent/CN107948251B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
    • 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
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/021—Gearings
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/028—Acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network

Abstract

The invention provides an android-based remote fault diagnosis system, which is characterized in that: comprises an acquisition end and a mobile end; the acquisition end and the mobile end are both provided with communication modules for data interaction between the devices; the acquisition end also comprises a digital signal acquisition module; the mobile terminal also comprises a digital signal processing module; the digital signal acquisition module acquires various detection data of the equipment to be detected and transmits the detection data to the mobile terminal; and the digital signal processing module processes and analyzes the detection data and then generates a result diagnosis report. The system can be used for rapidly, efficiently and accurately detecting the mechanical faults and other problems of the bearing and the gear box.

Description

Android-based remote mechanical fault diagnosis system
Technical Field
The invention belongs to the technical field of mechanical fault diagnosis, and particularly relates to an android-based remote mechanical fault diagnosis system.
Background
The mechanical failure diagnosis is a technology which can know and master the state of a machine in the operation process, determine the whole or local normality or abnormality of the machine, find a failure and the reason thereof at an early stage and forecast the development trend of the failure. With the great popularization of automation technology in modern industry, the requirements of people on safety and stability in the production process are stricter and stricter. Various portable fault diagnosis instruments on the market have respective characteristics, different instruments meet different use requirements, but have defects in different degrees, the portable fault diagnosis instruments are controlled by a single chip microcomputer, the operation is complex or the functions are single, the intelligent degree is low, and the portable fault diagnosis instruments cannot be communicated with a smart phone.
Disclosure of Invention
The invention aims to overcome the problems and provides a mechanical fault (such as a bearing, a gear box and the like) diagnosis system which is simple to operate, low in price, rich in functions, good in universality and convenient to carry.
The invention provides an android-based remote fault diagnosis system, which is characterized in that: comprises an acquisition end and a mobile end;
the acquisition end and the mobile end are both provided with communication modules for data interaction between the devices;
the acquisition end also comprises a digital signal acquisition module;
the mobile terminal also comprises a digital signal processing module;
the digital signal acquisition module acquires various detection data of the equipment to be detected and transmits the detection data to the mobile terminal;
the digital signal processing module processes and analyzes the detection data to generate a result diagnosis report.
Further, the android-based remote fault diagnosis system provided by the invention also has the following characteristics: namely, the digital signal acquisition module respectively performs the following processing procedures on the detection data:
s1, autocorrelation transformation and wavelet transformation;
s2, performing Hilbert transformation on the transformation result obtained in the first step, and obtaining a signal Hilbert envelope spectrum;
s3, obtaining a low-frequency spectrum of the signal by carrying out FFT (fast Fourier transform) on the envelope spectrum;
s4, obtaining low-frequency characteristic frequency through a low-frequency spectrum;
s5, comparing the calculated characteristic frequency with the characteristic frequency obtained by analyzing the S4 to generate a result diagnosis report;
the characteristic frequency obtained by the calculation is the characteristic frequency of the fault form of the rolling bearing obtained by theoretical calculation; the theoretical calculation is as follows:
(1) inner ring fault characteristic frequency:
Figure BDA0001458445200000021
(2) outer ring fault characteristic frequency:
Figure BDA0001458445200000022
(3) rolling element fault characteristic frequency:
Figure BDA0001458445200000023
wherein Z is the number of the rolling elements, D is the pitch diameter, D is the diameter of the rolling elements, alpha is the contact angle, and f i is the inner ring frequency conversion.
The result diagnosis report includes whether there is a fault and the specific form of the fault.
Further, the android-based remote fault diagnosis system provided by the invention also has the following characteristics: namely, also comprising a remote server;
the remote server comprises a storage module for storing various data from each device;
the remote server also comprises a communication module, and data interaction with other devices is realized through the communication module.
In the invention, the cloud server mainly has the function of storing historical data, and the fault development form of the bearing is judged according to the historical data, so that whether shutdown maintenance is needed or not is judged.
