Rolling bearing state detection method, detector and system based on LQ value

Abstract

The invention relates to an LQ value-based rolling bearing state detection method, a detector and a system. The method comprises the following steps of: acquiring a vibration signal of a rolling bearing; carrying out integration processing on the vibration signal to obtain a speed signal; performing band-pass filtering with upper and lower limit frequencies on the speed signal to obtain a rolling bearing fault signal; according to the rolling bearing fault signal, calculating a vibration speed effective value to obtain an LQ value; and determining the state of the rolling bearing according to the LQ value. Compared with the prior art, the method has the advantages that analysis by professionals is not needed; the detection is convenient, rapid and accurate; the rotating speed, the power and the bearing size almost have no influence on the measurement result; and early fault information of the bearing can be easily obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of bearing state detection, and in particular to a rolling bearing state detection method, a detector and a system based on an LQ value. Background Art

[0002] Rolling bearings are the main basic components of rotating equipment such as pumps, fans, and motors, and are also the main wearing parts. They are widely used in industry, especially in some process industries such as petrochemicals. They are the key monitoring objects of daily production and operation. Failure of rolling bearings can cause equipment damage, and in serious cases, it can cause fires and other serious accidents, resulting in huge direct and indirect losses.

[0003] Rolling bearing fault detection technology has developed rapidly in the field of rotating equipment condition monitoring and fault diagnosis technology. Various new technologies have been promoted and applied. As early as the 1960s, new detection technologies have been continuously developed and introduced internationally. Currently, 9 different types of rolling bearing detection technologies and methods are used on site, each with its own characteristics. Some technologies have obvious defects, are cumbersome to use, have vague analysis conclusions, and have low accuracy, making them difficult to be recognized by technical personnel on industrial production sites.

[0004] Sliding bearings can maintain long-term normal operation mainly due to the presence of lubricating oil film, which separates the shaft and bearing. As long as the boundary conditions such as the pressure, temperature and oil quality of the lubricating oil supply system, and the fatigue of the shaft and bearing materials remain unchanged, the bearing life can be extended indefinitely. However, since the balls of rolling bearings are in contact with the inner and outer rings, the balls roll and slide in the inner and outer slideways when the bearings are working. The lubricating oil only plays a role in reducing friction. Since the fatigue of metal materials has limits, rolling bearings have a life cycle. In theory, there is a rolling fatigue life, which is the total number of revolutions in the process in which the bearing raceway surface and the rolling element surface are subjected to alternating stress due to rotation, causing material fatigue until local fatigue peeling occurs. The rolling fatigue life is closely related to lubrication. Therefore, a necessary condition for the normal operation of rolling bearings is to maintain adequate lubrication. Without sufficient lubrication, rolling bearings will be damaged in a short time (within 3 to 5 minutes). This is why shaft burning, shaft sticking and shaft breakage often occur.

[0005] The causes of rolling bearing damage during operation: improper assembly, poor lubrication (intrusion of water and foreign matter, corrosion), large vibration and overload. The main failure forms of rolling bearings are: wear, pitting, fatigue peeling, plastic deformation, rust, fracture, bonding and cage damage, etc. Rolling bearing failure has three characteristics: large vibration, high temperature and loud noise, but in actual production and use, it is found that sometimes the vibration value (vibration speed) of the bearing seat does not exceed the standard and the temperature is not high (<65℃), and the rolling bearing will also fail. Sometimes the rolling bearing uses an alarm for excessive temperature (>85℃), but the lubrication is sufficient, the vibration value is not large, and the bearing can still operate normally. In addition, on-site technicians have reported that judging by listening to the noise will bring large errors. , leading to decision-making doubts. All these problems of using the three major characteristics to judge bearing fault errors arise, because the three major characteristics are only qualitative judgments and cannot be quantitatively analyzed, so the accuracy is low. The existing rolling bearing fault monitoring methods are mainly vibration signal analysis and oil pollution analysis (ferrography analysis). Ferrography analysis is relatively cumbersome and not suitable for industrial field applications. The main vibration signal analysis methods are the following: peak detection method, amplitude detection method, spectrum analysis method, shock pulse method (SPM), vibration peak energy, envelope demodulation analysis method, PeakVue peak analysis method and bearing fault factor DEF.

