Bearing Fault Diagnosis Methods and Systems

Abstract

This application discloses a bearing fault diagnosis method and system, relating to the field of compressor-related technology. The method includes: S1: acquiring bearing operating data and vibration signals; S2: comparing the operating data with preset operating data, and the vibration signal with a preset vibration signal; S3: if the operating data exceeds the preset operating data range, determining and outputting the cause of the bearing fault; S4: if the operating data is within the preset operating data range, and the vibration signal exceeds the preset vibration signal range, performing frequency domain transformation on the vibration signal to extract fault characteristic frequencies, and determining and outputting the cause of the bearing fault based on the fault characteristic frequencies. This application enables fault prediction, performance evaluation, and fault diagnosis of screw compressors.

Description

Technical Field

[0001] This application relates to the field of compressor-related technology, specifically to a bearing fault diagnosis method and system. Background Technology

[0002] In the petrochemical industry, with the development of advanced technologies such as the Internet of Things, big data, and intelligent learning, more and more scholars are studying the application of multi-sensor information fusion technology in fault diagnosis. During long-term operation, equipment inevitably experiences various faults. If early signs of faults are not detected in time, they can develop and expand, eventually reaching a critical point where the equipment is prone to sudden and serious failures, leading to a large amount of unplanned maintenance work.

[0003] As a crucial component of compressors, bearings require real-time monitoring to ensure their normal operation. Data processing and analysis can identify the causes of equipment failures and predict potential malfunctions, providing a scientific basis for accident prevention and timely maintenance planning. This ultimately saves on maintenance costs and improves equipment reliability and safety. Therefore, bearing fault monitoring and diagnosis are extremely important.

[0004] Therefore, this application provides a bearing fault diagnosis method and system that can monitor and diagnose bearing faults. Summary of the Invention

[0005] This application provides a bearing fault diagnosis method and system that can realize fault prediction, performance evaluation and fault diagnosis of screw compressors, assess the status and performance of the compressor unit in advance, judge the possible risks and provide contingency plans in advance.

[0006] To achieve the above objectives, this application provides a bearing fault diagnosis method, wherein the bearing is used in a screw compressor, and the fault diagnosis method includes:

[0007] S1: Acquire bearing operating data and vibration signals;

[0008] S2: Compare the running data with the preset running data, and the vibration signal with the preset vibration signal;

[0009] S3: If the operating data exceeds the preset operating data range, then determine and output the cause of the bearing failure;

[0010] S4: If the operating data is within the preset operating data range and the vibration signal exceeds the preset vibration signal, then the vibration signal is subjected to frequency domain transformation to extract the fault feature frequency, and the fault cause of the bearing is determined and output based on the fault feature frequency.

[0011] In some embodiments of this application, the operating data includes the current oil supply temperature of the main oil supply pipe in the screw compressor, the preset operating data includes a preset oil supply temperature, and the fault diagnosis method includes:

[0012] Obtain the current oil supply temperature;

[0013] Compare the current fuel supply temperature with the preset fuel supply temperature;

[0014] If the current oil supply temperature is higher than the preset oil supply temperature, then the cause of the fault is determined and output as high oil supply temperature.

[0015] In some embodiments of this application, the operating data includes the current inlet water temperature of the lubricating oil cooler, the preset operating data includes a preset inlet water temperature, and the fault diagnosis method includes:

[0016] Obtain the current inlet water temperature;

[0017] Compare the current inlet water temperature with the preset inlet water temperature;

[0018] If the current inlet water temperature is higher than the preset inlet water temperature, the fault cause is determined and output as high inlet water temperature; if the current oil supply temperature is higher than the preset oil supply temperature and the current inlet water temperature is higher than the preset inlet water temperature, the fault cause is output as high inlet water temperature leading to insufficient heat exchange of the lubricating oil cooler.

[0019] In some embodiments of this application, the operating data includes the current inlet water pressure of the lubricating oil cooler, the preset operating data includes a preset inlet water pressure, and the fault diagnosis method specifically includes:

[0020] Obtain the current inlet water pressure;

[0021] Compare the current inlet pressure with the preset inlet pressure;

[0022] If the current inlet water pressure is lower than the preset inlet water pressure, then the cause of the fault is determined and output as low inlet water pressure;

[0023] If the current oil supply temperature is higher than the preset oil supply temperature and the current water inlet pressure is lower than the preset water inlet pressure, then the fault cause is output as insufficient heat exchange of the lubricating oil cooler due to the current water inlet pressure.

[0024] If the current oil supply temperature is higher than the preset oil supply temperature, the current inlet water temperature is lower than the preset inlet water temperature, and the current inlet water pressure is higher than the preset inlet water pressure, then the fault cause is output as insufficient heat exchange of the lubricating oil cooler due to scaling.

[0025] In some embodiments of this application, the operating data includes the current oil supply pressure of the bearing, the preset operating data includes a preset oil supply pressure, and the fault diagnosis method specifically includes:

[0026] Obtain the current oil supply pressure;

[0027] Compare the current oil supply pressure with the preset oil supply pressure;

[0028] If the current oil supply pressure is lower than the preset oil supply pressure, then the cause of the fault is determined and output as insufficient bearing oil supply and unreasonable oil distribution by the oil distributor.

[0029] In some embodiments of this application, the bearing is a rolling bearing, the vibration signal is a vibration velocity signal, and step S4 specifically includes:

[0030] S41: If the current oil supply temperature is lower than the preset oil supply temperature, the current water inlet temperature is lower than the preset water inlet temperature, the current water inlet pressure is higher than the preset water inlet pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, then the vibration signal is frequency domain transformed and the fault characteristic frequency is extracted. The cause of the bearing failure is determined and output based on the fault characteristic frequency and the natural frequency.