Further, the android-based remote fault diagnosis system provided by the invention also has the following characteristics: that is, the operation flow of the mobile terminal is as follows:
s2-1, initializing the system;
s2-2, judging whether the acquisition terminal is connected or not;
if the judgment result is yes, performing the step 2-3;
if the judgment result is 'no', performing the step 2-6;
regarding the judgment of whether the two devices are connected, in a specific scheme of the invention, a flag bit for judging whether transmission is available is set in a working program or software of the acquisition end, when the mobile end is connected with the acquisition end, the flag bit is automatically set and reset when the mobile end is disconnected, and whether transmission is available is judged by judging whether the flag bit is in a set state.
S2-3, sending the acquisition parameters and the bearing parameters to an acquisition end;
such parameters may include acquisition channels, sampling frequency, number of sampling points, amplification factor, signal processing result of the digital signal processor, and the like, and related parameters of the bearing are reflected in the above fault characteristic frequency calculation formula.
S2-4, waiting for the vibration signal acquisition of the acquisition end, and receiving the data of the acquired original signal through the communication module after the acquisition is finished;
s2-5, processing and analyzing the original signal through a signal processing module, extracting characteristic frequency, and generating a result diagnosis report;
the contents of the diagnostic report mainly include the presence or absence of inner ring failure, outer ring failure, rolling element failure, and mixed failure.
S2-6, judging whether a remote server is connected or not;
if the judgment result is yes, performing the step 2-7;
if the judgment result is 'no', performing the step 2-2;
s2-7, uploading the original signal data and the related diagnosis report to a remote server;
and S2-8, downloading the historical signal data and the related diagnosis report from the remote server.
According to the invention, the remote server (such as a cloud server) mainly has the functions of storing historical data, observing and judging the fault development form of the bearing through the historical data, and judging whether shutdown maintenance is needed or not. The bearing can continue to be used during periods of minor failure and must be taken out of service for maintenance when the failure becomes severe. And when the field detection personnel are not experienced enough or can not judge the situation, the field detection personnel can request more professional detection personnel to download detection data from the cloud end to complete diagnosis.
The process of diagnosing whether the machine needs to be stopped or not can be realized in an automatic software diagnosis mode, and the android-based remote fault diagnosis system further has the characteristics that: namely, also comprises a diagnosis module;
the diagnosis module judges whether shutdown maintenance is needed or not according to the content of the current result diagnosis report;
when the judgment result is 'required', displaying the judgment result on a user interface of the mobile terminal;
and when the judgment result is 'unnecessary', the diagnosis module analyzes the historical signal data and the historical diagnosis report and generates a fault development condition trend report.
Further, the android-based remote fault diagnosis system provided by the invention also has the following characteristics: namely, the acquisition end also comprises a digital signal primary processing module;
the digital signal primary processing module performs time domain analysis and frequency domain analysis on various detection data.
In the invention, a primary processing module on an acquisition end and a data processing module on a mobile end (such as a mobile phone APP) are two sets of systems which are independent from each other, the two systems realize communication by means of wifi and other communication modules and mutually complete respective independent functions, the acquisition end mainly completes signal acquisition, simple signal processing work can be completed through the primary processing module provided by the acquisition end under the condition that the mobile end does not process and analyze signals, and if the mobile end exists, signal data are preferably sent to the mobile end, and the mobile end completes the following processing and analyzing functions.
The method comprises the following specific steps: the android-based remote fault diagnosis system provided by the invention also has the following characteristics: namely, the operation flow of the acquisition end is as follows:
s1-1, initializing the system;
s1-2, confirming acquisition parameters and bearing parameters;
s1-3, starting to collect and store the vibration signal;
s1-4, judging whether the collected signals need to be transmitted to the mobile terminal;
if the judgment result is yes, performing the step 1-7;
if the judgment result is 'no', performing the step 1-5;
s1-5, extracting an original signal, and performing time domain analysis and/or frequency domain analysis on the original signal to obtain a statistical index and/or characteristic frequency; and/or extracting a resonance demodulation signal, and performing frequency domain analysis on the resonance demodulation signal to obtain characteristic frequency;
