Method, device and medium for determining a component delivery state of a variable rotating device

Abstract

The invention relates to a method, a device and a medium for determining a component recession state of a variable-working-condition rotating device. The method comprises the following steps: based on historical vibration data and historical working condition data, calculating vibration spectrum baselines corresponding to a steady-state rotating device under different working conditions; acquiring a real-time frequency spectrum of the real-time vibration data based on the acquired real-time vibration data and the real-time working condition data; based on a real-time working condition and the vibration frequency spectrum base lines under the different working conditions, obtaining a vibration frequency spectrum base line corresponding to the real-time working condition; subtracting a vibration frequency spectrum base line corresponding to the real-time working condition from a real-time frequency spectrum of real-time vibration data to obtain a working condition-fault frequency spectrum; and based on the working condition-fault frequency spectrum, determining a frequency energy factor for determining a component recession state of the rotating equipment. Therefore, the part recession state of the rotating equipment under the variable working condition and the sub-health state can be effectively and accurately evaluated.

Description

Technical Field

[0001] The present invention generally relates to fault detection of rotating equipment, and in particular, to a method, computing device, and computer readable storage medium for determining a degradation state of a component of rotating equipment under varying operating conditions. Background Art

[0002] The traditional method for determining the decay state of rotating equipment components is mainly based on the extraction of spectrum and time domain features under stable working conditions, thereby building a decay model. Since the features under stable working conditions are relatively stable, the model performs better under stable working conditions. However, the rotating equipment of current enterprises is usually faced with variable working conditions. Therefore, the generalization performance of the traditional method for determining the decay state of rotating equipment components based on stable working conditions is poor, and it is difficult to accurately evaluate the decay trend of rotating equipment components under variable working conditions. In addition, the existing diagnostic technology is mainly based on healthy rotating equipment. Since healthy equipment has no obvious characteristic frequency in its initial state, a full-cycle evaluation model can be built. However, in actual production, due to the influence of comprehensive factors such as installation and environment, it is easy to cause the rotating equipment to be in a sub-healthy state from the beginning (for example, the sub-healthy state means that the vibration of the rotating equipment is close to the GB or ISO standard, but the energy of certain characteristic frequencies begins to appear in its spectrum). In the process of determining the decay state of the rotating equipment components, the influence of the initial energy concentration point will be magnified, resulting in the lack of highlighting the influence of other characteristic frequency points with fast energy changes. Therefore, conventional methods for determining the degradation status of components of rotating equipment cannot accurately assess the degradation status of components of rotating equipment that are in a sub-healthy state.

[0003] In summary, the conventional method for determining the degradation trend of components of rotating equipment has the disadvantage that it is impossible to accurately evaluate the degradation status of components of rotating equipment in variable operating conditions and sub-healthy conditions. Summary of the invention

[0004] In response to the above problems, the present invention provides a method, a computing device and a computer-readable storage medium for determining the degradation state of components of rotating equipment under variable operating conditions, which can effectively and accurately evaluate the degradation state of components of rotating equipment under variable operating conditions and sub-healthy states.

[0005] According to a first aspect of the present invention, a method for determining the degradation state of components of a rotating device under variable operating conditions is provided, the method comprising: caching acquired real-time vibration data and real-time operating condition data about the rotating device to generate historical vibration data and historical operating condition data, thereby calculating the vibration spectrum baseline under different operating conditions corresponding to the steady-state rotating device based on the vibration data and the operating condition data; acquiring a real-time spectrum of the real-time vibration data based on the acquired real-time vibration data and real-time operating condition data; acquiring a vibration spectrum baseline corresponding to the real-time operating condition based on the real-time operating condition and the vibration spectrum baselines under the different operating conditions; subtracting the vibration spectrum baseline corresponding to the real-time operating condition from the real-time spectrum of the real-time vibration data to obtain an operating condition-fault spectrum; and determining a frequency energy factor based on the operating condition-fault spectrum for determining the degradation state of components of the rotating device.

[0006] In some embodiments, based on the operating condition-fault spectrum, determining the change of the frequency energy factor includes: calculating the frequency energy factor corresponding to each characteristic frequency in the operating condition-fault spectrum based on the operating condition-fault spectrum; and determining the component degradation trend of the rotating equipment based on the change of the frequency energy factor.

[0007] In some embodiments, based on the operating condition-fault spectrum, calculating the frequency energy factor corresponding to each characteristic frequency in the operating condition-fault spectrum includes: based on the operating condition-fault spectrum, calculating the frequency energy proportion and the frequency energy change rate corresponding to each characteristic frequency in the operating condition-fault spectrum; and based on the frequency energy proportion and the frequency energy change rate, calculating the frequency energy factor corresponding to each characteristic frequency.

[0008] In some embodiments, based on the frequency energy proportion and the frequency energy change rate, calculating the frequency energy factor corresponding to each characteristic frequency includes: multiplying the frequency energy proportion and the frequency energy change rate to obtain the frequency energy factor corresponding to each characteristic frequency.

[0009] In some embodiments, calculating the frequency energy percentage and frequency energy change rate corresponding to each characteristic frequency in the operating condition-fault spectrum includes: calculating the frequency peak energy based on the amplitude of the operating condition-fault spectrum at each characteristic frequency; calculating the total frequency energy based on the sum of the squares of the amplitudes of the operating condition-fault spectrum under the current operating condition; and calculating the ratio of the frequency peak energy to the total frequency energy to obtain the frequency energy percentage.

