Rubbing fault quantitative diagnosis method based on harmonic phase

Abstract

The invention discloses a rub-impact fault quantitative diagnosis method based on a harmonic phase, and relates to the technical field of rub-impact fault diagnosis based on vibration, the method realizes the quantitative diagnosis of the rub-impact fault based on the processing of the harmonic phase, has strong directivity when the rub-impact fault is diagnosed, and the feasibility of practical application is enhanced. According to the technical scheme, the method comprises the following steps that harmonic phases in vibration data in a normal state are extracted, an optimal harmonic number selection criterion is constructed, and the optimal harmonic number is selected. And calculating a fault characteristic index fused with a multi-harmonic phase, and then carrying out multi-component fusion to form a system normal state threshold. And in combination with a normal state phase processing method and an optimal harmonic number selection result, extracting a harmonic phase in the vibration data in an undetermined state and performing preprocessing. And calculating a fault diagnosis index based on the index result of the normal state, comparing the numerical value of the fault diagnosis index with a normal state threshold, and judging the state of the system.

Description

Technical Field

[0001] The invention relates to the technical field of vibration-based rubbing fault diagnosis, and in particular to a harmonic phase-based rubbing fault quantitative diagnosis method. Background Art

[0002] The rotor system is the core of high-end rotating machinery. Accurate status assessment and fault diagnosis are key to ensuring the normal operation of the equipment. In order to improve the performance and efficiency of the equipment, the rotor system with a large number of blades is placed in a casing with a very small gap with it, which makes it very easy for the blades and casing to come into contact, causing friction failures and affecting the normal operation of the equipment. In order to ensure the safety and stable production of high-end rotating machinery, it is extremely important to study the friction failure mechanism and then construct indicators to identify the system status.

[0003] At present, the latest research results on rubbing fault diagnosis are to construct indicators based on the system nonlinear output frequency response functions (NOFRFs) to judge the system status, but this method has obvious limitations in practical applications.

[0004] The traditional method of diagnosing rubbing faults based on the effective value (RMS) of system vibration and a single change in the amplitude of some harmonics (a decrease in the amplitude of the rotor fundamental frequency) has the problem of lack of directionality, because in addition to rubbing faults, other typical rotor faults such as rotor imbalance can also cause changes in the above-mentioned characteristic values.

[0005] Although the rubbing fault diagnosis method based on nonlinear output frequency response function NOFRFs formed by current research avoids the influence of other typical rotor faults under stable working conditions, its practicality in the actual fault diagnosis environment is questionable. On the one hand, under the premise of known input, two tests with the same waveform but different amplitudes are required to obtain the value of NOFRFs, which is almost impossible in actual application. On the other hand, the premise of known system input is too harsh. In actual situations, the input excitation of the system is almost unobtainable. Therefore, it is difficult to effectively apply NOFRFs in the diagnosis of actual operating faults (such as rubbing).

[0006] Therefore, the existing solutions cannot solve the problem that the diagnosis of rubbing faults is not very directional and does not have good practicality. Summary of the invention

[0007] In view of this, the present invention provides a quantitative diagnosis method for rubbing faults based on harmonic phase. The method realizes quantitative diagnosis of rubbing faults based on processing of harmonic phase, has strong directionality when diagnosing rubbing faults, and enhances feasibility of practical application.

[0008] To achieve the above object, the technical solution of the present invention comprises the following steps: S1. Extract the harmonic phase from the vibration data under normal conditions.

[0009] S2. Based on the harmonic phase extracted from the normal state, the optimal harmonic number selection criterion is constructed to select the optimal harmonic number that can take into account both the sensitivity and robustness of the indicator.

[0010] S3. Based on the optimal harmonic number, calculate the fault characteristic index of the fused multi-harmonic phase, and based on the fault characteristic index, perform multi-component fusion to form the system normal state threshold.

[0011] S4. Combining the normal state phase processing method and the optimal harmonic number selection result, extract the harmonic phase in the vibration data under the pending state and perform preprocessing.