[0031] S42: If the fault characteristic frequency exceeds the preset deviation range of the inherent frequency, then determine and output the cause of the fault.

[0032] In some embodiments of this application, the rolling bearing includes a cage, the vibration velocity signal includes a cage vibration velocity signal, the fault characteristic frequency includes a cage fault characteristic frequency, and the natural frequency includes a cage natural frequency. c for:

[0033]

[0034] Among them, R pmi R is the inner ring speed. pmo Where is the outer ring speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle;

[0035] Step S42 specifically includes:

[0036] If the cage failure characteristic frequency exceeds the cage natural frequency f c If the preset deviation range is met, the cause of the fault is determined and output as cage failure.

[0037] In some embodiments of this application, the rolling bearing includes an inner ring, the vibration velocity signal includes an inner ring vibration velocity signal, the fault characteristic frequency includes an inner ring fault characteristic frequency, and the natural frequency includes the inner ring natural frequency. i for:

[0038]

[0039] Where N is the number of rolling elements, R pmi Where is the inner ring speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle;

[0040] Step S42 specifically includes:

[0041] If the inner loop fault characteristic frequency exceeds the inner loop natural frequency f i If the preset deviation range is met, the cause of the fault is determined and output as an inner loop fault.

[0042] In some embodiments of this application, the rolling bearing includes an outer ring, the vibration velocity signal includes an outer ring vibration velocity signal, the fault characteristic frequency includes an outer ring fault characteristic frequency, and the natural frequency includes the outer ring natural frequency. o for:

[0043]

[0044] Where N is the number of rolling elements, R pmi Where is the outer ring speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle;

[0045] Step S42 specifically includes:

[0046] If the outer ring fault characteristic frequency exceeds the outer ring natural frequency f o If the preset deviation range is met, the cause of the fault is determined and output as an outer loop fault.

[0047] In some embodiments of this application, the rolling bearing includes rolling elements, the vibration velocity signal includes rolling element vibration velocity signals, the fault characteristic frequency includes rolling element fault characteristic frequencies, and the natural frequency includes rolling element natural frequencies. b for:

[0048]

[0049] Among them, R pmi Where is the rolling element rotational speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle;

[0050] Step S42 specifically includes:

[0051] If the characteristic frequency of the rolling element failure exceeds the natural frequency f of the rolling element b If the preset deviation range is met, the cause of the fault is determined and output as a rolling element fault.

[0052] In some embodiments of this application, the bearing is a sliding bearing, the vibration signal is a displacement signal, and step S4 specifically includes:

[0053] S410: If the current oil supply temperature is lower than the preset oil supply temperature, the current water inlet temperature is lower than the preset water inlet temperature, the current water inlet pressure is higher than the preset water inlet pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, then the displacement signal is frequency domain transformed and the fault characteristic frequency is extracted. Based on the fault characteristic frequency, the cause of the sliding bearing failure is determined and output.

[0054] In some embodiments of this application, the displacement signal includes the shaft center trajectory, and mutually perpendicular horizontal and vertical displacements. The step of determining and outputting the cause of the sliding bearing failure based on the failure frequency specifically includes:

[0055] If the fault characteristic frequencies include the first, second, and third harmonics, and the vertical displacement is greater than the horizontal displacement, then the cause of the sliding bearing failure is determined and output as a large sliding bearing clearance.

[0056] If the fault characteristic frequency includes 0.42 to 0.48 octaves and the shaft center trajectory is double elliptical, or if the fault characteristic frequency includes 0.42 to 0.48 octaves and the shaft center trajectory is disordered and does not coincide, then the cause of the sliding bearing fault is determined and output as the sliding bearing oil film whirl.

[0057] Compared to existing technologies, this application considers the different sampling requirements for different signals in remote signal monitoring and diagnostic functions. The operating data mentioned above in this application refers to low-frequency signals, such as temperature, pressure, flow rate, liquid level, and valve position signals. These signals have already been processed by the IO module and controller and can be directly used for fault diagnosis. That is, the operating data is directly compared with the preset operating data range to determine and output the cause of the bearing failure. However, vibration signals are high-frequency signals due to their characteristics of high frequency, noise, and multimodal aliasing. They exhibit different characteristics, which are often present in various features of the signal. It is often difficult to effectively identify them based solely on some basic features in the time or frequency domain. In addition, the collision between the components in the bearing and the bearing itself will generate pulse signals, the frequency of which corresponds to the faulty component in the bearing and is called the fault characteristic frequency. Therefore, in this application, the vibration signal needs to be frequency-domain transformed to extract the fault characteristic frequency. Based on this fault characteristic frequency, the bearing is diagnosed for fault, the cause of the bearing failure is discovered in a timely manner, and a scientific basis is provided for accident prevention and scientific maintenance planning, thereby saving maintenance costs and improving the reliability and safety of the equipment.

[0058] On the other hand, this application also provides a bearing fault diagnosis system, which employs the above-mentioned bearing fault diagnosis method, including:

[0059] A data acquisition module, installed on the bearing, is used to acquire the bearing's operating data and vibration signals;

[0060] A digital-to-analog converter module is connected to the acquisition module via signal transmission.

[0061] The computing board, connected to the digital-to-analog converter module, is used to extract the fault characteristic frequencies of the vibration signal;

[0062] The intelligent gateway receives the fault characteristic frequency and operating parameters. The intelligent gateway includes a fault diagnosis module, which is used to analyze operating data and preset operating data, as well as vibration signals and preset vibration signals, to determine and output the cause of the bearing failure.

[0063] In some embodiments of this application, the bearing fault diagnosis system further includes:

[0064] A cloud server, connected to the smart gateway, is used to receive and display the cause of the fault.