[0006] Taking the envelope demodulation analysis method as an example, when there is local damage or defects in the bearing, a sudden attenuated impact pulse force is generated during operation, which excites the high-frequency natural vibration of the bearing. This high-frequency natural vibration acts as the carrier of the bearing vibration, making the final vibration waveform of the bearing appear as a complex amplitude modulation wave. The frequency component of the modulation wave contains the fault frequency corresponding to the defect. Through demodulation technology, the defect information of the bearing can be separated from the complex amplitude modulation vibration signal.

[0007] The envelope demodulation analysis method introduces profound mathematical theoretical formulas and spectra, which have specific and high requirements for the user's technical ability, machine environment such as measurement points, timing intervals and data collection. In terms of on-site maintenance, the inner ring, outer ring, ball and cage of the rolling bearing are damaged and replaced as a whole. There is no possibility of individual replacement. In the actual industrial field application process, it has not received good feedback, and its application is narrow and cannot be promoted. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a rolling bearing state detection method, detector and system based on LQ value which does not require analysis by professionals and is convenient, fast and accurate.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A rolling bearing state detection method based on LQ value comprises the following steps:

[0011] Obtain vibration signals of rolling bearings;

[0012] Performing integration processing on the vibration signal to obtain a speed signal;

[0013] Performing bandpass filtering with upper and lower frequency limits on the speed signal to obtain a rolling bearing fault signal;

[0014] According to the rolling bearing fault signal, the effective value of the vibration velocity is calculated to obtain the LQ value;

[0015] The condition of the rolling bearing is determined based on the LQ value.

[0016] Furthermore, the frequency of the rolling bearing fault signal obtained by bandpass filtering is within the range of 600 Hz-10 kHz.

[0017] Further, the state of the rolling bearing is determined according to the LQ value as follows:

[0018] Determine in advance a good state threshold and a fault state threshold of the LQ value based on historical field operation data of the rolling bearing, wherein the good state threshold is less than the fault state threshold;

[0019] If the measured LQ value is less than the good state threshold, it is judged that the rolling bearing is in a good bearing state and lubrication state;

[0020] If the measured LQ value is greater than the fault state threshold, it is determined that the rolling bearing is in a poor bearing state and the rolling bearing is replaced.

[0021] Furthermore, the method further comprises: if the measured LQ value is between a good state threshold and a fault state threshold, performing lubrication improvement processing on the rolling bearing.

[0022] Furthermore, the value of the good state threshold is within the range of 1.0-1.3, and the value of the fault state threshold is within the range of 1.8-2.0.

[0023] Furthermore, the vibration signal of the rolling bearing is a vibration acceleration signal of the rolling bearing collected by a vibration acceleration sensor.

[0024] Furthermore, the method obtains the effective value of the vibration velocity according to the rolling bearing fault signal based on an instrument type analog and digital signal optimization algorithm.

[0025] Furthermore, the instrument type analog and digital signal optimization algorithm is a root mean square calculation method.

[0026] Furthermore, the method uses a filter to perform bandpass filtering on the speed signal with upper and lower frequency limits, and removes some signal points with unstable amplitude responses just after the speed signal enters the filter.

[0027] Furthermore, the method also includes transmitting the determined rolling bearing state to a remote database via a data interface for data storage and analysis.

[0028] The present invention also provides a bearing state detector that adopts the rolling bearing state detection method based on LQ value as described above, characterized in that it includes a vibration acceleration sensor, an infrared temperature sensor and a processor, the processor being respectively connected to the vibration acceleration sensor and the infrared temperature sensor, and being used to execute the rolling bearing state detection method based on LQ value as described above based on the detection value of the vibration acceleration sensor, and to perform infrared temperature display according to the detection value of the infrared temperature sensor.