[0012] S5. Based on the indicator results of the normal state, calculate the fault diagnosis indicator, and compare the fault diagnosis indicator value with the normal state threshold to determine the state of the system.

[0013] Furthermore, in S1, the harmonic phase in the vibration data under the normal state is extracted, and the specific steps are as follows: First, preprocess the harmonic phase under normal conditions: when hour, Perform the following preprocessing:

[0014] in, Indicates the harmonic phase of the nth order and kth segment of data; Indicates the harmonic phase of the nth order, k+1th segment of data. The order indicates the multiple of the harmonic compared to the rotation frequency; when The following preprocessing is performed:

[0015] in is the maximum change in the phase of the nth order harmonic, is the harmonic phase of the nth order.

[0016] Finally, the harmonic phase is subtracted from the mean value of each harmonic and then added with 180° to obtain the extracted result of the harmonic phase.

[0017] Furthermore, S2 is specifically: The system is stably excited. Under normal conditions, the phase of the harmonics is stable. If the system fluctuates under normal conditions, it is due to the influence of noise.

[0018] Through the vibration data under normal conditions, the effect of noise is fully considered, the harmonic phase is extracted, and the variance of the harmonic phase is calculated based on the , N is the upper limit of the harmonic number, and the optimal harmonic number selection criterion S is constructed, and its expression is:

[0019] in represent variance and mean respectively; when S is the largest, the value of N is the optimal harmonic number, that is, this harmonic number is considered to be the optimal harmonic number.

[0020] Further, in S3, based on the optimal harmonic number, the fault characteristic index of the fused multi-harmonic phase is calculated. Specifically, the fault characteristic index of the fused multi-harmonic phase is P:

[0021] in They represent the harmonic order, exponential constant and the optimal harmonic number selected respectively.

[0022] Furthermore, in S3, based on the fault characteristic indicators, multi-component fusion is performed to form the system normal state threshold, which is specifically: Given the expression of KL divergence, the indicator PKL is constructed by amplifying the fault difference through normal state data:

[0023] in They represent the mean value of the index P under the normal state of the system and the index P value under the pending state respectively; under the normal state, after the index P value is calculated, the value of each harmonic phase is , whose mean is , from which the PKL under normal conditions is calculated.

[0024] If the PKL calculated by the phase of each harmonic of the system is normal under normal conditions, then the threshold PKLH setting is the maximum value of the PKL calculated by using each harmonic under different constants c:

[0025] At this point, the processing of the normal state data is completed, and the system normal state threshold is PKLH.

[0026] Further, S4 is specifically: The phase of the vibration signal under the condition to be determined is extracted. On the basis of the normal state phase processing, combined with the phase mean under the normal state, the harmonic phase is processed in stages, including the following three stages of processing: Stage (1): If the absolute difference between the harmonic phase and its mean exceeds , it is determined to be an oscillation in a normal phase state.

[0027] Stage (2): If the absolute difference between the harmonic phase and its mean exceeds 180°, the phase is adjusted to minimize the absolute difference between it and the mean: Stage (3): Further setting of upper limits on change , which specifies the maximum range of phase variation to prevent processing steps from affecting each other.

[0028] Furthermore, in S4, at stage (1), the absolute difference between the harmonic phase and its mean exceeds ,Right now , it is determined to be an oscillation in a normal phase state, and the following preprocessing is performed:

[0029] in, for The mean of Indicates the harmonic phase of the nth order and kth segment of data. The order indicates the multiple of the harmonic compared to the rotation frequency.

[0030] Furthermore, in S4, in stage (2), if the absolute difference between the harmonic phase and its mean exceeds 180°, that is, , then adjust the phase to minimize its absolute difference with the mean:

[0031] in They represent the order and the time sequence number of the data segment respectively.

[0032] Furthermore, in S4, stage (3) , further set the upper limit of change , which specifies the maximum range of phase variation to prevent processing steps from affecting each other.

