Motor variable speed fault detection method based on current phase information

Abstract

The invention discloses a motor variable speed fault detection method based on current phase information, and relates to the technical field of motor variable speed. The motor variable speed fault detection method based on the current phase information comprises the following specific steps: step 1, estimating a current signal instantaneous angle through Hilbert transform; 2, dividing the signal into equal-angle (equal-motor rotation angle) segments of (-pi, pi) according to the instantaneous angle of the current signal, and eliminating the first and last incomplete intervals; step 3, resampling the segments through interpolation to enable the segments to have the same number of data points in the equal motor corner segments so as to complete angular domain resampling; 4, reconnecting all the intervals; step 5, extracting a fault order by using order analysis, and overcoming the defect that the rotating speed information is difficult to obtain by replacing the rotating speed signal with the phase characteristic in the current signal; operation steps are tedious and efficiency is low.

Description

Technical Field

[0001] The present invention relates to the technical field of motor speed variation, and in particular to a motor speed variation fault detection method based on current phase information. Background Art

[0002] At present, there are many invention patents in the field of variable speed fault detection of motors that focus on three main methods. The first type is an order tracking method based on the speed signal. This method obtains a steady-state signal in the angle domain by performing equal-angle resampling and interpolation resampling on the original signal, thereby realizing spectrum analysis. However, due to limitations of design and other factors, a tachometer cannot be installed in all cases, so it is difficult to obtain speed information. The second type is an order analysis method based on time-frequency ridge extraction. This method can be directly analyzed without relying on a tachometer, but the current ridge extraction process has problems such as cumbersome steps, low efficiency, and susceptibility to noise, resulting in low extraction accuracy. The third type is a method based on deep learning. This method has strong adaptive capabilities and is suitable for processing large-scale data, but at the same time has high requirements on the quality of input data, large computing resource requirements, and relatively high parameter selection and training time costs. Summary of the invention

[0003] The purpose of the present invention is to solve at least one of the technical problems existing in the prior art and to provide a motor variable speed fault detection method based on current phase information, which can solve the problems of the current ridge extraction process being complicated, inefficient and susceptible to noise resulting in low extraction accuracy.

[0004] To achieve the above object, the present invention provides the following technical solution: a motor variable speed fault detection method based on current phase information, comprising the following specific steps: step 1: estimating the instantaneous angle of the current signal by Hilbert transform;

[0005] Step 2: Divide the signal into equal-angle (equal motor angle) segments of (-Π, Π) according to the instantaneous angle of the current signal, and remove the first and last incomplete intervals;

[0006] Step 3: Resample these segments by interpolation so that they have the same number of data points in the motor angle segments, thereby completing the angular domain resampling;

[0007] Step 4: Reconnect each interval;

[0008] Step 5: Use order analysis to extract the fault order.

[0009] Preferably, the step three: resampling the segments using a resampling technique so that each segment with equal motor rotation angle has the same number of data points. This step helps to eliminate the problem of uneven distribution of data points caused by changes in motor speed.

[0010] Preferably, in step 4: after completing the resampling, reconnect each interval to restore the continuity of the signal.

[0011] Preferably, the step five: using order analysis technology to extract the fault order, identifying the specific frequency components related to the fault, that is, the fault order, through this step, the fault source can be accurately located, providing strong support for subsequent repair and maintenance.

[0012] Preferably, the signal of each interval is interpolated and resampled, and after reconnecting the intervals, the angular domain signal is converted into an order spectrum using Fourier transform.