[0065] Compared to existing technologies, this application considers the different sampling requirements of remote signal monitoring and diagnostic functions for different signals. The operating data mentioned above in this application refers to low-frequency signals, such as temperature, pressure, flow rate, liquid level, valve position, etc. These signals have already been processed by the IO module and controller and can be directly entered into the smart gateway for fault diagnosis. That is, the operating data is directly compared with the preset operating data range to determine and output the cause of the bearing failure. However, due to the characteristics of vibration signals such as high frequency, noise, and multimodal mixing, they are high-frequency signals and will exhibit different characteristics. These characteristics often exist in various features of the signal, and it is often difficult to effectively identify them based solely on some basic features in the time or frequency domain. In addition, the collision between the various components in the bearing and the bearing will generate pulse signals, the frequency of which corresponds to the faulty component of the bearing and is called the fault characteristic frequency. Therefore, in this application, the vibration signal needs to be frequency-domain transformed in the computing board to extract the fault characteristic frequency, and the fault characteristic frequency is sent to the smart gateway for fault diagnosis to promptly discover the cause of the bearing failure, providing a scientific basis for accident prevention and scientifically arranging maintenance, thereby saving maintenance costs and improving the reliability and safety of the equipment. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart of the fault diagnosis method in the embodiments of this application;

[0068] Figure 2 This is a flowchart of the fault diagnosis process after the bearing temperature exceeds the limit in the embodiments of this application;

[0069] Figure 3 This is a flowchart illustrating the bearing temperature exceeding the limit in an embodiment of this application;

[0070] Figure 4 This is a flowchart of the fault diagnosis process after the rolling bearing vibration velocity signal exceeds the standard in the embodiments of this application;

[0071] Figure 5 This is a flowchart of the fault diagnosis process after the sliding bearing displacement signal exceeds the standard in the embodiments of this application;

[0072] Figure 6 This is a schematic diagram of the fault diagnosis system in the embodiments of this application. Detailed Implementation

[0073] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0074] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0075] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; or they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0076] This application provides a bearing fault diagnosis method and system, which are described in detail below. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments of this application. Furthermore, in the following embodiments, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments.

[0077] Reference Figure 1 The bearing fault diagnosis method provided in this application, wherein the bearing is used in a screw compressor, includes the following steps: S1: acquiring bearing operating data and vibration signals; S2: comparing the operating data with preset operating data, and comparing the vibration signals with preset vibration signals; S3: if the operating data exceeds the preset operating data range, determining and outputting the cause of the bearing fault; S4: if the operating data is within the preset operating data range and the vibration signal exceeds the preset vibration signal, performing frequency domain transformation on the vibration signal to extract fault feature frequencies, and determining and outputting the cause of the bearing fault based on the fault feature frequencies.

[0078] Compared to existing technologies, this application considers the different sampling requirements for different signals in remote signal monitoring and diagnostic functions. The operating data mentioned above in this application refers to low-frequency signals, such as temperature, pressure, flow rate, liquid level, and valve position signals. These signals have already been processed by the IO module and controller and can be directly used for fault diagnosis. That is, the operating data is directly compared with the preset operating data range to determine and output the cause of the bearing failure. However, vibration signals are high-frequency signals due to their characteristics of high frequency, noise, and multimodal aliasing. They exhibit different characteristics, which are often present in various features of the signal. It is often difficult to effectively identify them based solely on some basic features in the time or frequency domain. In addition, the collision between the components in the bearing and the bearing itself will generate pulse signals, the frequency of which corresponds to the faulty component in the bearing and is called the fault characteristic frequency. Therefore, in this application, the vibration signal needs to be frequency-domain transformed to extract the fault characteristic frequency. Based on this fault characteristic frequency, the bearing is diagnosed for fault, the cause of the bearing failure is discovered in a timely manner, and a scientific basis is provided for accident prevention and scientific maintenance planning, thereby saving maintenance costs and improving the reliability and safety of the equipment.

[0079] It should be noted that the above-mentioned fault diagnosis method is used to diagnose the cause of bearing failure; that is, when a bearing malfunctions, the fault diagnosis method of this application can determine the cause of the bearing failure. Figure 2 As shown, before step S1, the method further includes: acquiring the current temperature of the bearing, performing data separation and filtering on the temperature information, and then proceeding to the alarm judgment step. Specifically, the alarm judgment step includes: when the current temperature is higher than a first preset temperature value, it indicates that the current temperature of the bearing is too high, and the system is controlled to enter the fault diagnosis system for fault diagnosis. When the current temperature is higher than a second preset temperature value, it indicates that the temperature of the bearing is extremely high, and the system is controlled to enter step S1 for fault diagnosis and to shut down the system. The second preset temperature value is higher than the first preset temperature value.

[0080] For example, the first preset temperature value is 85℃, and the second preset temperature value is 90℃. When the current temperature is higher than 85℃, it indicates that the current temperature of the bearing is too high, meaning that the bearing already has some faults. In this case, the system will proceed to step S1 to perform fault diagnosis and determine the cause of the bearing overheating. When the current temperature is higher than 90℃, it indicates that the current temperature of the bearing is extremely high, and continued operation may cause irreversible damage to the bearing. In this case, the system will proceed to step S1 to perform fault diagnosis and control the system to shut down.

[0081] The process includes, prior to the alarm judgment step, calibrating the third temperature sensor used to detect the current temperature of the bearing. The calibrated third temperature sensor is then processed for interference and noise reduction using a combination of time-domain median filtering and IIR digital low-pass filtering. The filtered signal is analyzed in both the time and frequency domains, and an alarm threshold (first preset temperature, second preset temperature) is used to determine if data exceeds the limit. If no threshold is exceeded, the signal can be displayed normally; otherwise, the process proceeds to step S1 for fault diagnosis.