s1-6, judging the bearing state according to the statistical indexes and/or the characteristic frequency;
s1-7, judging whether the collected signals and/or the judgment result of S1-6 need to be transmitted to the mobile terminal;
if the judgment result is 'yes', performing the step 1-8;
if the judgment result is 'no', entering a standby state;
the standby state is that when the data acquisition is finished, whether the next operation is available or not is waited. For example, an operator directly controls the digital signal processor to perform new data acquisition through the touch screen, or receives a command sent by the mobile terminal and sent to the acquisition terminal to perform the next operation.
S1-8, initializing a communication module by the system;
s1-9, establishing a link with the mobile terminal;
and S1-10, transmitting the acquired signal and/or the judgment result of S1-6 to the mobile terminal through the communication module.
Further, the android-based remote fault diagnosis system provided by the invention also has the following characteristics: that is, the operation flow of the acquisition end may further replace the steps from S1-7 as follows:
s1-7, judging whether the collected signals and/or the judgment result of S1-6 need to be transmitted to the mobile terminal;
if the judgment result is 'yes', performing the step 1-8;
if the judgment result is 'no', performing the step 1-11;
s1-8, initializing a communication module by the system;
s1-9, establishing a link with the mobile terminal;
s1-10, transmitting the acquired signal and/or the judgment result of S1-6 to the mobile terminal through the communication module;
s1-11, judging whether a remote server is connected or not;
if the judgment result is yes, performing the step 1-12;
if the judgment result is 'no', entering a standby state;
and S1-12, uploading various types of signal data and related diagnosis reports to a remote server.
Further, the invention also provides a hardware structure of the android-based remote fault diagnosis system, which is characterized in that: the system comprises acquisition equipment, a smart phone and a cloud end;
the acquisition equipment is used for acquiring detection signal data of the equipment to be detected;
the smart phone is used for counting and analyzing the detection data;
the cloud is used for storing historical detection signal data and historical signal diagnosis reports;
the acquisition equipment, the smart phone and the cloud realize data transmission through the communication module;
the acquisition equipment comprises a screen with an operation interface, a control panel, an acquisition panel and a power supply system which are sequentially installed;
the acquisition board comprises a signal acquisition module which is connected with the sensor;
the control panel comprises a signal processing module;
the acquisition board, the control board and the screen are connected through a socket.
In addition, the android-based remote fault diagnosis system has the following characteristics: namely, the sensor is a piezoelectric acceleration sensor and is attached to a test surface by a magnet fixing method for signal acquisition;
the signal acquisition module comprises an original signal acquisition circuit and a resonance demodulation signal acquisition circuit.
In addition, the android-based remote fault diagnosis system has the following characteristics: namely, the signal acquisition module comprises two signal acquisition circuits: the system comprises an original signal acquisition circuit and a resonance demodulation signal acquisition circuit.
The invention has the following functions and effects:
the system has the characteristics of small volume, convenience in carrying, simplicity in operation, powerful signal processing function, accurate detection result and large storage capacity of the cloud database, can realize the functions of on-site rapid diagnosis and remote expert diagnosis by utilizing the mobile phone APP, and can effectively improve the working efficiency of on-site detection of point inspection personnel.
In the invention, the mobile phone (mobile terminal) and the digital signal processor (acquisition terminal) are cooperated to work: the digital signal processor mainly finishes the acquisition work of signals, and the mobile phone mainly finishes the complex algorithm processing of the signals. The traditional portable fault diagnosis instrument has limited data processing and data storage capacity, so that the detection data are processed and stored mainly through a mobile phone, and the data processing and data storage capacity is improved. The digital signal collector can only complete simple signal processing function under the condition of lacking the mobile phone, and the diagnosis function is limited. Obviously, the volume is small enough, the device is convenient to carry and operate on site, and the device has enough computing power and can complete complex algorithm processing; and the cost of the product is low, and the cost of the portable fault diagnosis instrument in the current market is very high.
Drawings
FIG. 1 is a schematic structural diagram of a system according to the present embodiment;
FIG. 2 is a schematic structural diagram of the case of the present embodiment;
FIG. 3 is a schematic diagram of DSP system flow design;
FIG. 4 is a schematic diagram of a mobile phone APP process design;