[0010] In some embodiments, calculating the frequency energy proportion and frequency energy change rate corresponding to each characteristic frequency in the operating condition-fault spectrum includes: caching the calculated frequency energy under the predetermined operating condition to obtain the frequency energy sequence corresponding to the predetermined operating condition; and based on the frequency energy sequence, constructing a linear model of time and frequency energy to calculate the slope of the change of the frequency energy sequence corresponding to the predetermined operating condition, thereby obtaining the frequency energy change rate.

[0011] In some embodiments, determining the degradation state of a component of a rotating device includes: calculating the mean and standard deviation of multiple frequency energy factors under predetermined operating conditions at the current time; calculating a frequency energy factor threshold based on the mean and standard deviation; screening frequency energy factors that exceed the frequency energy factor threshold; and determining the degradation state of a component of the rotating device based on the characteristic frequency corresponding to the frequency energy factor that exceeds the frequency energy factor threshold, and the corresponding relationship between the characteristic frequency and the component failure of the rotating device.

[0012] In some embodiments, calculating the vibration spectrum baseline corresponding to different operating conditions of a steady-state rotating device includes: based on the historical vibration data and the historical operating condition data, respectively generating a vibration spectrum sequence and an operating condition data sequence of the rotating device in a steady state, the vibration spectrum sequence indicating the correspondence between time and the vibration spectrum, and the operating condition data sequence indicating the correspondence between time and the operating condition data; for each operating condition, calculating the vibration spectra of all detection positions so as to obtain the mean of the vibration spectra of all detection positions corresponding to each operating condition; based on the mean, generating the vibration spectrum baseline under different operating conditions of the steady-state rotating device.

[0013] In some embodiments, determining the component degradation trend of the rotating equipment based on the change of the frequency energy factor includes: determining the frequency energy factor exceeding the frequency energy factor threshold for different operating conditions at the current time point; performing weighted summation on the frequency energy factors exceeding the frequency energy factor threshold to obtain the weighted frequency energy factors corresponding to the different operating conditions at the current time point; sorting the weighted frequency energy factors corresponding to the different operating conditions to determine the component failure category most likely to occur under the different operating conditions at the current time point based on the sorting result; calculating the weighted frequency energy factor corresponding to each operating condition for multiple historical time points respectively, so as to construct a time series-frequency energy factor prediction model for each operating condition based on the weighted frequency energy factor corresponding to each operating condition at the current time point and multiple historical time points; based on the time series-frequency energy factor prediction model for each operating condition, predicting the weighted frequency energy factor corresponding to each operating condition at future time points, so as to determine the component failure category most likely to occur under each operating condition at future time points.

[0014] According to a second aspect of the present invention, a computing device is provided, which comprises: at least one processing unit; at least one memory, which is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, and when the instructions are executed by the at least one processing unit, the device performs the steps of the method according to the first aspect.

[0015] According to a third aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a machine, the method according to the first aspect is implemented.

[0016] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the invention, nor is it intended to limit the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a system implementing a method for determining a degradation state of a component of a rotating machine under varying operating conditions according to an embodiment of the present invention is shown.

[0019] Figure 2 A flow chart of a method for determining a degradation state of a component of a rotating device under varying operating conditions according to an embodiment of the present invention is shown.

[0020] Figure 3 The vibration spectrum and fault spectrum of the rotating equipment in a steady state according to an embodiment of the present invention are schematically shown.

[0021] Figure 4 The vibration spectrum and the fault spectrum of the rotating equipment in a fault state according to an embodiment of the present invention are schematically shown.

[0022] Figure 5 A flow chart of a method for determining a degradation state of a component of a rotating device according to an embodiment of the present invention is illustrated.

[0023] Figure 6 The flowchart of the method for determining the component failure category most likely to occur under each operating condition at a future time point according to an embodiment of the present invention is illustrated.

[0024] Figure 7 The diagram illustrates a schematic diagram of the changing trends of weighted frequency energy factors of different components according to an embodiment of the present invention.

[0025] Figure 8 The block diagram of an electronic device suitable for implementing the embodiments of the present invention is schematically shown. DETAILED DESCRIPTION

[0026] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0027] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects.

[0028] As described above, the conventional method for determining the degradation state of components of rotating equipment has the disadvantage that it is impossible to accurately evaluate the degradation state of components of rotating equipment in variable operating conditions and sub-healthy conditions.

[0029] In order to at least partially solve the above problems and one or more of other potential problems, the present invention proposes a method for determining the component decay state of a rotating device under variable working conditions. In the scheme of the present invention, by calculating the vibration spectrum baseline corresponding to different working conditions of the rotating device under steady state based on historical vibration data and working condition data, the present invention can not only effectively correct the influence of different working conditions on the vibration spectrum, improve the applicability and generalization of the rotating device under variable working conditions; but also obtain the original spectrum of the vibration spectrum of the rotating device under steady state. In addition, by obtaining the real-time spectrum of the real-time vibration data based on the acquired real-time vibration data and real-time working condition data; obtaining the vibration spectrum baseline corresponding to the working condition based on the real-time working condition and the vibration spectrum baseline under different working conditions; and subtracting the vibration spectrum baseline from the real-time spectrum of the real-time vibration data to obtain the working condition-fault spectrum, the present invention can effectively eliminate the influence of the sub-health state, reduce the influence of the main energy in the original spectrum, and thus highlight the change of the frequency component in the original spectrum. Furthermore, by determining the frequency energy factor based on the working condition-fault spectrum for determining the component decay state of the rotating device, the present invention can accurately evaluate the component decay state of the rotating device through the dimension of the frequency energy change. Therefore, the present invention can effectively correct the influence of variable operating conditions and sub-healthy states on the evaluation of the decay state of components of rotating equipment, thereby being able to effectively and accurately evaluate the decay trend of components of rotating equipment in variable operating conditions and sub-healthy states.