[0033]

[0034] So far, the preprocessing of the harmonic phase in the vibration data under the predetermined state is completed.

[0035] Further, S5 is specifically: First, calculate the value of the indicator P in the pending state ; Based on the mean value of the indicator P under normal conditions , calculate the characteristic index PKLA:

[0036] On the basis of PKLA, its maximum value PKLM under different constants c is further adopted as the final fault judgment basis:

[0037] PKLM and its difference from the normal state threshold PKLH are used to determine the system state.

[0038] Beneficial effects: 1: The present invention provides a quantitative diagnosis method for rubbing faults based on harmonic phase. The harmonic phase becomes an effective indicator input parameter through preprocessing: Since the phase has a variation range of [0,360°) and a base value, it is difficult to distinguish between normal and faulty states (the advantage of the amplitude is that the variation is more significant. In the spectrum, the unexcited parts are all 0, and the changes can be significantly observed). The present invention effectively extracts the phase, and through staged preprocessing, based on the unified mean and variation trend of the phase, lays the foundation for constructing indicators based on phase changes to determine the system state. The feasibility of the practical application of the present invention is enhanced: the present invention can be used in real time in actual equipment status monitoring, and there is no need for multiple starts and stops. Although normal status data is required as a basis, it conforms to the current data reserve status (few fault cases and a large amount of normal data).

[0039] 2: The present invention sets the threshold for determining the system state and the fault state. Most previous technologies only set sensitive indicators that change significantly when a fault occurs, but do not specify when the change is large enough for the system to enter a fault state. This method sets the threshold for the normal state and can quantitatively diagnose the degree of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for quantitatively diagnosing rubbing faults based on harmonic phase provided by the present invention; Figure 2 This is a diagram showing the effect of phase processing in a normal state in an embodiment of the present invention; Figure 3 This is a diagram showing the effect of rubbing fault diagnosis application in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0042] The present invention provides a quantitative diagnosis method for rubbing fault based on harmonic phase. The principle of this method is that the influence of rubbing fault on the system can be converted into the influence on the inherent characteristics of the system (stiffness, damping, etc.), combined with the vibration hysteresis angle The expression is: (1) in, They represent system mass, system stiffness, additional stiffness caused by rubbing faults, system damping, additional damping caused by rubbing faults, and harmonic frequency. This is the harmonic phase.

[0043] It can be seen from formula (1) that rubbing faults will cause the vibration lag angle of the system to change, while typical rotor faults will not affect the lag angle of the system. Therefore, based on the lag angle of the harmonics, a fault characteristic index can be constructed to diagnose rubbing faults in a targeted manner.

[0044] The present invention provides a rubbing fault diagnosis method based on harmonic phase, which can form a normal state threshold based on a large amount of existing normal state data, and can perform quantitative diagnosis on rubbing faults without the need for fault state data. The acquisition process is as follows: Figure 1 As shown. This method provides technical guidance for the healthy operation of mechanical equipment. Compared with the existing friction fault diagnosis method, this method can be directly used in the actual rotation equipment friction fault diagnosis without repeated test runs, has strong practicality and a wider range of applications.

[0045] The main steps in obtaining and using diagnostic methods are as follows: S1. Extracting the harmonic phase in the vibration data under normal conditions; Furthermore, in S1, certain preprocessing is required for the harmonic phase under normal conditions, mainly to exclude two factors: (1) Setting the phase angle range to [0,360°). Since the phase measurement in actual conditions fluctuates and is affected by the initial phase angle, there is a problem that the harmonic phase fluctuating around 0° will oscillate significantly. To prevent the influence of such factors, an angle threshold is set. , if the harmonic phase of the latter data changes by more than or equal to the previous data , it is considered that this is a normal phase fluctuation. The data segment is based on the set number of rotations of the shaft as one end of the data, and the set number of rotations is at least fifty; (2) Not all harmonic phases are generated by stable excitation forces. Some harmonic phases are also generated by noise excitation. It is necessary to discard these harmonic phases and set the angle threshold , if the maximum change in the phase of the nth-order harmonic If the threshold is exceeded, the phase is discarded.