FIG. 5, example envelope spectrum;
FIG. 6, Fourier series decomposition of periodic signals;
fig. 7, a flow chart of FFT calculation when N is 8;
fig. 8, results obtained after Hilbert transform.
Detailed Description
As shown in fig. 1 and fig. 2, the android-based remote fault diagnosis system provided by the embodiment includes a box, a smartphone, and a cloud database; a switch, a fan, a sensor and a touch screen are arranged outside the box body; the interior of the box body is of a three-layer structure, the bottom layer is a lithium battery, the middle layer is a collecting plate, and the top layer is a control plate; the acquisition board comprises a MicroUSB interface, a power supply module and a signal acquisition module; the control panel comprises a signal processing module and a wireless network module; the acquisition board, the control board and the touch screen are connected through a socket; the digital signal processor and the smart phone are respectively provided with special fault diagnosis software; historical detection signal data and historical signal diagnosis reports are stored in the cloud database.
The sensor is a piezoelectric acceleration sensor and is attached to a test surface by a magnet fixing method to collect signals.
The touch screen can set sampling parameters including a collecting channel, a sampling frequency, a sampling point number and an amplification factor, and displays a signal processing result of the digital signal processor.
The lithium battery supplies power for double 12V, and the power supply module converts 12V voltage into 3.3V and 5V voltage to supply power for the whole system.
The signal acquisition module comprises two signal acquisition circuits, an original signal acquisition circuit and a resonance demodulation signal acquisition circuit.
The wireless network module is a Wi-Fi module, the chip model is ESP8266, and the wireless network module is used for communicating with a smart phone.
The specific operation mode is as follows: various detection data of the mobile terminal to-be-detected equipment are collected through the box body and are transmitted to the smart phone; and after the smart phone processes and analyzes the detection data, a result diagnosis report is generated and displayed at the front end.
The chip model of the signal processing module is TMS320F28335, is provided with special software, can be directly operated by a touch screen, and simultaneously can be remotely controlled by a smart phone, as shown in FIG. 3, the operation flow of the DSP system software is as follows:
the method comprises the following steps: initializing a system;
step two: inputting acquisition parameters and bearing parameters, including: sampling channel, sampling frequency, sampling point number, amplification factor, bearing related parameters and the like;
step three: starting to collect and store vibration signals;
step four: judging whether transmission is needed;
if yes, entering the ninth step;
if not, entering the step five;
step five: extracting an original signal, and performing time domain analysis and frequency domain analysis on the original signal;
step six: extracting a resonance demodulation signal, and performing frequency domain analysis on the resonance demodulation signal;
step seven: judging the state of the bearing according to the characteristic frequency;
step eight: judging whether transmission is needed, if so, entering a ninth step; if not, the system enters a waiting mode;
step nine: the system initializes the Wi-Fi module;
step ten: establishing a link with a mobile phone APP;
step eleven: and transmitting the data to the mobile phone APP through the Wi-Fi module.
The smart phone is provided with special fault diagnosis software, can finish remote control of the box to collect signals, can extract the characteristic frequency of the signals and the characteristic frequency of the signal envelope spectrum, and can upload and download historical detection signal data and historical signal diagnosis reports from a cloud database. As shown in fig. 4, the operation flow of the mobile phone APP is as follows:
the method comprises the following steps: initializing software;
step two: judging whether a DSP is connected;
if yes, entering a third step;
if not, entering a seventh step;
step three: inputting acquisition parameters and bearing parameters, including: sampling channel, sampling frequency, sampling point number, amplification factor, bearing parameter and the like;
step four: waiting for the DSP to acquire a vibration signal, and receiving data through a Wi-F i module after the acquisition is finished;
step five: carrying out envelope spectrum analysis on autocorrelation transformation and wavelet transformation of an original signal, and extracting characteristic frequency;
step six: checking a fault diagnosis report, comparing the characteristic frequency with a fault characteristic frequency theoretical value obtained by calculating according to bearing parameters, and judging the state of the bearing;
step seven: judging whether the cloud database is connected or not, and if so, entering the step eight; if not, entering the step two;
step eight: uploading local original signal data and a related diagnosis report;
step nine: and downloading historical signal data and related diagnosis reports from a cloud database.
The concrete method for processing and analyzing the detection data in the fifth step is as follows:
s1, autocorrelation transformation and wavelet transformation;
s2, performing Hilbert transformation on the transformation result obtained in the first step, and obtaining a signal Hilbert envelope spectrum;
s3, obtaining a low-frequency spectrum of the signal by carrying out FFT (fast Fourier transform) on the envelope spectrum;
s4, obtaining low-frequency characteristic frequency through a low-frequency spectrum;
and S5, comparing the characteristic frequency of the fault form of the rolling bearing obtained by theoretical calculation with the characteristic frequency obtained by analysis of S4, judging whether the fault exists and the specific form of the fault, and further generating a result diagnosis report.
In one specific example, the envelope spectrum result is shown in FIG. 5, which is expressed by the formula
Inner ring fault characteristic frequency:
Figure BDA0001458445200000111
outer ring fault characteristic frequency:
Figure BDA0001458445200000112
rolling element fault characteristic frequency:
Figure BDA0001458445200000113
calculation shows that under the condition of a rotating speed of 600rpm (the rotating frequency is 10Hz), the theoretical value of the characteristic frequency of the inner ring fault is 71.4Hz, the theoretical value of the characteristic frequency of the outer ring fault is 48.6Hz, and is very close to the peak value in the Hilbert envelope spectrum, and the fault type of the bearing can be judged to be as follows according to the result of the envelope spectrum: and (4) outer ring failure.
In addition, on the APP interface of the smart phone, parameter setting, selection of various algorithms, query of diagnosis reports, uploading and downloading of historical data and the like can be directly performed.
In this embodiment, the various main algorithms related to the DSP system and the smart phone are as follows:
(1) time domain statistical analysis
Time domain diagram analysis is the simplest fault diagnosis method for mechanical fault diagnosis, if a fault occurs, an obvious periodic impact signal appears on a time domain diagram, and the condition that the amplitude or the waveform on the time domain diagram is abnormal is analyzed, so that the preliminary diagnosis of the operation condition of the bearing can be completed.
During the analysis, it is usually necessary to complete the fault diagnosis by means of some statistical indicators. The commonly used dimension field statistical analysis has dimension indicators: mean, variance, mean value, effective value, skewness, and kurtosis. The mean represents the average of the signal amplitude and describes the magnitude of the shift in the center of the signal. The variance represents the degree of dispersion of the signal, and the normal signal variance is usually small. The mean square value and the effective value represent the strength of the signal. The distortion is the degree of asymmetry of the probability distribution of the signal, and the greater the degree of skew of the signal, the greater the distortion. The kurtosis is sensitive to the pulse signal of the signal, and if the probability of occurrence becomes large, the kurtosis will increase rapidly.
These parameters essentially depend on the probability density function of the random signal. The amplitude probability of a signal represents the magnitude of the probability that a random signal will be amplitude generated at a certain instant. And the amplitude probability density of a signal refers to the probability of the signal within a unit amplitude interval, which is a function of the amplitude. For a discrete-time series, the magnitude probability density function of a signal may be defined as follows:
Figure BDA0001458445200000121
where N is the number of data points of the discrete signal, NxThe signal amplitude falls at (x, x +. DELTA.x). According to the probability density function, a calculation formula of other indexes can be obtained:
mean value:
Figure BDA0001458445200000122
variance:
Figure BDA0001458445200000123
root mean square value:
Figure BDA0001458445200000124
skewness:
Figure BDA0001458445200000125
kurtosis:
Figure BDA0001458445200000126
absolute mean value:
Figure BDA0001458445200000127
mean value:
Figure BDA0001458445200000128
in actual diagnosis, statistical indexes are required to accurately reflect the fault condition of a bearing, but when the rotating speed and the load of the bearing change, the change of the statistical indexes is small, and five dimensionless indexes are introduced:
the waveform index is as follows:
Figure BDA0001458445200000131
peak index:
Figure BDA0001458445200000132
pulse index:
Figure BDA0001458445200000133
margin indexes are as follows:
Figure BDA0001458445200000134
kurtosis index:
Figure BDA0001458445200000135
where Xmax denotes the peak value of the signal and Xrms denotes the effective value. The waveform index and the peak index cannot reflect the change of the impact signal, and the kurtosis index, the margin index and the pulse index are slightly better in performance, so the latter three indexes are usually selected to analyze the bearing fault condition.
(2) Fourier transform and FFT algorithm