[0082] Reference Figure 3 The operating data includes the current oil supply temperature of the main oil supply pipe in the screw compressor, and the preset operating data includes a preset oil supply temperature. The fault diagnosis method includes: acquiring the current oil supply temperature; comparing the current oil supply temperature with the preset oil supply temperature; and if the current oil supply temperature is higher than the preset oil supply temperature, then determining and outputting that the fault cause is a high oil supply temperature. For example, the temperature of the main oil supply pipe can be obtained by a first temperature sensor installed on the main oil supply pipe. For instance, if the preset oil supply temperature is 55°C, when the current oil supply temperature is higher than 55°C, it indicates that the temperature of the main oil supply pipe is too high, and at this time, the bearing fault (i.e., the bearing temperature exceeds the standard) is confirmed to be caused by an abnormally high oil supply temperature.

[0083] Furthermore, the operating data includes the current inlet water temperature of the lubricating oil cooler, the preset operating data includes a preset inlet water temperature, and the fault diagnosis method includes: acquiring the current inlet water temperature; comparing the inlet water temperature with the preset inlet water temperature; if the current inlet water temperature is higher than the preset inlet water temperature, then determining and outputting the fault cause as high inlet water temperature; if the current oil supply temperature is higher than the preset oil supply temperature, and the current inlet water temperature is higher than the preset inlet water temperature, then outputting the fault cause as high inlet water temperature leading to insufficient heat exchange of the lubricating oil cooler.

[0084] For example, the aforementioned lubricating oil cooler is equipped with a second temperature sensor to obtain the current inlet water temperature of the lubricating oil cooler. For instance, if the preset inlet water temperature is 37°C, when the current inlet water temperature is higher than 37°C, it indicates that the current inlet water temperature of the lubricating oil cooler is too high, and the cause of the fault is determined and output as high inlet water temperature. When the current oil supply temperature is higher than 55°C and the current inlet water temperature is higher than 37°C, meaning both the current oil supply temperature and the current inlet water temperature exceed the standard, the cause of the fault is output as insufficient heat exchange in the lubricating oil cooler due to high inlet water temperature. Therefore, the cause of the bearing fault can be determined using the above bearing fault diagnosis method.

[0085] In addition, the above-mentioned operating data also includes the current inlet water pressure of the lubricating oil cooler. The preset operating data includes a preset inlet water pressure. The fault diagnosis method specifically includes: obtaining the current inlet water pressure; comparing the current inlet water pressure with the preset inlet water pressure; if the current oil supply temperature is higher than the preset oil supply temperature and the current inlet water pressure is lower than the preset inlet water pressure, then the fault cause is output as insufficient heat exchange of the lubricating oil cooler due to the current inlet water pressure; if the current oil supply temperature is higher than the preset oil supply temperature, the current inlet water temperature is lower than the preset inlet water temperature, and the current inlet water pressure is higher than the preset inlet water pressure, then the fault cause is output as insufficient heat exchange of the lubricating oil cooler due to scaling.

[0086] For example, the aforementioned lubricating oil cooler is equipped with a first pressure sensor, which is used to acquire the current inlet water pressure of the lubricating oil cooler. The preset inlet water pressure is 0.35 MPaG. When the current inlet water pressure is lower than 0.35 MPaG, it indicates that the inlet water pressure of the lubricating oil cooler is low, which also means that the water flow in the lubricating oil cooler is low, resulting in insufficient heat exchange of the lubricating oil cooler. If the temperature value is higher than the preset temperature value, the current inlet water temperature is lower than the preset inlet water temperature, and the current inlet water pressure is higher than the preset inlet water pressure, it indicates that the current inlet water temperature and current inlet water pressure are both normal, but the current oil supply temperature of the main oil supply pipe exceeds the standard. In this case, the fault is output as insufficient heat exchange of the lubricating oil cooler due to scaling.

[0087] Continue to refer to Figure 3 The operating data includes the current oil supply pressure of the bearing, and the preset operating data includes the preset oil supply pressure. The fault diagnosis method specifically includes: obtaining the current oil supply pressure; comparing the current oil supply pressure with the preset oil supply pressure; if the current oil supply pressure is lower than the preset oil supply pressure, then determining that the fault is caused by insufficient oil supply to the bearing, and simultaneously outputting that the fault is caused by unreasonable oil distribution by the oil distributor.

[0088] The bearing is also equipped with a second pressure sensor to obtain the bearing's oil supply pressure. For example, if the preset oil supply pressure is 0.2 MPaG, and the current oil supply pressure is lower than 0.2 MPaG, it indicates that the bearing's oil supply pressure is low and the oil supply is insufficient. In this case, the bearing failure is confirmed to be due to improper oil distribution by the oil distributor.

[0089] It should be noted that the bearings in screw compressors are typically rolling bearings or hydrodynamic sliding bearings. The current oil supply temperature of the main oil supply pipe, the current inlet water temperature of the lubricating oil cooler, the current inlet water pressure of the lubricating oil cooler, and the current oil supply pressure of the bearing are applicable to both rolling and hydrodynamic sliding bearings. However, for screw compressors equipped with rolling bearings, the vibration signal is a vibration velocity signal, which needs to be frequency-domain transformed (Fast Fourier Transform, FFT) to extract the fault characteristic frequencies. These frequencies are then compared with the natural frequencies of the cage, inner ring, outer ring, and rolling elements of the rolling bearing. If a match is found, the cause of the fault is output. For screw compressors equipped with hydrodynamic sliding bearings, the vibration signal is a displacement signal, which needs to be frequency-domain transformed (Fast Fourier Transform, FFT) to extract the characteristic frequencies. Based on the range of fault frequencies corresponding to the vibration direction, the cause of the hydrodynamic sliding bearing fault is determined.