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and they cannot be understood as limiting the scope of protection of this application. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Figure 1 FIG. 1 is a schematic diagram of a system 100 for implementing a method for determining a degradation state of a component of a rotating device under varying operating conditions according to an embodiment of the present invention. Figure 1 As shown, the system 100 includes: a computing device 110 , a rotating device 130 , and an acceleration sensor 140 .

[0032] The rotating device 130 may be, for example but not limited to, a pump (eg, a variable frequency driven pump to be detected, or a power frequency driven pump), a motor, a bearing, or a generator.

[0033] Regarding the acceleration sensor 140 (for example, a three-axis acceleration sensor, or a single-axis sensor), it collects acceleration waveform data of various detection positions of the rotating device. Taking the pump as an example, the various detection positions of the rotating device include but are not limited to: the pump non-driving end detection position, the pump driving end detection position, the motor driving end detection position, and the motor non-driving end detection position. The acceleration waveform data for each detection position includes, for example: the acceleration waveform data of the vertical direction of the bearing, the acceleration waveform data of the horizontal direction of the bearing, and the acceleration waveform data of the axial direction of the bearing.

[0034] Regarding the computing device 110, it is used to determine the degradation state of the components of the rotating equipment under variable operating conditions. Specifically, the computing device 110 is used to calculate the vibration spectrum baseline under different operating conditions corresponding to the steady-state rotating equipment; and based on the acquired real-time vibration data and real-time operating condition data, obtain the real-time spectrum of the real-time vibration data. The computing device 110 is also used to obtain the vibration spectrum baseline corresponding to the real-time operating condition based on the real-time operating condition and the vibration spectrum baseline under the different operating conditions; subtract the vibration spectrum baseline corresponding to the real-time operating condition from the real-time spectrum of the real-time vibration data to obtain the operating condition-fault spectrum; and based on the operating condition-fault spectrum, determine the frequency energy factor for determining the degradation state of the components of the rotating equipment. In some embodiments, the computing device 110 is also used to determine the degradation trend of the components of the rotating equipment under variable operating conditions.

[0035] In some embodiments, the computing device 110 may have one or more processing units, including dedicated processing units such as GPU, FPGA and ASIC, and general processing units such as CPU. In addition, one or more virtual machines may also be running on each computing device. The computing device 110 includes, for example: a steady-state vibration spectrum baseline calculation unit 112, a real-time spectrum acquisition unit 114, a vibration spectrum baseline acquisition unit 116 corresponding to the real-time working condition, a working condition-fault spectrum acquisition unit 118, and a component decay state determination unit 120. The above-mentioned steady-state vibration spectrum baseline calculation unit 112, the real-time spectrum acquisition unit 114, the vibration spectrum baseline acquisition unit 116 corresponding to the real-time working condition, the working condition-fault spectrum acquisition unit 118, and the component decay state determination unit 120 can be configured on one or more computing devices 110.

[0036] Regarding the steady-state vibration spectrum baseline calculation unit 112, it is used to cache the acquired real-time vibration data and real-time operating condition data about the rotating equipment to generate historical vibration data and historical operating condition data, so as to calculate the vibration spectrum baseline corresponding to different operating conditions of the steady-state rotating equipment based on the historical vibration data and historical operating condition data.

[0037] The real-time spectrum acquisition unit 114 is used to acquire a real-time spectrum of the real-time vibration data based on the acquired real-time vibration data and real-time working condition data.

[0038] The vibration spectrum baseline acquisition unit 116 corresponding to the real-time working condition is used to acquire the vibration spectrum baseline corresponding to the real-time working condition based on the real-time working condition and the vibration spectrum baselines under the different working conditions.

[0039] The operating condition-fault spectrum obtaining unit 118 is used to subtract the vibration spectrum baseline corresponding to the real-time operating condition from the real-time spectrum of the real-time vibration data to obtain the operating condition-fault spectrum.

[0040] The component degradation state determining unit 120 is used to determine a frequency energy factor based on the operating condition-fault spectrum, so as to determine the component degradation state of the rotating equipment.

[0041] Figure 2 FIG. 2 is a flow chart of a method 200 for determining a component degradation state of a rotating device under variable operating conditions according to an embodiment of the present invention. It should be understood that the method 200 may be implemented, for example, in Figure 8 The electronic device 800 described above is executed. Figure 1 The method 200 is executed at the described computing device 110. It should be understood that the method 200 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0042] At step 202, the computing device 110 caches the acquired real-time vibration data and real-time operating condition data about the rotating equipment to generate historical vibration data and historical operating condition data, so as to calculate the vibration spectrum baseline corresponding to different operating conditions of the steady-state rotating equipment based on the historical vibration data and historical operating condition data.

[0043] Regarding the working condition, it includes speed, load, etc., and in some embodiments, the working condition also includes temperature, etc. For example, different speeds or different loads represent different working conditions. It should be understood that the working conditions of the present invention are multiple and exhaustive.

[0044] It should be understood that rotating equipment usually includes at least a shaft and a rotor (or a rotating part). Under different working conditions, the speed and load of the rotating equipment are different, and its vibration spectrum may also be different. Taking the speed as an example, the shaft may cause resonance at different speeds, especially when starting the rotation (for example, when starting the pump). When the speed of the shaft reaches a certain value, its vibration will be significantly aggravated; if the speed exceeds this value, its vibration will be significantly reduced. It can be seen that the speed has a correlation effect on the vibration of the rotating equipment. Taking the load as an example, for example, when the pump is working, the liquid will produce liquid vaporization at the inlet of the impeller due to a certain vacuum pressure. The vaporized bubbles will peel off the metal surface of the impeller under the impact movement of the liquid particles, thereby destroying the metal such as the impeller, that is, cavitation, and the vacuum pressure is the vaporization pressure. It can be seen that under different loads, the vibration of the rotating equipment and the decay of the components are also correlated. Therefore, constructing a vibration spectrum baseline under different working conditions can be effectively used to correct the impact of different working conditions on the spectrum.