The time domain analysis is only suitable for comparing typical signals or other obvious faults, and when the relation between information contained in waveform signals and the faults is not obvious, accurate judgment is difficult to perform. According to the fourier series decomposition principle, the periodic signal can be divided into a plurality of simple harmonic signals, the simple harmonic signals have different amplitudes, the decomposed signals are arranged from small to large according to the frequency to obtain the frequency spectrum, as shown in fig. 6, the signals with various frequency components can be visually seen from the frequency spectrum, and the analysis of the signals by using the frequency spectrum is a very important means.
Assuming that x (t) is a periodic signal, then:
Figure BDA0001458445200000136
Figure BDA0001458445200000141
Figure BDA0001458445200000142
Figure BDA0001458445200000143
wherein, a0Is a static component, nw0Is the nth harmonic (n is 1,2, 3, …), T is the fundamental period, w is0Is a fundamental frequency, and the periodic signal x (T) can be expressed by a Fourier series in a (-T/2, T/2) interval according to an Euler formula as follows:
Figure BDA0001458445200000144
when T tends to infinity, a fourier transform can be obtained:
Figure BDA0001458445200000145
Figure BDA0001458445200000146
it can be known from the formula that x (t) is obtained by integrating x (w) in the frequency domain, so that x (w) can truly reflect the changes of the amplitudes and phases of the harmonics with different frequencies.
The computer cannot perform continuous fourier transform on an analog signal, and needs to convert the analog signal into a discrete digital signal and then perform Discrete Fourier Transform (DFT), and the formula is as follows:
Figure BDA0001458445200000147
Figure BDA0001458445200000148
wherein N is 0,1,2, …, N-1; k is 0,1,2, …, N-1; wN＝e-j2π/N。
Observing the discrete Fourier transform equation, it can be seen that N is required in total to calculate all X (N)2The computation workload becomes very large when N is large for the second complex multiplication, and a fast algorithm FFT is proposed for this. The FFT algorithm firstly extracts the data sequence with the length of 2 and the positive integer power step by step according to odd and even, calculates according to the FFT algorithm, finally connects two half sections X (N) and X (N + N/2) obtained by calculation to obtain the whole sequence X (N), and the detailed steps and the calculation formula are as follows:
Figure BDA0001458445200000151
Figure BDA0001458445200000152
Figure BDA0001458445200000153
Figure BDA0001458445200000154
the fourier transform is to represent a periodic signal as the superposition of a series of sinusoidal signals with different frequencies, the frequency spectrum obtained by FFT arranges the sinusoidal signals from small to large in frequency, as shown in fig. 7, the FFT computation flow chart when N is 8, what we see on such frequency spectrum is not the frequency of the periodic impact signal generated by a bearing fault, but a series of different sinusoidal signal frequencies obtained by decomposing the periodic impact signal, and the natural frequencies generated by the bearing and the bearing seat are included, which reflects that the impact signal is modulated by the high-frequency sinusoidal signal on the time domain chart, so that the direct spectrum analysis of the signal does not obtain an ideal result, that is, the frequency component of the impact signal cannot be obtained.
(3) Hilbert envelope spectrum
The Hilbert transform can analyze the original signal containing modulation information by representing it as an analytic signal, and its physical meaning is to delay the phase of all signals by 90 degrees for demodulation purpose, and the Hilbert transform formula is as follows:
Figure BDA0001458445200000161
Figure BDA0001458445200000162
where q (t) is the analytic signal we wish to obtain by transformation, and x' (t) is the Hilbert transformation of x (t). The result obtained after Hilbert transform is shown in fig. 8, which shows a time domain diagram of an analytic signal q (t), and it can be seen that the pulse part of the signal becomes more obvious at this time, and then the analytic signal is enveloped:
Figure BDA0001458445200000163
because the FFT transform is sensitive to standard sinusoidal signals, but not to impulse signals; the envelope spectrum is sensitive to large amplitude impact signals, can highlight periodic impact signals and is insensitive to small amplitude standard sinusoidal signals. Therefore, the influence of the natural frequency can be filtered by performing FFT conversion on the envelope of the analytic signal, and the frequency of the impact signal can be obtained. As shown in fig. 8, the Hilbert envelope spectrum, it can be seen that the characteristic frequency of the low frequency signal is clearly shown, where the peak characteristic frequency is the impact frequency of a particular bearing fault.
The operation mode of combining two system software can find that the portable fault diagnosis instrument is often limited in data processing and data storage capacity, so that the portable fault diagnosis instrument is mainly used for processing and storing detected data through a mobile phone, the data processing and data storage capacity is improved, and the digital signal collector can complete a simple signal processing function under the condition of lacking the mobile phone, but is limited in diagnosis function. In this example, the handset and the digital signal processor are co-operating: the digital signal processor mainly finishes the acquisition work of signals, and the mobile phone mainly finishes the complex algorithm processing of the signals.