[0090] The following is for reference Figure 4 This paper provides a detailed explanation of the fault diagnosis when the vibration velocity signal of a rolling bearing exceeds the standard.

[0091] Wherein, the bearing is a rolling bearing, the vibration signal is a vibration velocity signal, and step S4 specifically includes: if the current oil supply temperature is lower than the preset oil supply temperature, the current inlet water temperature is lower than the preset inlet water temperature, the current inlet water pressure is higher than the preset inlet water pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, then the vibration signal is subjected to frequency domain transformation and the fault characteristic frequency is extracted, and the cause of the bearing failure is determined and output according to the fault characteristic frequency and the natural frequency; S42: if the fault characteristic frequency exceeds the preset deviation range of the natural frequency, then the cause of the failure is determined and output.

[0092] It is understood that if the current oil supply temperature is lower than the preset oil supply temperature, the current inlet water temperature is lower than the preset inlet water temperature, the current inlet water pressure is higher than the preset inlet water pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, it indicates that the bearing's operating data are all normal except for the vibration signal exceeding the standard. This application extracts the fault characteristic frequency by performing frequency domain transformation on the vibration velocity signal, compares the fault characteristic frequency with the natural frequency, and determines and outputs the cause of the fault.

[0093] More specifically, the extraction of fault feature frequencies mentioned above includes: performing digital-to-analog conversion on the acquired vibration velocity signal, followed by feature extraction and frequency domain transformation on the vibration signal within the computing board. The high-frequency fault feature frequencies generated by logic include, but are not limited to, the following feature values: vibration mean, standard deviation, root mean square amplitude, RMS root mean square, peak-to-peak value, skewness, kurtosis, peak factor, margin factor, centroid frequency, mean square frequency, root mean square frequency, frequency variance, and frequency standard deviation. It also includes rotational speed based on the key phase signal output by some application algorithm modules, and some dimensionless feature parameters used for fault diagnosis.

[0094] The rolling bearing includes a cage, inner ring, outer ring, and rolling elements, and the natural frequencies of the cage, inner ring, outer ring, and rolling elements are all different. The following is a detailed description of each component in the rolling bearing. For example, the preset vibration signal is a preset vibration velocity signal of 8 mm / s. That is, if the vibration velocity signal exceeds 8 mm / s, the vibration signal is subjected to frequency domain transformation and the fault characteristic frequency is extracted. If the vibration velocity signal exceeds the preset limit vibration velocity signal (e.g., 12 mm / s), it indicates that the vibration of the rolling bearing is very severe, and the control system is then shut down.

[0095] In some embodiments of this application, the rolling bearing includes a cage, the vibration velocity signal includes a cage vibration velocity signal, the fault characteristic frequency includes a cage fault characteristic frequency, and the natural frequency includes a cage natural frequency. c for:

[0096]

[0097] Among them, R pmi R is the inner ring speed. pmo Let D be the outer ring speed, d be the pitch circle diameter of the rolling bearing, d be the rolling element diameter, and α be the contact angle. The inner and outer ring speeds can be obtained through real-time monitoring. The pitch circle diameter, rolling element diameter, and contact angle of the rolling bearing are specification parameters, which can be found in the bearing's instruction manual. Then, these parameters are substituted into the cage's natural frequency f. c The result can be obtained by calculation using the formula.

[0098] Therefore, step S42 specifically includes: if the cage fault characteristic frequency exceeds the cage natural frequency f c If the preset deviation range is met, then the cause of the fault is determined and output as a cage fault. If the characteristic frequency of the cage fault is within the cage's natural frequency f... cIf the vibration is within the preset deviation range, it is determined that the cage has not failed, and the vibration of the rolling bearing may be caused by other reasons.

[0099] In some embodiments of this application, the rolling bearing includes an inner ring, the vibration velocity signal includes an inner ring vibration velocity signal, the fault characteristic frequency includes an inner ring fault characteristic frequency, and the natural frequency includes the inner ring natural frequency. i for:

[0100]

[0101] Where N is the number of rolling elements, R pmi Let α be the inner ring speed, D be the pitch circle diameter of the rolling bearing, d be the rolling element diameter, and α be the contact angle. The number of rolling elements, the pitch circle diameter, the rolling element diameter, and the contact angle are all specification parameters of the rolling bearing, which can be obtained from the rolling bearing's instruction manual. The inner ring speed can be obtained through real-time monitoring.

[0102] Therefore, step S42 specifically includes: if the inner loop fault characteristic frequency exceeds the inner loop natural frequency f i If the preset deviation range is met, the cause of the fault is determined and output as an inner loop fault. If the characteristic frequency of the inner loop fault is within the inner loop's natural frequency f... i If the vibration is within the preset deviation range, it is determined that the inner ring has not failed, and the vibration of the rolling bearing may be caused by other reasons.

[0103] In some embodiments of this application, the rolling bearing includes an outer ring, the vibration velocity signal includes an outer ring vibration velocity signal, the fault characteristic frequency includes an outer ring fault characteristic frequency, and the natural frequency includes the outer ring natural frequency. o for:

[0104]

[0105] Where N is the number of rolling elements, R pmi Let θ be the outer ring speed, D be the pitch circle diameter of the rolling bearing, d be the rolling element diameter, and α be the contact angle. The number of rolling elements, the pitch circle diameter, the rolling element diameter, and the contact angle are all specification parameters of the rolling bearing, which can be obtained from the rolling bearing's instruction manual. The outer ring speed can be obtained through real-time monitoring.