[0046] Regarding (1), , , Indicates the nth order harmonic phase of the kth and k+1th segment data. The order indicates the multiple of the harmonic compared to the rotation frequency. Perform the following preprocessing: (2) Regarding (2), , the maximum change in the phase of the nth-order harmonic , perform the following preprocessing: (3) is the harmonic phase of the nth order; Finally, in order to facilitate the subsequent state threshold setting, the phase mean of all harmonics is adjusted to 180°, and the harmonic phase is subtracted from the mean of each harmonic and then added with 180° to obtain the extraction result.

[0047] The effect of this pre-processing method, such as Figure 2 shown.

[0048] S2. Based on the harmonic phase extracted from the normal state, the optimal harmonic number selection criterion is constructed to select the harmonic number that can take into account both the sensitivity and robustness of the indicator; Furthermore, in S2, because the fusion of multiple harmonic phases to construct the index cannot infinitely fuse all harmonic phases. On the one hand, not all phases can feedback faults well; on the other hand, extracting a large number of phases and fusing the calculation index will consume a lot of time, thus affecting the real-time performance of the method.

[0049] Assuming that the system is stably excited, the phase of the harmonics is stable under normal conditions, and the fluctuation is due to the influence of noise.

[0050] Through the vibration data under normal conditions, the effect of noise is fully considered, the harmonic phase is extracted, and the variance of the harmonic phase is calculated based on the , N is the upper limit of the harmonic number, and the optimal harmonic number selection criterion S is constructed, and its expression is: (4) in They represent the variance and mean respectively. When S is the largest, the value of N is the optimal harmonic number, that is, the harmonic number is considered to be the optimal harmonic number.

[0051] According to the expression, the criterion S is constructed by the variance and mean of the harmonic phase variance. The construction of this expression mainly considers the following two aspects: (1) The indicator cannot be interfered by too much noise. Therefore, the harmonic phase variance shows the impact level of noise, and its mean Smaller means that the indicator is less affected by noise; (2) The indicator needs to have a certain sensitivity to faults. Taking noise as the determining factor, harmonics can sensitively reflect the impact of noise, and can also sensitively reflect faults. Therefore, the criterion S needs to include some sensitive harmonic phases, which will lead to Larger.

[0052] In summary, the present invention constructs an optimal harmonic number selection criterion S and based on this, selects the harmonic phase used by the indicator.

[0053] S3. Based on the optimal harmonic number, calculate the fault characteristic index of the fused multi-harmonic phase, and based on the index, perform multi-component fusion to form the normal state threshold of the system; Furthermore, in S3, it is necessary to construct fault characteristic indicators and set thresholds. First, construct indicator P: (5) in They represent the harmonic order, exponential constant (commonly used) and the optimal harmonic number selected respectively.

[0054] At the same time, given the expression of KL divergence, the indicator PKL is constructed by amplifying the fault difference through normal state data: (6) in They represent the mean value of the index P under the normal state of the system and the index P value under the pending state. Under the normal state, after the index P value is calculated, the value of each harmonic phase is , whose mean is , so that the PKL under normal conditions can be calculated.

[0055] Under normal conditions, the PKL calculated by the phases of each harmonic of the system is normal, so the threshold PKLH setting is the maximum value of the PKL calculated by using each harmonic under different constants c: (7) In summary, S1, S2, and S3 complete the processing of normal state data. The normal state threshold of the system is PKLH.

[0056] S4. Combining the normal state phase processing method and the optimal harmonic number selection result, extract the harmonic phase in the vibration data under the pending state and perform preprocessing.