[0045] A method for obtaining a vibration spectrum baseline corresponding to different operating conditions of a rotating device in a steady state, for example, includes: the computing device 110 generates a vibration spectrum sequence and an operating condition data sequence of the rotating device in a steady state based on the historical vibration data and the historical operating condition data, respectively, wherein the vibration spectrum sequence indicates the corresponding relationship between time and the vibration spectrum, and the operating condition data sequence indicates the corresponding relationship between time and the operating condition data; for each operating condition, the vibration spectra of all detection positions are calculated to obtain the mean of the vibration spectra of all detection positions corresponding to each operating condition; based on the mean, a vibration spectrum baseline of the rotating device in a steady state under different operating conditions is generated.

[0046] Regarding real-time vibration data, for example, it is obtained from the acceleration waveform data of each detection position of the three-axis acceleration sensor. Each detection position includes, for example: a pump non-drive end measurement point, a pump drive end measurement point, a motor drive end measurement point, and a motor non-drive end measurement point. The acceleration waveform data for each detection position includes, for example: acceleration waveform data in the vertical direction of the bearing, acceleration waveform data in the horizontal direction of the bearing, and acceleration waveform data of the axial data of the bearing. The sampling period of real-time vibration data is, for example, not less than 30 minutes each time.

[0047] Regarding the method for caching real-time vibration data, it includes, for example: caching the real-time vibration data of the rotating equipment in operation acquired within a predetermined buffering period, so as to form a vibration data sequence based on the cached real-time vibration data (i.e., historical vibration data). The predetermined buffering period is, for example, but not limited to, greater than or equal to 2 weeks.

[0048] The following expression (1) illustrates a vibration data sequence.

[0049] (1).

[0050] In the above expression (1), Represents the vibration acceleration of the mth detection position. Represents the time at the nth time point.

[0051] The method for caching operating condition data includes, for example, caching the operating condition data of the rotating equipment in operation acquired within a predetermined buffer period, so as to form an operating condition data sequence based on the cached operating condition data. The predetermined buffer period is, for example but not limited to, greater than or equal to 2 weeks.

[0052] The following expression (2) illustrates the operating condition data sequence.

[0053] (2).

[0054] In the above expression (2), Represents the kth operating condition. Represents the time at the nth time point.

[0055] The method of performing signal processing on a vibration data sequence to obtain the spectrum of real-time vibration data is described below in conjunction with formulas (3) to (6). The following formulas (3) to (6) schematically illustrate the algorithm for performing signal transformation on a vibration acceleration signal. Among them, formula (3) schematically illustrates the calculation method of a vibration acceleration signal at any frequency.

[0056] (3).

[0057] In the above formula (3), Represents the vibration acceleration signal at frequency The Fourier components under The coefficient of . j represents an imaginary number. represents frequency. t represents time.

[0058] The following formula (4) schematically shows: when the initial velocity component is 0, the velocity signal component obtained by time-domain integration of the vibration acceleration signal component.

[0059] (4).

[0060] In the above formula (4), Represents the speed signal component. V represents the corresponding The coefficient of . Represents the acceleration signal component. Represents the integration variable with respect to the acceleration signal component being integrated. A small increment in the integrated variable representing the acceleration signal component.

[0061] The following formula (5) schematically shows that when the initial velocity and initial displacement components are both 0, the displacement signal component is obtained by performing two time domain integrations on the acceleration signal component.

[0062] (5).

[0063] In the above formula (5), Represents the displacement signal at frequency The Fourier components under The coefficient of . Represents the acceleration signal component. Represents the integral The integral variable of the velocity signal component. The integral variable representing the velocity signal component of small increments.

[0064] The present invention mainly adopts fast Fourier transform for signal transformation of spectrum, which is a fast algorithm of discrete Fourier transform. The following formula (4) schematically shows the algorithm of discrete Fourier transform.

[0065] (6).

[0066] In the above formula (6), The representative frequency is The amplitude and phase information of the sine and cosine components, Represents the nth point in a discrete time series. N represents the length of the series.

[0067] Regarding the vibration spectrum sequence of the rotating device in a steady state, the following expression (7) schematically shows: the vibration spectrum sequence of the rotating device in a steady state.

[0068] (7).

[0069] In the above expression (7), Represents the vibration spectrum of the mth detection position. Represents the time at the nth time point. Represents the vibration spectrum of the 1st to mth detection positions corresponding to the time of the nth time point.

[0070] The corresponding relationship between the working condition of the rotating equipment in steady state and the vibration spectrum is schematically shown in the following expression (8).

[0071] (8).

[0072] In the above expression (8), Represents the vibration spectrum of the mth detection position. Represents the time at the nth time point. Represents the kth operating condition.

[0073] It should be understood that each operating condition corresponds to multiple vibration spectra, so the computing device 110 obtains multiple groups of vibration spectra corresponding to each operating condition. The following expression (9) schematically shows: multiple groups of vibration spectra corresponding to one operating condition.

[0074] (9).

[0075] In the above expression (9), Represents the vibration spectrum of the mth detection position. Represents the time of the i-th time point. Represents the kth operating condition corresponding to the ith time point. represent The time points covered by this working condition, and the corresponding multiple groups of vibration spectra (i.e., the vibration spectra of the 1st to mth detection positions).