[0029] The present invention also provides a network version software system using the bearing status detector as described above, characterized in that it includes the bearing status detector as described above and a data server, the data server stores network version analysis software, and the bearing status detector is remotely connected to the data server via a data interface.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] (1) The present invention selects and extracts vibration signals in the frequency band above 600 Hz and below 10 kHz as basic signals after integrating the vibration signals from the vibration acceleration sensor. The vibration signals in this interval contain rich rolling bearing fault information, and all vibrations caused by mechanical faults such as imbalance and misalignment are suppressed by filtering in this frequency band, thereby further determining the effective value of the vibration velocity as the LQ value to characterize the running state of the rolling bearing; and the calculated LQ value also reflects the lubrication state of the bearing. Poor lubrication will lead to an increase in the LQ value, and the machine speed, power and bearing size have almost no effect on the LQ value; thus, the present invention has the following advantages:

[0032] The operating status of the bearing can be directly obtained without the need for analysis by professionals;

[0033] The detection is convenient, fast and accurate, and can be used as the main means of monitoring rolling bearings;

[0034] Speed, power and bearing size have little influence on the measurement results;

[0035] Early bearing failure information can be easily obtained.

[0036] (2) The present invention does not further study the frequency division characteristics of rolling bearing parts, and does not explore the corresponding logical relationship between complex signals and specific parts, especially diagnosing the location and degree of mechanical damage, and finding a safe and healthy operating life. This will take a lot of ineffective time and energy. Taking the envelope demodulation analysis method as an example, it introduces profound mathematical theoretical formulas and spectra. In this way, it has specific and high requirements for the user's technical ability, machine environment such as measurement points, timing intervals and data collection. Specifically, in terms of on-site maintenance, the inner ring, outer ring, ball and cage of the rolling bearing are damaged. The four major parts are replaced as a whole, and there is no possibility of individual replacement. In the actual industrial field application process, no good feedback has been obtained, and the application is narrow and cannot be promoted;

[0037] The present invention only considers the running state of the rolling bearing and provides judgment, and has a high fault accuracy rate. It is well received by on-site personnel and is rapidly promoted.

[0038] (3) There are three characteristics of rolling bearing operation failure: large vibration, loud noise, and high temperature. Large vibration does not necessarily exceed the standard. Loud noise is sometimes difficult to distinguish due to the large background noise on site. It requires experienced personnel to distinguish it by listening with a listening stick, but the error is large and it is also greatly affected by the physiological and psychological influence of the test personnel at the time. High temperature generally alarms at 65°C, but sometimes the bearing (imported bearing) runs well at 80 or 90°C. Therefore, based on the above three characteristics, the general method can only qualitatively judge the quality of the bearing operation status but cannot determine whether it needs to be stopped for repair. The method of the present invention can be used for qualitative analysis and timely detection of early faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic flow chart of a rolling bearing state detection method based on LQ value provided in an embodiment of the present invention;

[0040] Figure 2 is a first-order integral structure diagram of a filter provided in an embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of a frequency response curve of a first-order integrator of a filter provided in an embodiment of the present invention;

[0042] Figure 4 A schematic diagram of an IIR high-pass filter calculation structure provided in an embodiment of the present invention;

[0043] Figure 5 A schematic diagram of a step response of an IIR filter provided in an embodiment of the present invention;

[0044] Figure 6 The present invention provides a schematic diagram of the structure of a network version software system based on a bearing status detector in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0046] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0048] Example 1

[0049] The rolling bearings used in general-purpose pumps (pumps, motors, fans) in process industries usually have 8 to 12 balls (spheres, cylinders) and a rotation speed of 1500 / 3000rpm. Theoretical calculations, experiments and actual tests show that the fault spectrum generated is concentrated in the high-frequency range, and the vibration spectrum contains a large number of rich, broad and complex high-frequency component signals. Previous rolling bearing detection technologies have intercepted signals of different frequency bands as basic signals for technical processing, combined with certain analysis methods, and proposed their own diagnostic analysis theories.

[0050] According to the statistical understanding of the technical parameters of general equipment containing rolling bearing components in the petrochemical industry, most of the speeds are 1500 / 3000 rpm, and a small number of dynamic equipment are 700-1200 rpm. Most of the rolling bearings in use have 8-12 balls (spheres, cylinders). Based on the classified collection of common machine pump vibration failures, common machine pump failures include: imbalance, misalignment, soft foot of foundation, wear and running of rolling bearings, vacuum or cavitation (low flow), fluid discontinuity, friction of impeller mouth ring, loose anchor bolts, pipeline excitation, seal leakage, etc.