[0106] Therefore, step S42 specifically includes: if the outer loop fault characteristic frequency exceeds the outer loop natural frequency f o If the preset deviation range is met, then the cause of the fault is determined and output as an outer loop fault. If the characteristic frequency of the outer loop fault is within the outer loop's natural frequency f... oIf the vibration of the rolling bearing is within the preset deviation range, it is determined that the outer ring has not failed, and the vibration may be caused by other reasons.

[0107] In some embodiments of this application, the rolling bearing includes rolling elements, the vibration velocity signal includes rolling element vibration velocity signals, the fault characteristic frequency includes rolling element fault characteristic frequencies, and the natural frequency includes rolling element natural frequencies. b for:

[0108]

[0109] Among them, R pmi Let α be the rolling element rotational speed, D be the pitch circle diameter of the rolling bearing, d be the rolling element diameter, and α be the contact angle. The pitch circle diameter, rolling element diameter, and contact angle are all specification parameters of the rolling bearing, which can be obtained from the bearing's instruction manual. The rolling element rotational speed can be obtained through real-time monitoring.

[0110] Therefore, step S42 specifically includes: if the rolling element fault characteristic frequency exceeds the rolling element's natural frequency f b If the preset deviation range is met, the cause of the fault is determined and output as a rolling element fault. If the characteristic frequency of the rolling element fault is within the natural frequency f of the rolling element... b If the vibration of the rolling bearing is within the preset deviation range, it is determined that the rolling element has not malfunctioned, and the vibration of the rolling bearing may be caused by other reasons.

[0111] The aforementioned preset deviation range is ±5% of the natural frequency. That is, when the fault characteristic frequency exceeds (95% to 105%) times the natural frequency, it indicates that the fault characteristic frequency exceeds the preset deviation range of the natural frequency, and the cause of the fault is determined and output.

[0112] The following is for reference Figure 5 This document provides a detailed explanation of fault diagnosis when the vibration signal (displacement signal) of a sliding bearing exceeds the limit. The preset vibration signal is a preset displacement signal of 100µm. Specifically, if the displacement signal exceeds 100µm, a frequency domain transformation is performed on the displacement signal to extract the fault characteristic frequency. If the displacement signal exceeds the preset limit displacement signal (e.g., 115µm), it indicates that the vibration of the sliding bearing is very severe, and the control system is then shut down.

[0113] For example, the bearing is a sliding bearing, the vibration signal is a displacement signal, and step S4 specifically includes: S410: if the current oil supply temperature is lower than the preset oil supply temperature, the current water inlet temperature is lower than the preset water inlet temperature, the current water inlet pressure is higher than the preset water inlet pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, then the displacement signal is frequency domain transformed and the fault characteristic frequency is extracted, and the fault cause of the sliding bearing is determined and output according to the fault characteristic frequency.

[0114] Based on the above embodiments, the displacement signal includes the shaft center trajectory, and mutually perpendicular horizontal and vertical displacements. The step of determining and outputting the cause of the sliding bearing failure based on the fault frequency specifically includes: if the fault characteristic frequency includes the 1st, 2nd, and 3rd harmonics, and the vertical displacement is greater than the horizontal displacement, then the cause of the sliding bearing failure is determined and output as a large sliding bearing clearance; if the fault characteristic frequency includes 0.42 to 0.48 harmonics, and the shaft center trajectory is double-elliptical, or if the fault characteristic frequency includes 0.42 to 0.48 harmonics, and the shaft center trajectory is disordered and non-coincident, then the cause of the sliding bearing failure is determined and output as oil film whirl in the sliding bearing.

[0115] Reference Figure 6 This application also provides a fault diagnosis system that employs the aforementioned bearing fault diagnosis method. The fault diagnosis system includes a data acquisition module, a digital-to-analog converter module, a computing board, and a smart gateway. The data acquisition module is installed on the bearing to acquire its operating data and vibration signals. The digital-to-analog converter module is signal-connected to the data acquisition module. The computing board is connected to the digital-to-analog converter module and is used to extract the fault characteristic frequency of the vibration signal. The smart gateway receives the fault characteristic frequency and operating parameters. The smart gateway includes a fault diagnosis module, which analyzes the operating data and preset operating data, as well as the vibration signal and preset vibration signal, to determine and output the cause of the bearing fault.

[0116] Compared to existing technologies, this application considers the different sampling requirements of remote signal monitoring and diagnostic functions for different signals. The operating data mentioned above in this application refers to low-frequency signals, such as temperature, pressure, flow rate, liquid level, valve position, etc. These signals have already been processed by the IO module and controller and can be directly entered into the smart gateway for fault diagnosis. That is, the operating data is directly compared with the preset operating data range to determine and output the cause of the bearing failure. However, due to the characteristics of vibration signals such as high frequency, noise, and multimodal mixing, they are high-frequency signals and will exhibit different characteristics. These characteristics often exist in various features of the signal, and it is often difficult to effectively identify them based solely on some basic features in the time or frequency domain. In addition, the collision between the various components in the bearing and the bearing will generate pulse signals, the frequency of which corresponds to the faulty component of the bearing and is called the fault characteristic frequency. Therefore, in this application, the vibration signal needs to be frequency-domain transformed in the computing board to extract the fault characteristic frequency, and the fault characteristic frequency is sent to the smart gateway for fault diagnosis to promptly discover the cause of the bearing failure, providing a scientific basis for accident prevention and scientifically arranging maintenance, thereby saving maintenance costs and improving the reliability and safety of the equipment.