[0076] For example, the following expression (10) schematically shows the vibration spectrum baseline corresponding to a working condition.

[0077] (10).

[0078] In the above expression (10), Representative working conditions The mean value of the vibration spectrum of the corresponding m-th detection position. m represents the m-th detection position. Represents the time of the i-th time point. Represents the kth operating condition corresponding to the ith time point. Representative working conditions The corresponding vibration spectrum baseline.

[0079] At step 204 , the computing device 110 acquires a real-time frequency spectrum of the real-time vibration data based on the acquired real-time vibration data and the real-time operating condition data.

[0080] For example, the computing device 110 obtains real-time vibration data and real-time operating condition data of the rotating equipment at the current time; and performs signal processing based on the obtained vibration data and operating condition data at the current time to generate a vibration spectrum regarding the real-time vibration data.

[0081] Regarding the method of generating the vibration spectrum of the real-time vibration data, the computing device 110 adopts, for example, the method shown in the above-mentioned formulas (3) to (7), which will not be described in detail here.

[0082] At step 206 , the computing device 110 acquires a vibration spectrum baseline corresponding to the real-time operating condition based on the real-time operating condition and the vibration spectrum baselines under the different operating conditions.

[0083] The computing device 110 queries the vibration spectrum baselines under different working conditions of the rotating equipment in a steady state based on the real-time working condition, so as to obtain the vibration spectrum baseline corresponding to the real-time working condition.

[0084] At step 208 , the computing device 110 subtracts the vibration spectrum baseline corresponding to the real-time operating condition from the real-time spectrum of the real-time vibration data to obtain an operating condition-fault spectrum.

[0085] For example, the computing device 110 calculates the difference between the real-time spectrum of the acquired real-time vibration data and the acquired vibration spectrum baseline corresponding to the real-time operating condition, and takes an absolute value of the calculated difference to obtain the operating condition-fault spectrum.

[0086] For example, Figure 3 The vibration spectrum and fault spectrum of the rotating equipment in a steady state according to an embodiment of the present invention are schematically shown. Figure 3 The mark 310 in the middle indicates the original vibration spectrum of the rotating device in a steady state, in which the first, second and third frequencies are relatively high. The mark 312 indicates the vibration spectrum of the real-time (for example, at a certain time) vibration data of the rotating device in a steady state, in which the first, second and third frequencies are also relatively high. The vibration spectrum on the right side indicated by the mark 320 is the fault spectrum of the rotating device in a steady state. The fault spectrum 320 of the rotating device in a steady state shows a lot of noise, and no obvious energy peak is seen, in which the first, second and third frequencies are eliminated.

[0087] For another example, Figure 4 The vibration spectrum and fault spectrum of the rotating equipment under the fault state according to the embodiment of the present invention are schematically shown. Figure 4 The mark 410 indicates the original vibration spectrum of the rotating device in the fault state, and the mark 412 indicates the vibration spectrum of the real-time (for example, at a certain time) vibration data of the rotating device in the fault state. The mark 420 indicates the fault spectrum of the rotating device in the fault state. Figure 4 As shown, in addition to the rotation frequency peak, other frequency peaks appear in the fault spectrum 420 of the rotating equipment in the fault state, which indicates that the rotating equipment has an abnormality at this characteristic frequency.

[0088] Therefore, by subtracting the vibration spectrum baseline from the real-time spectrum of the real-time vibration data to obtain the operating condition-fault spectrum, the present invention can reduce the influence of the main energy in the original spectrum corresponding to the steady-state rotating equipment, thereby highlighting the changes in frequency components in the vibration spectrum.

[0089] At step 210 , the computing device 110 determines a frequency energy factor based on the operating condition-fault spectrum to determine a component degradation state of the rotating equipment.

[0090] It should be understood that the condition-fault spectrum shows that the evolution of faults in rotating equipment is related to changes in certain specific frequencies.

[0091] Regarding the method for determining the frequency energy factor, in some embodiments, it includes, for example: the computing device 110 calculates the frequency energy proportion and the frequency energy change rate corresponding to each characteristic frequency in the operating condition-fault spectrum based on the operating condition-fault spectrum; and based on the frequency energy proportion and the frequency energy change rate, calculates the frequency energy factor corresponding to each characteristic frequency. It should be understood that by taking into account the influence of the frequency energy proportion and the frequency energy change rate when calculating the frequency energy factor, the present invention can highlight the influence of characteristic frequency points with fast energy changes, for example, for points with small initial energy, the rate of change may be relatively fast, and the influence of the fault that dominates as the fault evolves.

[0092] Regarding the calculation method of frequency energy proportion, it includes, for example: calculating the frequency peak energy based on the amplitude of the working condition-fault spectrum under each characteristic frequency; calculating the total frequency energy based on the square sum of the amplitude of the working condition-fault spectrum under the current working condition; and calculating the ratio of the frequency peak energy to the total frequency energy to obtain the frequency energy proportion. For example, the frequency energy proportion of each characteristic frequency is obtained. The frequency energy proportion is used to characterize the energy proportion of the frequency energy of the characteristic frequency in the entire spectrum. The following formulas (11) to (13) show the algorithm for the frequency energy proportion of any characteristic frequency in the working condition-fault spectrum.

[0093] (11).

[0094] (12).

[0095] (13).

[0096] In the above formulas (11) to (13), Represents peak energy. Represents the amplitude of the operating condition-fault spectrum at a predetermined frequency (characteristic frequency). Represents the total frequency energy. Represents the frequency energy ratio.