[0057] Further, in S4, the phase of the vibration signal under the predetermined state is extracted. Based on the normal state phase processing, combined with the phase mean under the normal state, the harmonic phase is processed in stages, wherein the abandonment of the noise excitation phase under the normal state is used in the present invention: (1) If the absolute difference between the harmonic phase and its mean exceeds , it is determined to be an oscillation in a normal phase state, and the processing method is as follows (8) in, for The mean of Indicates the harmonic phase of the nth order and kth segment of data. The order indicates the multiple of the harmonic compared to the rotation frequency.

[0058] (2) If the absolute difference between the harmonic phase and its mean exceeds 180°, the present invention adjusts the phase to minimize the absolute difference between the harmonic phase and the mean: (9) in They represent the order and the time sequence number of the data segment respectively.

[0059] (3) Step (2) ensures that the difference between all phases and harmonic mean values ​​is no greater than 180°. The present invention further sets an upper limit on the change , which specifies the maximum range of phase variation to prevent processing steps from affecting each other.

[0060] (10) In summary, the technology of the present invention completes the preprocessing of the harmonic phase under the predetermined state.

[0061] S5. Based on the indicator results of the normal state, calculate the fault diagnosis index, and compare the indicator value with the normal state threshold to determine the state of the system.

[0062] Furthermore, in S5, the value of the index P in the pending state is first calculated based on the fault characteristic index formula (5): .

[0063] Based on the mean value of the indicator P under normal conditions , the present invention calculates the characteristic index PKLA: (11) On the basis of PKLA, its maximum value PKLM under different constants c is further adopted as the final fault judgment basis: (12) It is important to note PKLM and its difference from the normal state threshold PKLH. PKLH is the sum of the maximum values ​​of each PKL under different constants c, while PKLM is the maximum value of the sum of PKL under the same constant c. Therefore, PKLH must be greater than or equal to the normal PKLM in the pending state. When PKLM exceeds PKLH, it indicates that the system has entered an abnormal state.

[0064] The effect of this method in practical application is shown in the figure Figure 3As shown: the horizontal line in the figure is the normal state threshold, and the state below the red line is normal. It can be seen that the technology of the present invention not only quantitatively identifies the development of the fault, but also determines the stage when the system returns to the normal state after the friction fault ends.

[0065] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A quantitative diagnosis method for rubbing fault based on harmonic phase, characterized in that: The steps include: S1. Extracting the harmonic phase from the vibration data under normal conditions; S2. Based on the harmonic phase extracted from the normal state, the optimal harmonic number selection criterion is constructed to select the optimal harmonic number that can take into account both the sensitivity and robustness of the indicator; S3. Based on the optimal harmonic number, calculate the fault characteristic index of the fused multi-harmonic phase, and based on the fault characteristic index, perform multi-component fusion to form a system normal state threshold; S4, combining the normal state phase processing method and the optimal harmonic number selection result, extracting the harmonic phase in the vibration data under the pending state and performing preprocessing; S5. Based on the indicator results of the normal state, calculate the fault diagnosis indicator, and compare the fault diagnosis indicator value with the normal state threshold to determine the state of the system.

2. A method for quantitative diagnosis of rubbing faults based on harmonic phase according to claim 1, characterized in that: In S1, the harmonic phase in the vibration data under normal conditions is extracted, and the specific steps are as follows: First, preprocess the harmonic phase under normal conditions: when hour, Perform the following preprocessing: in, Indicates the harmonic phase of the nth order and kth segment of data; Indicates the harmonic phase of the nth order, k+1th segment of data. The order indicates the multiple of the harmonic compared to the rotation frequency; when When , the following preprocessing is performed: in is the maximum change in the phase of the nth order harmonic, is the harmonic phase of the nth order; Finally, the harmonic phase is subtracted from the mean value of each harmonic and then added with 180° to obtain the extracted result of the harmonic phase.