[0051] In this regard, Figure 1 As shown, the present invention proposes a rolling bearing state detection method based on LQ value, comprising the following steps:

[0052] S1: Obtain the vibration signal of the rolling bearing;

[0053] S2: Integrate the vibration signal to obtain the speed signal;

[0054] S3: Perform band-pass filtering on the speed signal with upper and lower frequency limits set to obtain a rolling bearing fault signal;

[0055] S4: Calculate the effective value of the vibration velocity according to the rolling bearing fault signal to obtain the LQ value;

[0056] S5: Determine the condition of the rolling bearing based on the LQ value.

[0057] If the above method is used to optimize the pump detection technology for petrochemical field operation, the above method is equivalent to:

[0058] 1) According to the rolling bearing fault type, confirm the energy form generated mainly by wear fault and the specification range based on the vibration velocity energy as the signal object;

[0059] 2) Based on the signal objects based on the operating parameters of a large number of pumps at the petrochemical site, the rolling bearing fault signal is extracted by setting the bandpass filter signal with upper and lower frequency limits;

[0060] 3) Based on the instrument type simulation and digital signal optimization algorithm, obtain the characteristic value of the rolling bearing operation state;

[0061] 4) Based on a large amount of rolling bearing field operation data statistics over the years, extract representative operating status characteristics and safety margins, clarify the corresponding rolling bearing operation quality, propose judgment indicators with practical guidance, continuously screen and correct through user feedback information, improve the accuracy of rolling bearing fault judgment, predict the occurrence and progress of faults in advance, and take timely measures.

[0062] Preferably, before the integration processing in step S2, the vibration signal is also subjected to high-pass filtering. The purpose of the high-pass filtering is to remove low-frequency interference in the signal. Because the integration processing is more sensitive to low-frequency signals, by eliminating low-frequency interference, a good foundation is laid for the next integration processing process. The cut-off frequency is 2 Hz.

[0063] The integration process in step S2 is specifically described as follows:

[0064] The speed signal is obtained by integrating the vibration signal once.

[0065] The conventional method for definite integral of discrete signals is the area method, that is, calculating the area enclosed by the curve connecting the signal points and the x-axis. Simpson's formula is a widely used numerical integration method. However, for some signals, Simpson's formula may not converge. Therefore, a special method is used for integration.

[0066] For a sine or cosine signal, its integration result is a cosine or sine signal with the same frequency as itself, and the amplitude is the amplitude before integration. Where 2πf=w.

[0067] Therefore, after the vibration signal is integrated once, the amplitudes of different frequency components will have corresponding changes. The first-order integrator is regarded as a system. The frequency response curve of this system is a branch of the hyperbola. Therefore, a filter can be designed. As long as the value of the frequency response curve of this filter at the frequency f is The vibration signal can be considered as the superposition of multiple sine waves of different frequencies, so passing the vibration signal through this filter is equivalent to performing I integration. Figure 2 The figure shows the calculation structure diagram of the I-th integration, which is the IIR filter of type I.

[0068] The specific calculation method is as follows:

[0069] y(n)=α·x(n)+α·x(n-1)+γ·y(n-1)

[0070] Figure 2 There are two parameters α and γ in the equation, and the calculation method is as follows:

[0071]

[0072] Where k is a double type parameter to be determined. Indicates the time interval between two sampling points. In the product, the coefficient k = 10, Fs = 25600, so the coefficient is:

[0073] α=19.530869E-6

[0074] γ=0.99996094

[0075] The corresponding filter frequency response is Figure 3 As shown, the red line is the frequency response requirement that should be met in theory, and the blue circle is the actual frequency response of the filter. It can be seen that the filter basically meets the requirements.

[0076] The bandpass filtering process in step S3 is specifically described as follows:

[0077] Bandpass filtering is based on digital filtering algorithms, which are used to extract signals in a specific frequency band and filter out interference and noise, including IIR (infinite impulse response) filters and FIR (finite impulse response) filters. IIR filters have small computational complexity and high efficiency, but the phase is nonlinear; FIR filters are linear phase, but the filter order and complexity are high. In this embodiment, an IIR filter is used, and the IIR filter has the following characteristics:

[0078] (1) IIR is divided into the antecedent coefficient and the posterior coefficient. The length of the antecedent coefficient is 1.5 times the length of the posterior coefficient.