[0097] Regarding the frequency energy change rate, it indicates how fast the frequency energy changes within a certain period of time. In some embodiments, regarding the method for obtaining the frequency energy change rate, it includes, for example: the computing device 110 caches the calculated frequency energy under the predetermined working condition to obtain the frequency energy sequence corresponding to the predetermined working condition; and based on the frequency energy sequence, constructs a linear model of time and frequency energy to calculate the slope of the change of the frequency energy sequence corresponding to the predetermined working condition, thereby obtaining the frequency energy change rate.

[0098] For example, the following expression (14) shows the frequency energy sequence corresponding to the predetermined operating condition.

[0099] (14).

[0100] In the above formula (14), represent The frequency energy of time. Represents time. Represents working condition. represent The corresponding frequency energy sequence under working conditions.

[0101] Regarding the frequency energy factor, in some embodiments, the computing device 110 may multiply the frequency energy proportion and the frequency energy change rate to obtain the frequency energy factor.

[0102] The following formula (15) shows the algorithm of the frequency energy factor.

[0103] (15).

[0104] In the above formula (15), Represents the rate of change of frequency energy. Represents the frequency energy ratio. Represents the frequency energy factor.

[0105] Regarding the method for determining the degradation state of a component of a rotating device, in some embodiments, the computing device 110 may calculate the frequency energy factor of each frequency at each time point for each operating condition, thereby constructing a time series-frequency energy factor prediction model for each frequency energy factor under each operating condition, for example, the operating condition The time-frequency energy factor prediction model for the predetermined frequency is: It should be understood that the time series-frequency energy factor prediction model is, for example, a time series model or a linear model.

[0106] The following table 1 shows the working conditions The frequency energy factor at different times.

[0107] Table 1 .

[0108] The following formula (16) shows Time-frequency energy factor prediction model under working conditions.

[0109] (16).

[0110] In the above formula (16), Represents working condition. Represents the frequency energy factor of the second frequency. Represents the frequency energy factor of the first frequency. Represents the first point in time. Represents the second time point. Represents the frequency energy factor of each characteristic frequency at the first time point T1. Represents the frequency energy factor of each characteristic frequency at the second time point T2.

[0111] In the above scheme, by calculating the vibration spectrum baseline corresponding to different working conditions of the rotating equipment in steady state based on historical vibration data and working condition data, the present invention can not only effectively correct the influence of different working conditions on the vibration spectrum, improve the applicability and generalization of the rotating equipment under variable working conditions; but also obtain the original spectrum of the vibration spectrum of the rotating equipment in steady state. In addition, by obtaining the real-time spectrum of the real-time vibration data based on the acquired real-time vibration data and real-time working condition data; obtaining the vibration spectrum baseline corresponding to the working condition based on the real-time working condition and the vibration spectrum baseline under different working conditions; and subtracting the vibration spectrum baseline from the real-time spectrum of the real-time vibration data to obtain the working condition-fault spectrum, the present invention can effectively eliminate the influence of the sub-health state and reduce the influence of the main energy in the original spectrum, thereby highlighting the change of the frequency component in the original spectrum. Furthermore, by determining the frequency energy factor based on the working condition-fault spectrum to determine the component decay state of the rotating equipment, the present invention can accurately evaluate the component decay state of the rotating equipment through the dimension of the frequency energy change. Therefore, the present invention can effectively correct the influence of variable operating conditions and sub-healthy states on the evaluation of the decay state of components of rotating equipment, thereby being able to effectively and accurately evaluate the decay trend of components of rotating equipment in variable operating conditions and sub-healthy states.

[0112] Figure 5 FIG. 5 is a flow chart of a method 500 for determining a degradation state of a component of a rotating device according to an embodiment of the present invention. It should be understood that the method 500 may be implemented, for example, in Figure 8 The electronic device 800 described above is executed. Figure 1The method 500 is executed at the described computing device 110. It should be understood that the method 500 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0113] At step 502 , the computing device 110 calculates the mean and standard deviation of a plurality of frequency energy factors under a predetermined operating condition at the current time.

[0114] The predetermined operating condition may be, for example, a standard operating condition or a non-rated operating condition.

[0115] Regarding the method for calculating the mean and standard deviation of multiple frequency energy factors, for example, it is: based on the multiple frequency energy factors included in the frequency energy factor sequence under the predetermined working condition at the current time, the mean and standard deviation are calculated.

[0116] Regarding the frequency energy factor sequence, it is shown in the following formula (17), for example.

[0117] (17).

[0118] In the above formula (17), Represents a frequency energy factor sequence composed of multiple frequency energy factors under a certain working condition. represents the frequency energy factor for the second frequency. represents the frequency energy factor for the first frequency.

[0119] At step 504 , the computing device 110 calculates a frequency energy factor threshold based on the mean and the standard deviation.

[0120] Regarding the frequency energy factor threshold, it is calculated based on the following formula (18), for example.

[0121] (18).

[0122] In the above formula (18), Represents the average value of multiple frequency energy factors under a certain working condition at the current time. Represents the standard deviation of multiple frequency energy factors under a certain working condition at the current time. Represents the frequency energy factor threshold.

[0123] At step 506 , the computing device 110 screens the frequency energy factors that exceed a frequency energy factor threshold.

[0124] It should be understood that since the frequency energy factor is calculated based on the operating condition-fault spectrum, and since the operating condition-fault spectrum eliminates the influence of the original high energy in the original spectrum, for a certain operating condition, all frequency energy factors should be in a random fluctuation state. If an abnormal frequency energy factor exceeds the frequency energy factor threshold, it means that the fault corresponding to this frequency energy factor is very likely to occur.

[0125] At step 508 , the computing device 110 determines the degradation state of the component of the rotating device based on the characteristic frequency corresponding to the frequency energy factor exceeding the frequency energy factor threshold and the correspondence between the characteristic frequency and the component failure of the rotating device.