[0117] It should be noted that the aforementioned operational data (i.e., low-frequency signals) includes signals such as temperature, pressure, flow rate, liquid level, and valve position. These signals have already been processed by the I / O module and controller and can be directly input into the smart gateway for fault diagnosis. However, the aforementioned vibration signals (i.e., high-frequency signals) need to be converted from digital to analog through a digital-to-analog converter module, and then input into the embedded real-time computing board to extract fault characteristic frequencies. The extracted fault characteristic frequencies are then input into the smart gateway for fault diagnosis.

[0118] Furthermore, if the aforementioned fault diagnosis methods are developed based on the software layer (e.g., Windows system), although software development is easy and quick, it suffers from weak stability and is prone to crashing. Windows is an open system, and its core software algorithms are easily cracked by competitors. It is also non-modular, resulting in cumbersome hardware maintenance and high costs. Cloud-based methods require massive storage capacity and regular updates and cleanups, leading to expensive maintenance. Therefore, the fault diagnosis method proposed in this application is based on the edge layer (e.g., edge intelligent devices). This means embedding fault monitoring and fault diagnosis algorithms into the system's edge terminal devices (e.g., embedding fault diagnosis algorithms into computing boards) to achieve edge intelligent computing. This allows the computation process to operate independently of the software-based operating system platform, thus addressing the problems of weak reliability, easily cracked algorithms, and high deployment costs inherent in software-based methods.

[0119] The aforementioned analog-to-digital converter (ADC) module is a high-speed analog-to-digital converter (AD / ADC) module. Its front end connects to the signal transmission modules of various sensors. The high-speed AD / ADC module includes a multi-channel synchronous acquisition module with a channel sampling rate exceeding 20kHz. The acquired data is stored in its respective storage space through independent channels. To ensure sampling accuracy, the high-speed AD / ADC module is internally equipped with analog input protection, an aliasing simulator filter, a track-and-hold amplifier, a reference voltage, and a high-speed parallel interface. More specifically, due to the high precision and strong processing power of ADI chips, the aforementioned acquisition module is designed and manufactured based on ADI chips.

[0120] Furthermore, the aforementioned computing board in this application is developed based on an FPGA chip and is used for high-speed acquisition, real-time analysis, frequency domain transformation (FFT), and frequency domain amplitude feature extraction of vibration signals. The fault feature frequency extraction module draws on filtering and signal processing methods used in audio vibration signal processing, retaining the feature values ​​generated by traditional vibration feature value calculation, including filtering and noise reduction, cepstral analysis, signal decomposition, deconvolution analysis, random resonance, and time-frequency analysis, to achieve fault feature frequency extraction.

[0121] More specifically, the aforementioned smart gateway is a programmable smart gateway module built on an ARM microcontroller, programmed in C language. This application aggregates the calculation results of the fault characteristic frequencies of the computing board and the data from other status sensors (i.e., operational data) via the 485 bus, and performs fault diagnosis according to the fault diagnosis process.

[0122] Continue to refer to Figure 6 The fault diagnosis system also includes a cloud server, which is connected to the smart gateway to receive and display the cause of the fault. The smart gateway packages the diagnostic results according to the Modbus IP protocol and sends them to the cloud server in real time for display, realizing visualization of diagnostic results, cloud monitoring, and cloud early warning.

[0123] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0124] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims. Furthermore, specific examples have been used in the specification to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application, and the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for diagnosing bearing faults, wherein the bearing is used in a screw compressor, characterized in that, The fault diagnosis method includes: S1: Acquire bearing operating data and vibration signals; S2: Compare the running data with the preset running data, and the vibration signal with the preset vibration signal; S3: If the operating data exceeds the preset operating data range, then determine and output the cause of the bearing failure; S4: If the operating data is within the preset operating data range and the vibration signal exceeds the preset vibration signal, then the vibration signal is subjected to frequency domain transformation to extract the fault feature frequency, and the fault cause of the bearing is determined and output based on the fault feature frequency; The operating data includes the current oil supply temperature of the main oil supply pipe in the screw compressor, the preset operating data includes the preset oil supply temperature, and the fault diagnosis method includes: Obtain the current oil supply temperature; Compare the current fuel supply temperature with the preset fuel supply temperature; If the current oil supply temperature is higher than the preset oil supply temperature, then the cause of the fault is determined and output as high oil supply temperature; The operating data includes the current inlet water temperature of the lubricating oil cooler, the preset operating data includes the preset inlet water temperature, and the fault diagnosis method includes: Obtain the current inlet water temperature; Compare the current inlet water temperature with the preset inlet water temperature; If the current inlet water temperature is higher than the preset inlet water temperature, the fault cause is determined and output as high inlet water temperature; if the current oil supply temperature is higher than the preset oil supply temperature and the current inlet water temperature is higher than the preset inlet water temperature, the fault cause is output as high inlet water temperature leading to insufficient heat exchange of the lubricating oil cooler.

2. The bearing fault diagnosis method according to claim 1, characterized in that, The operating data includes the current inlet water pressure of the lubricating oil cooler, the preset operating data includes the preset inlet water pressure, and the fault diagnosis method specifically includes: Obtain the current inlet water pressure; Compare the current inlet pressure with the preset inlet pressure; If the current inlet water pressure is lower than the preset inlet water pressure, then the cause of the fault is determined and output as low inlet water pressure; If the current oil supply temperature is higher than the preset oil supply temperature and the current water inlet pressure is lower than the preset water inlet pressure, then the fault cause is output as insufficient heat exchange of the lubricating oil cooler due to the current water inlet pressure. If the current oil supply temperature is higher than the preset oil supply temperature, the current inlet water temperature is lower than the preset inlet water temperature, and the current inlet water pressure is higher than the preset inlet water pressure, then the fault cause is output as insufficient heat exchange of the lubricating oil cooler due to scaling.