Claims (3)

1. A remote fault diagnosis system based on android is characterized in that: comprises an acquisition end and a mobile end;
the acquisition end and the mobile end are both provided with communication modules for data interaction between the devices;
the acquisition end also comprises a digital signal acquisition module;
the mobile terminal also comprises a digital signal processing module;
the digital signal acquisition module acquires various detection data of the equipment to be detected and transmits the detection data to the mobile terminal;
the digital signal processing module processes and analyzes the detection data and then generates a result diagnosis report;
the system also comprises a remote server;
the remote server comprises a storage module used for storing various data from each device;
the remote server also comprises a communication module, and data interaction with other equipment is realized through the communication module;
the operation flow of the mobile terminal is as follows:
s2-1, initializing the system;
s2-2, judging whether the acquisition terminal is connected or not;
if the judgment result is yes, performing the step 2-3;
if the judgment result is 'no', performing the step 2-6;
s2-3, sending the acquisition parameters and the bearing parameters to an acquisition end;
s2-4, waiting for the vibration signal acquisition of the acquisition end, and receiving the data of the acquired original signal through the communication module after the acquisition is finished;
s2-5, processing and analyzing the original signal through a signal processing module, extracting characteristic frequency, and generating a result diagnosis report;
s2-6, judging whether a remote server is connected or not;
if the judgment result is yes, performing the step 2-7;
if the judgment result is 'no', performing the step 2-2;
s2-7, uploading the original signal data and the related diagnosis report to a remote server;
s2-8, downloading historical signal data and relevant diagnosis reports from a remote server;
also includes a diagnostic module;
the diagnosis module judges whether shutdown maintenance is needed or not according to the content of the current result diagnosis report;
when the judgment result is 'required', displaying the judgment result on a user interface of the mobile terminal;
when the judgment result is 'unnecessary', the diagnosis module generates a fault development condition trend report after analyzing the historical signal data and the historical diagnosis report;
the acquisition end also comprises a digital signal primary processing module;
the digital signal primary processing module is used for carrying out time domain analysis and frequency domain analysis on various detection data;
the operation flow of the acquisition end is as follows:
s1-1, initializing the system;
s1-2, confirming acquisition parameters and bearing parameters;
s1-3, starting to collect and store the vibration signal;
s1-4, judging whether the collected signals need to be transmitted to the mobile terminal;
if the judgment result is yes, performing the step 1-7;
if the judgment result is 'no', performing the step 1-5;
s1-5, extracting an original signal, and performing time domain analysis and/or frequency domain analysis on the original signal to obtain a statistical index and/or characteristic frequency; and/or extracting a resonance demodulation signal, and performing frequency domain analysis on the resonance demodulation signal to obtain characteristic frequency;
s1-6, judging the bearing state according to the statistical indexes and/or the characteristic frequency;
s1-7, judging whether the collected signals and/or the judgment result of S1-6 need to be transmitted to the mobile terminal;
if the judgment result is 'yes', performing the step 1-8;
if the judgment result is 'no', entering a standby state;
s1-8, initializing a communication module by the system;
s1-9, establishing a link with the mobile terminal;
and S1-10, transmitting the acquired signal and/or the judgment result of S1-6 to the mobile terminal through the communication module.
2. The android-based remote troubleshooting system of claim 1, further comprising:
the digital signal acquisition module respectively carries out the following processing procedures on the detection data:
s1, autocorrelation transformation and wavelet transformation;
s2, Hilbert transformation is carried out on the transformation result obtained in the step S1, and a signal Hilbert envelope spectrum is obtained;
s3, obtaining a low-frequency spectrum of the signal by carrying out FFT (fast Fourier transform) on the envelope spectrum;
s4, obtaining low-frequency characteristic frequency through a low-frequency spectrum;
s5, comparing the calculated characteristic frequency with the characteristic frequency obtained by analyzing the S4 to generate a result diagnosis report;
the characteristic frequency obtained by calculation is the characteristic frequency of the fault form of the rolling bearing obtained by theoretical calculation;
the result diagnostic report includes whether a fault exists, and the specific form of the fault.
3. The android-based remote troubleshooting system of claim 1, further comprising:
the operation flow of the acquisition end can also replace the steps from S1-7 as follows:
s1-7, judging whether the collected signals and/or the judgment result of S1-6 need to be transmitted to the mobile terminal;
if the judgment result is 'yes', performing the step 1-8;
if the judgment result is 'no', performing the step 1-11;
s1-8, initializing a communication module by the system;
s1-9, establishing a link with the mobile terminal;
s1-10, transmitting the acquired signal and/or the judgment result of S1-6 to the mobile terminal through the communication module;
s1-11, judging whether a remote server is connected or not;
if the judgment result is yes, performing the step 1-12;
if the judgment result is 'no', entering a standby state;
and S1-12, uploading various types of signal data and related diagnosis reports to a remote server.
CN201711078434.3A 2017-11-06 2017-11-06 Android-based remote mechanical fault diagnosis system Expired - Fee Related CN107948251B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711078434.3A CN107948251B (en) 2017-11-06 2017-11-06 Android-based remote mechanical fault diagnosis system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711078434.3A CN107948251B (en) 2017-11-06 2017-11-06 Android-based remote mechanical fault diagnosis system