[0126] Regarding the correspondence between the characteristic frequency and the component failure of the rotating device, for example, it is a correspondence table between the abnormal frequency energy factor and the component failure. The following Table 2 schematically shows the association table between the component failure and the characteristic frequency of the rotating device. For example, if the characteristic frequency corresponding to the frequency energy factor exceeding the frequency energy factor threshold is 2 times the radial rotation frequency and 1 times the axial rotation frequency, it is determined that the component of the rotating device has a coupling failure.

[0127] Table 2 .

[0128] In some embodiments, if the computing device 110 determines that multiple abnormal frequency energy factors jointly affect a component of the rotating device, a weighted sum is performed on the multiple abnormal frequency energy factors to determine the degradation state of the component of the rotating device based on the weighted sum result.

[0129] Figure 6 A flow chart of a method 600 for determining the most likely component failure category to occur under each operating condition at a future time point according to an embodiment of the present invention is illustrated. Figure 7 FIG. 6 is a schematic diagram showing the variation trend of the weighted frequency energy factors of different components according to an embodiment of the present invention. It should be understood that the method 600 may be used, for example, in Figure 8 The electronic device 800 described above is executed. Figure 1 The method 600 is executed at the described computing device 110. It should be understood that the method 600 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0130] At step 602 , the computing device 110 determines a frequency energy factor exceeding a frequency energy factor threshold for different operating conditions at the current time point.

[0131] At step 604 , if the computing device 110 performs weighted summation on the frequency energy factors exceeding the frequency energy factor threshold, so as to obtain the weighted frequency energy factors corresponding to different operating conditions at the current time point.

[0132] For example, as shown in Table 3 below. For working condition W1, its abnormal frequency energy factor is The weighted frequency energy factor corresponding to working condition W1 is + The weighted frequency energy factor corresponds to component failure 1.

[0133] Table 3 .

[0134] At step 606, the computing device 110 sorts the weighted frequency energy factors corresponding to different operating conditions, so as to determine the most likely component failure category under different operating conditions at the current time point based on the sorting result. Thus, the present invention can identify the current state of the components of the rotating equipment.

[0135] At step 608, the computing device 110 calculates the weighted frequency energy factor corresponding to each operating condition for multiple historical time points, so as to construct a time series-frequency energy factor prediction model for each operating condition based on the weighted frequency energy factor corresponding to each operating condition at the current time point and multiple historical time points.

[0136] The method of calculating the weighted frequency energy factor corresponding to each operating condition for multiple historical time points is similar to the method of calculating the weighted frequency energy factor corresponding to each operating condition for the current time in steps 602 and 604, and will not be repeated here.

[0137] It should be understood that the time series-frequency energy factor prediction model indicates the changing trend of the frequency energy factor under different working conditions in the time series dimension. Figure 7 As shown, each curve represents the change trend of the weighted frequency energy factor of different components, which indicates the evolution trend of the failure of the associated components. The solid line part of each curve is the weighted frequency energy factor at the current time point and the historical time point, and the dotted line is the trend of the weighted frequency energy factor at the predicted future time point.

[0138] At step 610 , the computing device 110 predicts the weighted frequency energy factor corresponding to each operating condition at a future time point based on the time-frequency energy factor prediction model for each operating condition, so as to determine the component failure category most likely to occur under each operating condition at the future time point.

[0139] Therefore, the present invention can predict the frequency energy factor corresponding to a certain period of time in the future, and further can be used to evaluate the development trend of components of future rotating equipment.

[0140] For example, as shown in Table 4 below. The weighted frequency energy factor at the current time point is The weighted frequency energy factor corresponds to the coupling failure. The weighted frequency energy factor at the future time point 1 is .

[0141] Table 4 .

[0142] In some embodiments, the weighted frequency energy factor at the current time point can be given a higher weight, for example, the closer the frequency energy factor is to the current time point, the higher the weight is. On the other hand, in the later stage of the deployment of the time-frequency energy factor prediction model, a higher weight can be given to the future state evaluation results of the component. This is because the data cache of the early deployment of the time-frequency energy factor prediction model is small, and the prediction model will become more accurate as the data accumulates.

[0143] By adopting the above means, the present invention can evaluate the status of the components of the rotating equipment under each working condition in real time, and can effectively evaluate the degradation trend of the components of the rotating equipment in the future.

[0144] Figure 8 The block diagram of an electronic device 800 suitable for implementing an embodiment of the present invention is schematically shown. The electronic device 800 may be a device for implementing Figure 2 , 5 , 6. The method 200, 500, 600 shown in FIG. Figure 8 As shown, the electronic device 800 includes a central processing unit (i.e., CPU 801), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (i.e., ROM 802) or computer program instructions loaded from a storage unit 808 to a random access memory (i.e., RAM 803). In RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input / output interface (i.e., I / O interface 805) is also connected to the bus 804.

[0145] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, and a storage unit 808. The CPU 801 performs the various methods and processes described above, such as executing methods 200, 500, and 600. For example, in some embodiments, methods 200, 500, and 6000 can be implemented as a computer software program, which is stored in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the CPU 801, one or more operations of the methods 200, 500, and 600 described above can be performed. Alternatively, in other embodiments, the CPU 801 can be configured to perform one or more actions of the methods 200, 500, and 600 by any other appropriate means (for example, by means of firmware).

[0146] It should be further noted that the present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0147] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0148] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0149] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present invention.