[0079] (2) The longer the coefficient is, the higher the order is, which means the transition zone is steeper and the algorithm has more computational complexity.

[0080] (3) Filter design and selection, including Butterworth type, Chebyshev type I, Chebyshev type II, etc.

[0081] The basic structure of IIR high-pass filtering is as follows Figure 4 As shown, this is a direct type II, and the structure is normal to the delay.

[0082] The specific implementation logic is as follows:

[0083] (1) Initialize temporary variables, F = [0,0,0].

[0084] (2) Calculate the output value according to the following formula:

[0085] F(1)=x(n)-a1·F(2)-a2·F(3)

[0086] y(n)=b0·F(1)+b1·F(2)+b2·F(3)

[0087] F(3)=F(2)

[0088] F(2)=F(1)

[0089] In practice, multiple such IIR filters can be cascaded together.

[0090] As a signal processing system, the filter has a transition period after the signal enters the system. During this transition period, the amplitude response is unstable, so the filtering result generated during this period needs to be removed.

[0091] The method to determine the number of removal points is to input a unit step signal to the filter system. Figure 5 In the step response shown, after 0.1482 seconds, the response decays to 0.001. The sampling rate is 25600 SPS, so the number of points that should be removed is equal to:

[0092] 0.1482×25600=3794 points

[0093] That is, after 3794 points, the filter's effect on the amplitude is less than 0.1%.

[0094] In this embodiment, the filter for extracting the frequency band above 600 Hz is an Inverse Chebyshev filter, which takes the transition band into consideration. The corresponding filter coefficients are as follows:

[0095] Filter type :Butterworth Highpass(4order)

[0096] Cutoff frequency:600.00Hz(attenuation@-0.10dB)

[0097] Stop frequency:100.00Hz(attenuation@-40.00dB)* /

[0098] float S1_F600_ReverseCoeffs[S1_F600_REVERSCOEFFS_NUM]=

[0099] {

[0100] -1.835480E+0,

[0101] 8.432999E-1,

[0102] -1.923774E+0,

[0103] 9.319704E-1

[0104] };

[0105] float S1_F600_ForwardCoeffs[S1_F600_FORWARDCOEFFS_NUM]=

[0106] {

[0107] 9.415557E-1,

[0108] -1.883111E+0,

[0109] 9.415557E-1,

[0110] 9.415557E-1,

[0111] -1.883111E+0,

[0112] 9.415557E-1

[0113] };

[0114] The calculation process of the effective value of the vibration velocity in step S4 is specifically as follows:

[0115] By calculating the root mean square of the rolling bearing fault signal, the above-mentioned effective value of the vibration velocity is obtained;

[0116] The root mean square calculation process includes:

[0117] Calculate the square of each number; add up all the squared numbers; divide the obtained sum by the number of numbers; calculate the square root of the obtained quotient to get the root mean square.

[0118] The process of determining the operating state of the rolling bearing in step S5 is specifically as follows:

[0119] The rolling bearing detection theory and actual measurement show that: the fault frequency of the rolling bearing is concentrated in the high-frequency range. This method extracts the vibration signal in its high-frequency band and performs special algorithm processing to obtain the characteristic quantity LQ value, which characterizes the operating state of the rolling bearing. All vibrations caused by mechanical faults such as imbalance and misalignment are filtered by band-pass filtering. The LQ method also reflects the bearing lubrication state. Poor lubrication will cause the LQ value to increase. The machine speed, power, and bearing size have little effect on the LQ value; two states of bearing operation can be defined according to the LQ value: good and faulty.

[0120] Pre-determine the good state threshold and fault state threshold of the LQ value according to the historical on-site operation data of the rolling bearing. The good state threshold is less than the fault state threshold;

[0121] If the measured LQ value is less than the good state threshold, it is determined that the rolling bearing is in a good bearing state and lubrication state;

[0122] If the measured LQ value is greater than the fault state threshold, it is determined that the rolling bearing is in a poor bearing state, and the rolling bearing is replaced.