3. A method for quantitatively diagnosing rubbing faults based on harmonic phase according to claim 1 or 2, characterized in that: The S2 is specifically: The system is stably excited. Under normal conditions, the phase of the harmonics is stable. If the system fluctuates under normal conditions, it is due to the influence of noise. Through the vibration data under normal conditions, the effect of noise is fully considered, the harmonic phase is extracted, and the variance of the harmonic phase is calculated based on the , N is the upper limit of the harmonic number, and the optimal harmonic number selection criterion S is constructed, and its expression is: in represent variance and mean respectively; when S is the largest, the value of N is the optimal harmonic number, that is, this harmonic number is considered to be the optimal harmonic number.

4. A method for quantitative diagnosis of rubbing fault based on harmonic phase as claimed in claim 3, characterized in that: In S3, based on the optimal harmonic number, the fault characteristic index of the fused multi-harmonic phase is calculated. Specifically, the fault characteristic index of the fused multi-harmonic phase is P: in They represent the harmonic order, exponential constant and the optimal harmonic number selected respectively.

5. A method for quantitative diagnosis of rubbing fault based on harmonic phase as claimed in claim 4, characterized in that: In S3, based on the fault characteristic index, multi-component fusion is performed to form a system normal state threshold, which is specifically: Given the expression of KL divergence, the indicator PKL is constructed by amplifying the fault difference through normal state data: in They represent the mean value of the index P under the normal state of the system and the index P value under the pending state respectively; under the normal state, after the index P value is calculated, the value of each harmonic phase is , whose mean is , from which the PKL under normal conditions is calculated; If the PKL calculated by the phase of each harmonic of the system is normal under normal conditions, then the threshold PKLH setting is the maximum value of the PKL calculated by using each harmonic under different constants c: At this point, the processing of the normal state data is completed, and the system normal state threshold is PKLH.

6. A method for quantitative diagnosis of rubbing fault based on harmonic phase according to claim 1, characterized in that: The S4 is specifically: The phase of the vibration signal under the condition to be determined is extracted. On the basis of the normal state phase processing, combined with the phase mean under the normal state, the harmonic phase is processed in stages, including the following three stages of processing: Stage (1): If the absolute difference between the harmonic phase and its mean exceeds , it is determined to be an oscillation in a normal phase state; Stage (2): If the absolute difference between the harmonic phase and its mean exceeds 180°, the phase is adjusted to minimize the absolute difference between it and the mean: Stage (3): Further setting of upper limits on change , which specifies the maximum range of phase variation to prevent processing steps from affecting each other.

7. A method for quantitative diagnosis of rubbing fault based on harmonic phase as claimed in claim 6, characterized in that: In S4, in stage (1), the absolute difference between the harmonic phase and its mean exceeds ,Right now , it is determined to be an oscillation in a normal phase state, and the following preprocessing is performed: in, for The mean of Indicates the harmonic phase of the nth order and kth segment of data. The order indicates the multiple of the harmonic compared to the rotation frequency.

8. A method for quantitative diagnosis of rubbing faults based on harmonic phase according to claim 6, characterized in that: In S4, in stage (2), if the absolute difference between the harmonic phase and its mean value exceeds 180°, that is, , then adjust the phase to minimize its absolute difference with the mean: in They represent the order and the time sequence number of the data segment respectively.

9. A method for quantitative diagnosis of rubbing fault based on harmonic phase according to claim 6, characterized in that: In S4, the stage (3) , further set the upper limit of change , which specifies the maximum range of phase variation to prevent the processing steps from affecting each other; So far, the preprocessing of the harmonic phase in the vibration data under the predetermined state is completed.

10. The method for quantitative diagnosis of rubbing fault based on harmonic phase according to claim 1, characterized in that: The S5 is specifically: First, calculate the value of the indicator P in the pending state ; Based on the mean value of the indicator P under normal conditions , calculate the characteristic index PKLA: On the basis of PKLA, its maximum value PKLM under different constants c is further adopted as the final fault judgment basis: PKLM and its difference from the normal state threshold PKLH are used to determine the system state.

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