[0150] Various aspects of the present invention are described herein with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to an embodiment of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0151] These computer-readable program instructions can be provided to a processor in a voice interaction device, a general-purpose computer, a special-purpose computer, or a processing unit of other programmable data processing devices, thereby producing a machine, so that when these instructions are executed by a processing unit of a computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions enable the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0152] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0153] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the equipment, method and computer program product according to multiple embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of this module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0154] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

[0155] The above are only optional embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining the degradation state of a component of a rotating device under variable operating conditions, characterized in that: include: Cache the acquired real-time vibration data and real-time working condition data about the rotating equipment to generate historical vibration data and historical working condition data, so as to calculate the vibration spectrum baseline corresponding to different working conditions of the rotating equipment in a steady state based on the historical vibration data and historical working condition data; Based on the acquired real-time vibration data and real-time working condition data, a real-time spectrum of the real-time vibration data is acquired; Based on the real-time working condition and the vibration spectrum baselines under the different working conditions, obtaining the vibration spectrum baseline corresponding to the real-time working condition; Subtracting a vibration spectrum baseline corresponding to the real-time working condition from a real-time spectrum of the real-time vibration data to obtain a working condition-fault spectrum; as well as Based on the operating condition-fault spectrum, a frequency energy factor is determined for determining a degradation state of a component of the rotating equipment.

2. The method according to claim 1, characterized in that Based on the operating condition-fault spectrum, determining the change of the frequency energy factor includes: Based on the working condition-fault spectrum, calculating the frequency energy factor corresponding to each characteristic frequency in the working condition-fault spectrum; and Determine the component degradation trend of rotating equipment based on the change of frequency energy factor.

3. The method according to claim 2, characterized in that Based on the working condition-fault spectrum, the frequency energy factor corresponding to each characteristic frequency in the working condition-fault spectrum is calculated including: Based on the working condition-fault spectrum, calculate the frequency energy proportion and frequency energy change rate corresponding to each characteristic frequency in the working condition-fault spectrum; and Based on the frequency energy proportion and the frequency energy change rate, a frequency energy factor corresponding to each characteristic frequency is calculated.

4. The method according to claim 3, characterized in that Based on the frequency energy proportion and the frequency energy change rate, calculating the frequency energy factor corresponding to each characteristic frequency includes: The frequency energy proportion and the frequency energy change rate are multiplied to obtain the frequency energy factor corresponding to each characteristic frequency.

5. The method according to claim 3, characterized in that: The frequency energy proportion and frequency energy change rate corresponding to each characteristic frequency in the calculation condition-fault spectrum include: Calculate the frequency peak energy based on the amplitude of the operating condition-fault spectrum at each characteristic frequency; Calculating the total frequency energy based on the sum of the squares of the amplitudes of the operating condition-fault spectrum under the current operating condition; and The ratio of the frequency peak energy to the total frequency energy is calculated to obtain the frequency energy contribution.

6. The method according to claim 3, characterized in that The frequency energy proportion and frequency energy change rate corresponding to each characteristic frequency in the calculation condition-fault spectrum include: caching the calculated frequency energy under the predetermined working condition so as to obtain a frequency energy sequence corresponding to the predetermined working condition; and Based on the frequency energy sequence, a linear model of time and frequency energy is constructed to calculate the slope of the frequency energy sequence change corresponding to the predetermined working condition, thereby obtaining the frequency energy change rate.

7. The method according to claim 2, characterized in that Determining the degradation state of rotating equipment components includes: Calculate the mean and standard deviation of multiple frequency energy factors under the current time and predetermined working conditions; Based on the mean and standard deviation, the frequency energy factor threshold is calculated; Screening frequency energy factors exceeding a frequency energy factor threshold; and Based on the characteristic frequency corresponding to the frequency energy factor exceeding the frequency energy factor threshold and the corresponding relationship between the characteristic frequency and the component failure of the rotating equipment, the degradation state of the component of the rotating equipment is determined.

8. The method according to claim 1, characterized in that: Calculation of the vibration spectrum baseline for different operating conditions of the rotating equipment corresponding to the steady state includes: Based on the historical vibration data and the historical operating condition data, respectively generate a vibration spectrum sequence and an operating condition data sequence of the rotating equipment in a steady state, wherein the vibration spectrum sequence indicates a corresponding relationship between time and the vibration spectrum, and the operating condition data sequence indicates a corresponding relationship between time and the operating condition data; For each working condition, the vibration spectra of all detection positions are calculated to obtain the average value of the vibration spectra of all detection positions corresponding to each working condition; Based on the mean value, a vibration spectrum baseline of the rotating equipment in different working conditions in a steady state is generated.

9. The method according to claim 1, characterized in that: Based on the change of frequency energy factor, the degradation trend of rotating equipment components is determined including: According to different working conditions at the current time point, determine the frequency energy factor exceeding the frequency energy factor threshold; Performing weighted summation on the frequency energy factors exceeding the frequency energy factor threshold, so as to obtain the weighted frequency energy factors corresponding to different working conditions at the current time point; Sorting the weighted frequency energy factors corresponding to different operating conditions, so as to determine the component failure category most likely to occur under different operating conditions at the current time point based on the sorting results; For multiple historical time points, respectively calculating the weighted frequency energy factor corresponding to each operating condition, so as to construct a time series-frequency energy factor prediction model for each operating condition based on the weighted frequency energy factor corresponding to each operating condition at the current time point and multiple historical time points; and Based on the time series-frequency energy factor prediction model for each operating condition, the weighted frequency energy factor corresponding to each operating condition at a future time point is predicted to determine the component failure category most likely to occur under each operating condition at the future time point.

10. A computing device comprising: at least one processing unit; At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the computing device to perform the steps of the method according to any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 1 to 9 when executed by a machine.

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