[0123] If the measured LQ value is between the good state threshold and the fault state threshold, the lubrication condition of the rolling bearing is improved.

[0124] Optionally, the value of the good state threshold is within the range of 1.0 - 1.3, and the value of the fault state threshold is within the range of 1.8 - 2.0. Preferably, the value of the good state threshold is 1.3, and the value of the fault state threshold is 1.8.

[0125] In this embodiment,

[0126] If the LQ value < 1, it is determined that the ball bearing is in a good bearing state and lubrication state, and continue to operate;

[0127] If the LQ value > 2, it is determined that the ball bearing is in a poor bearing state, and replace the bearing;

[0128] If 1 < LQ value < 2, add oil to the ball bearing to improve the lubrication condition.

[0129] Example 2

[0130] This embodiment is based on the rolling bearing state detection method based on the LQ value provided in Example 1, and further proposes an LQ bearing state detector. The LQ bearing state detector uses a vibration high-frequency filtering method to detect, analyze and evaluate the operating state of the rolling bearing. No parameters need to be input. The special rolling bearing state detection method (LQ method) can determine the operating state of the rolling bearing and measure vibration and temperature at the same time. It is particularly suitable for on-site detection of motors, pumps and fans. It is easy to use, practical and cost-effective. It has the following functions: Vibration detection: frequency range: 5 to 1000 Hz

[0131] Measuring range: Acceleration: 0~200m / s 2 (Peak)

[0132] Speed: 0~200mm / s (effective value)

[0133] Displacement: 0~2.0mm (peak-to-peak value)

[0134] Rolling bearing status LQ value detection: measurement range: 0~20, frequency range: 600Hz~10kHz

[0135] Infrared temperature measurement range: -40~240℃

[0136] Data storage function: 50 values ​​​​are stored

[0137] Explosion-proof grade: Ex ib IIC T4 Gb

[0138] Protection level: IP66

[0139] Example 3

[0140] Based on the LQ bearing status detector provided in Example 2, this embodiment further proposes a network version software system, such as Figure 6 As shown in the figure, the system is an intelligent pump monitoring and inspection system launched to meet the needs of on-site management of a large number of pump equipment in petrochemical enterprises. The system adopts LQ rolling bearing detection core technology, integrating inspection data collection and storage, equipment operation status query, data query, alarm query, and vibration waveform spectrum analysis. Based on Internet and LAN data sharing, it can effectively monitor high-risk pump equipment, provide equipment management personnel with detailed pump operation information, timely predict faults, and have a clear understanding of the operation status of high-risk pump groups, bringing the equipment management level of high-risk pumps to a new level.

[0141] The system consists of network analysis software, data server, and inspection instrument. The system adopts B / S architecture design. Users access the system through a browser with a friendly interface.

[0142] The inspection instrument can collect four types of data: bearing status LQ value, vibration, temperature, and sealing status;

[0143] The network version of the analysis software has the functions of vibration waveform spectrum analysis, trend query, alarm query, equipment operation status query, equipment data statistical report, diagnostic report writing, and inspection plan preparation.

[0144] LQ2012 intelligent pump inspection instrument is suitable for use in the inspection and maintenance department, operation department, and status monitoring department of the petrochemical industry. The corresponding technical parameters are shown in Table 1.

[0145] Table 1 Technical parameters of intelligent pump inspection instrument

[0146]

[0147] The functions of the network version software system are as follows:

[0148] a) Network server software compatibility and data transmission format

[0149] The system adopts B / S network version structure. The LQ2000 pump monitoring software system has a data interface, complies with the prescribed format of data transmission, supports enough users, and enters the system by entering the server IP address through the IE browser.

[0150] b) Intuitive vertical tree database structure attributes

[0151] According to the complete and clear database structure: company → workshop → device → equipment → measuring point → measuring point.

[0152] c) Device status query is clear at a glance

[0153] The powerful alarm query function and equipment status query provide you with a clear view of the equipment status and can quickly identify problematic areas. The inspection data in the database is classified by inspection area, time, data range and data type, and the green zone / yellow zone / and red zone status are clearly identified.

[0154] d) Rich status analysis tools

[0155] Perform waveform, spectrum and trend analysis on the data, LQ technical methods and bearing characteristics analysis, etc., to facilitate technical personnel to conduct data analysis.