Publications (2)

Publication Number Publication Date
CN107948251A CN107948251A (en) 2018-04-20
CN107948251B true CN107948251B (en) 2021-02-02

Family

ID=61934318

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711078434.3A Expired - Fee Related CN107948251B (en) 2017-11-06 2017-11-06 Android-based remote mechanical fault diagnosis system

Country Status (1)

Country Link
CN (1) CN107948251B (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110177017B (en) * 2019-06-04 2023-09-19 沃德(天津)智能技术有限公司 Intelligent fault diagnosis system and diagnosis method for speed reducer
CN110779716A (en) * 2019-11-01 2020-02-11 苏州德姆斯信息技术有限公司 Embedded mechanical fault intelligent diagnosis equipment and diagnosis method
CN111189640A (en) * 2020-01-09 2020-05-22 珠海格力电器股份有限公司 Bearing fault monitoring method, monitoring device adopting same and washing machine
CN112350529A (en) * 2020-10-13 2021-02-09 深圳微米自动化科技有限公司 Servo motor maintenance method
CN112432791B (en) * 2020-12-16 2022-11-01 广东省科学院智能制造研究所 Automatic bearing vibration signal fault diagnosis method based on Alan variance
CN113044723A (en) * 2021-03-16 2021-06-29 永富建工集团有限公司 State monitoring, analyzing and fault diagnosing integrated system for hoisting machinery
CN113741378B (en) * 2021-11-04 2022-03-15 西安热工研究院有限公司 Fault analysis method and system for background abnormal point high-frequency acquisition of distributed control system
CN114325104A (en) * 2021-12-30 2022-04-12 长沙巨杉智能科技有限公司 Rock and ore sample impedance remote measurement system and method
CN114353935A (en) * 2022-01-04 2022-04-15 北京英华达软件工程有限公司 Electric quantity controllable wireless vibration acceleration sensor

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001033513A1 (en) * 1999-10-28 2001-05-10 General Electric Company Method and system for remotely managing communication of data used for predicting malfunctions in a plurality of machines
CN1514209A (en) * 2003-08-01 2004-07-21 重庆大学 Rotary machine failure intelligent diagnosis method and device
CN101183262A (en) * 2007-12-13 2008-05-21 上海交通大学 Engineering machinery remote control system and method
CN104535323A (en) * 2015-01-12 2015-04-22 石家庄铁道大学 Locomotive wheelset bearing fault diagnosis method based on angular domain-time domain-frequency domain
CN105425709A (en) * 2015-12-18 2016-03-23 杨斌 Remote monitoring and management system of ball mills based on wireless access into Internet and equipment of same

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001033513A1 (en) * 1999-10-28 2001-05-10 General Electric Company Method and system for remotely managing communication of data used for predicting malfunctions in a plurality of machines
CN1514209A (en) * 2003-08-01 2004-07-21 重庆大学 Rotary machine failure intelligent diagnosis method and device
CN101183262A (en) * 2007-12-13 2008-05-21 上海交通大学 Engineering machinery remote control system and method
CN104535323A (en) * 2015-01-12 2015-04-22 石家庄铁道大学 Locomotive wheelset bearing fault diagnosis method based on angular domain-time domain-frequency domain
CN105425709A (en) * 2015-12-18 2016-03-23 杨斌 Remote monitoring and management system of ball mills based on wireless access into Internet and equipment of same

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
尹爱军."手机移动平台的虚拟轴承诊断仪的开发及应用".《振动、测试与诊断》.2015,第35卷(第5期), *

Also Published As

Publication number Publication date
CN107948251A (en) 2018-04-20

Similar Documents

Publication Publication Date Title
CN107948251B (en) Android-based remote mechanical fault diagnosis system
CN109444566B (en) Electric automobile charging facility detection equipment and method
CN110779716A (en) Embedded mechanical fault intelligent diagnosis equipment and diagnosis method
CN104237977A (en) Automatic meteorological station fault handling system
CN202974666U (en) Virtual instrument detection device for automobile engine
CN102175917A (en) Online nonlinear spectrum analysis and fault diagnosis instrument
CN102375136A (en) System and method for verifying electric energy meter on site
CN104459435A (en) Wiring verification method and device for transformer substation
CN102445343A (en) Flush type bearing failure intelligent diagnosing device based on ARM (advanced RISC machine) and DSP (digital signal processor)
CN101819285B (en) Detection device of nuclear and earthquake instrument system
CN103885097A (en) Integrated test system of meteorological station
CN203163988U (en) Wind turbine generator gear case on-line fault diagnosis device
CN202057357U (en) Integrated test-bed of traction motor
CN203799033U (en) Integrated testing system of meteorological station
CN203965533U (en) A kind of individual line subscriber energy-saving potential and quality of power supply intelligent diagnosing instrument
CN207181570U (en) A kind of electronic surveying integrated system
CN202501918U (en) Hand-held helicopter vibration signal testing and analyzing device
CN205786975U (en) A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault
CN201561980U (en) Automobile engine speed measuring device based on multiple detection
CN205015079U (en) Little wind vibration of transmission line moves monitoring devices
CN104133136A (en) Testing device for rotating table frequency response
CN211042673U (en) Portable signal diagnostic apparatus based on smart phone
CN104165683A (en) Online power capacitor vibration monitoring device
CN201909805U (en) Digital universal meter
WO2022067150A1 (en) Pump performance data logging apparatus

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
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20210202

Termination date: 20211106