3. The bearing fault diagnosis method according to claim 2, characterized in that, The operating data includes the current oil supply pressure of the bearing, the preset operating data includes the preset oil supply pressure, and the fault diagnosis method specifically includes: Obtain the current oil supply pressure; Compare the current oil supply pressure with the preset oil supply pressure; If the current oil supply pressure is lower than the preset oil supply pressure, then the cause of the fault is determined and output as insufficient bearing oil supply and unreasonable oil distribution by the oil distributor.

4. The bearing fault diagnosis method according to claim 3, characterized in that, The bearing is a rolling bearing, the vibration signal is a vibration velocity signal, and step S4 specifically includes: S41: If the current oil supply temperature is lower than the preset oil supply temperature, the current water inlet temperature is lower than the preset water inlet temperature, the current water inlet pressure is higher than the preset water inlet pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, then the vibration signal is frequency domain transformed and the fault characteristic frequency is extracted. The cause of the bearing failure is determined and output based on the fault characteristic frequency and the natural frequency. S42: If the fault characteristic frequency exceeds the preset deviation range of the inherent frequency, then determine and output the cause of the fault.

5. The bearing fault diagnosis method according to claim 4, characterized in that, The rolling bearing includes a cage, the vibration velocity signal includes a cage vibration velocity signal, the fault characteristic frequency includes a cage fault characteristic frequency, and the natural frequency includes a cage natural frequency. The cage natural frequency f c for: Among them, R pmi R is the inner ring speed. pmo Where is the outer ring speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle; Step S42 specifically includes: If the cage failure characteristic frequency exceeds the cage natural frequency f c If the preset deviation range is met, the cause of the fault is determined and output as cage failure.

6. The bearing fault diagnosis method according to claim 4, characterized in that, The rolling bearing includes an inner ring, the vibration velocity signal includes an inner ring vibration velocity signal, the fault characteristic frequency includes an inner ring fault characteristic frequency, and the natural frequency includes an inner ring natural frequency. The inner ring natural frequency f i for: Where N is the number of rolling elements, R pmi Where is the inner ring speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle; Step S42 specifically includes: If the inner loop fault characteristic frequency exceeds the inner loop natural frequency f i If the preset deviation range is met, the cause of the fault is determined and output as an inner loop fault.

7. The bearing fault diagnosis method according to claim 4, characterized in that, The rolling bearing includes an outer ring, the vibration velocity signal includes an outer ring vibration velocity signal, the fault characteristic frequency includes an outer ring fault characteristic frequency, and the natural frequency includes an outer ring natural frequency. The outer ring natural frequency f o for: Where N is the number of rolling elements, R pmi Where is the outer ring speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle; Step S42 specifically includes: If the outer ring fault characteristic frequency exceeds the outer ring natural frequency f o If the preset deviation range is met, the cause of the fault is determined and output as an outer loop fault.

8. The bearing fault diagnosis method according to claim 4, characterized in that, The rolling bearing includes rolling elements, the vibration velocity signal includes the rolling element vibration velocity signal, the fault characteristic frequency includes the rolling element fault characteristic frequency, and the natural frequency includes the rolling element natural frequency. The rolling element natural frequency f b for: Among them, R pmi Where is the rolling element rotational speed, D is the pitch circle diameter of the rolling bearing, d is the rolling element diameter, and α is the contact angle; Step S42 specifically includes: If the characteristic frequency of the rolling element failure exceeds the natural frequency f of the rolling element b If the preset deviation range is met, the cause of the fault is determined and output as a rolling element fault.

9. The bearing fault diagnosis method according to claim 4, characterized in that, The bearing is a sliding bearing, the vibration signal is a displacement signal, and step S4 specifically includes: S410: If the current oil supply temperature is lower than the preset oil supply temperature, the current water inlet temperature is lower than the preset water inlet temperature, the current water inlet pressure is higher than the preset water inlet pressure, the current oil supply pressure is higher than the preset oil supply pressure, and the vibration signal exceeds the preset vibration signal, then the displacement signal is frequency domain transformed and the fault characteristic frequency is extracted. Based on the fault characteristic frequency, the cause of the sliding bearing failure is determined and output.

10. The bearing fault diagnosis method according to claim 9, characterized in that, The displacement signal includes the shaft center trajectory, as well as mutually perpendicular horizontal and vertical displacements. The step of determining and outputting the cause of the sliding bearing failure based on the failure frequency specifically includes: If the fault characteristic frequencies include the first, second, and third harmonics, and the vertical displacement is greater than the horizontal displacement, then the cause of the sliding bearing failure is determined and output as a large sliding bearing clearance. If the fault characteristic frequency includes 0.42 to 0.48 octaves and the shaft center trajectory is double elliptical, or if the fault characteristic frequency includes 0.42 to 0.48 octaves and the shaft center trajectory is disordered and does not coincide, then the cause of the sliding bearing fault is determined and output as the sliding bearing oil film whirl.

11. A bearing fault diagnosis system, employing the bearing fault diagnosis method according to any one of claims 1 to 10, characterized in that, include: A data acquisition module, installed on the bearing, is used to acquire the bearing's operating data and vibration signals; A digital-to-analog converter module is connected to the acquisition module via signal transmission. The computing board, connected to the digital-to-analog converter module, is used to extract the fault characteristic frequencies of the vibration signal; The intelligent gateway receives the fault characteristic frequency and operating parameters. The intelligent gateway includes a fault diagnosis module, which is used to analyze operating data and preset operating data, as well as vibration signals and preset vibration signals, to determine and output the cause of the bearing failure.

12. The bearing fault diagnosis system according to claim 11, characterized in that, Also includes: A cloud server, connected to the smart gateway, is used to receive and display the cause of the fault.

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