[0156] e) Report function and alarm data push

[0157] It can generate equipment data statistics reports and export them to Excel, and write analysis reports based on the provided report templates. It can also push alarm data to WeChat and SMS.

[0158] f) Implement open monitoring data: seamlessly connect the inspection database with external software platforms and provide data interface software.

[0159] The software provides data query, graph analysis and management

[0160] g) Data types collected: vibration, temperature, bearing status value (LQ value), characteristic value and seal leakage status, waveform spectrum;

[0161] h) Statistical query: equipment operation status query, equipment alarm record query, equipment operation cumulative time statistics, historical data query;

[0162] i) Diagnostic analysis: waveform spectrum, vibration trend graph, temperature trend graph, bearing status LQ value trend graph, trend data comparison graph, bearing database;

[0163] j) Report management: equipment data statistics report, equipment fault diagnosis report;

[0164] k) Inspection management: inspection scheme management, inspection plan management, and inspection instrument communication;

[0165] l) Settings: monitoring tree settings, device management;

[0166] m) System: user management, role management, work log, system log;

[0167] n) Help: Instructions for using the system.

[0168] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A rolling bearing state detection method based on LQ value, It is characterized in that The following steps are involved: Obtain vibration signals of rolling bearings; Performing integration processing on the vibration signal to obtain a speed signal; Performing bandpass filtering with upper and lower frequency limits on the speed signal to obtain a rolling bearing fault signal; According to the rolling bearing fault signal, the effective value of the vibration velocity is calculated to obtain the LQ value; The condition of the rolling bearing is determined based on the LQ value.

2. A rolling bearing state detection method based on LQ value according to claim 1, It is characterized in that The frequency of the rolling bearing fault signal obtained by bandpass filtering is within the range of 600Hz-10kHz.

3. A rolling bearing state detection method based on LQ value according to claim 1, It is characterized in that Determining the state of the rolling bearing according to the LQ value is specifically as follows: Determine in advance a good state threshold and a fault state threshold of the LQ value based on historical field operation data of the rolling bearing, wherein the good state threshold is less than the fault state threshold; If the measured LQ value is less than the good state threshold, it is judged that the rolling bearing is in a good bearing state and lubrication state; If the measured LQ value is greater than the fault state threshold, it is determined that the rolling bearing is in a poor bearing state and the rolling bearing is replaced.

4. A rolling bearing state detection method based on LQ value according to claim 3, It is characterized in that The method further includes: if the measured LQ value is between a good state threshold and a fault state threshold, performing lubrication improvement processing on the rolling bearing.

5. A rolling bearing state detection method based on LQ value according to claim 3, It is characterized in that The value of the good state threshold is within the range of 1.0-1.3, and the value of the fault state threshold is within the range of 1.8-2.

0.

6. A rolling bearing state detection method based on LQ value according to claim 1, It is characterized in that The method is based on an instrument type analog and digital signal optimization algorithm, and obtains the effective value of the vibration velocity according to the rolling bearing fault signal.

7. A rolling bearing state detection method based on LQ value according to claim 6, It is characterized in that The instrument type analog and digital signal optimization algorithm is the root mean square calculation method.

8. A rolling bearing state detection method based on LQ value according to claim 1, It is characterized in that The method uses a filter to perform band-pass filtering on the speed signal with upper and lower frequency limits, and removes some signal points with unstable amplitude responses just after the speed signal enters the filter.

9. A bearing state detector using a rolling bearing state detection method based on LQ value as claimed in any one of claims 1 to 8, It is characterized in that It includes a vibration acceleration sensor, an infrared temperature sensor and a processor, wherein the processor is connected to the vibration acceleration sensor and the infrared temperature sensor respectively, and is used to execute a rolling bearing state detection method based on an LQ value as described in any one of claims 1 to 8 based on the detection value of the vibration acceleration sensor, and to display the infrared temperature according to the detection value of the infrared temperature sensor.

10. A network software system using the bearing condition detector according to claim 9, It is characterized in that It comprises the bearing status detector and a data server as claimed in claim 9, wherein the data server stores network version analysis software, and the bearing status detector is connected to the data server via a data interface for remote communication.

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