[0013] Preferably, the motor stator current signal can usually be considered to be sinusoidal, and the motor speed is proportional to the current fundamental frequency. This method estimates the instantaneous angle of the current signal through Hilbert transform, and divides the signal into segments of equal angles (equal motor angles) according to the instantaneous angle of the current signal. These segments are resampled by interpolation so that the segments with equal motor angles have the same number of data points, thereby completing angular domain resampling, and then using the order envelope spectrum to perform motor fault detection.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. The motor variable speed fault detection method based on current phase information overcomes the problems of difficulty in obtaining speed information, cumbersome and inefficient operation steps, high sensitivity to noise, high requirements on input data quality and large consumption of computing resources, by utilizing the phase characteristics in the current signal to replace the speed signal. The present invention can realize accurate detection of motor faults under variable speed conditions with simple steps and small amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described below in conjunction with the accompanying drawings and embodiments:

[0017] Figure 1 It is a flow chart of the detection method of the present invention;

[0018] Figure 2 This is a vibration signal waveform diagram of the variable speed bearing outer ring under pitting state of the present invention;

[0019] Figure 3 This is a spectrum diagram of vibration signals of the variable speed bearing outer ring under pitting state of the present invention;

[0020] Figure 4 This is a current signal phase information diagram of the present invention;

[0021] Figure 5 This is the acceleration signal diagram of the present invention;

[0022] Figure 6 This is the order spectrum of the present invention. DETAILED DESCRIPTION

[0023] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it cannot be understood as a limitation on the scope of protection of the present invention.

[0024] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0025] In the description of the present invention, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0026] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0027] See also Figure 1 The present invention provides a technical solution: a motor variable speed fault detection method based on current phase information,

[0028] The motor stator current signal can usually be considered as sinusoidal, and the motor speed is proportional to the current fundamental frequency. This method estimates the instantaneous angle of the current signal through Hilbert transform, and divides the signal into equal-angle (equal motor angle) segments according to the instantaneous angle of the current signal. These segments are resampled by interpolation so that the equal motor angle segments have the same number of data points, thereby completing the angular domain resampling, and then using the order envelope spectrum to detect motor faults, including the following specific steps;

[0029] Step 1: First, estimate the instantaneous angle of the current signal through Hilbert transform;

[0030] Step 2: Divide the signal into equal-angle (equal motor rotation angle) segments of (-Π, Π) according to the instantaneous angle of the current signal, and remove the first and last incomplete intervals to ensure that each segment represents a fixed angle during the motor rotation process, so as to facilitate subsequent analysis;

[0031] Step 3: Resample these segments using resampling technology so that each segment with equal motor rotation angle has the same number of data points. This step helps to eliminate the problem of uneven distribution of data points caused by changes in motor speed, thereby improving the accuracy of the analysis;

[0032] Step 4: After resampling, reconnect the intervals to restore the continuity of the signal. This step is crucial to maintaining the overall structure and characteristics of the signal.

[0033] Step 5: Finally, use order analysis technology to extract the fault order and identify the specific frequency components related to the fault, namely the fault order. Through this step, the source of the fault can be accurately located, providing strong support for subsequent repairs and maintenance. The entire processing flow not only improves the accuracy of fault diagnosis, but also enhances the reliability and practicality of the analysis.

[0034] For further information, see Figure 2-3 ,The experiment collects the fault signal of the outer ring of the motor bearing, such as Figure 2 , Figure 3 As shown in the figure, there are many fault pulses in the time domain signal, and it contains many noise interference components caused by environmental factors and installation errors; there is a serious frequency aliasing phenomenon in the frequency domain signal, and the frequency band tends to be dispersed, which greatly affects the health monitoring of the motor;

[0035] For further information, see Figure 4-5 ,The proposed resampling method is applied to the experimental signal. First, the instantaneous phase of the current signal is accurately calculated using the Hilbert transform. The signal is divided into (-Π, Π) intervals according to the phase information, and the first and last incomplete intervals are eliminated. Figure 4 , Figure 5 is the acceleration signal after division, and the figure shows each interval obtained after division based on phase information;

[0036] For further information, see Figure 6 , interpolate and resample the signal in each interval, reconnect each interval and use Fourier transform to convert the angular domain signal into order spectrum. Figure 6 is the transformed order spectrum, Figure 6 It can be seen that the specific frequency component fr and its frequency multiples generated by the bearing outer ring fault can be clearly identified. This result verifies the effectiveness of the method and shows that the method has high accuracy and practicality in fault detection.

[0037] Furthermore, the present invention overcomes the problems of difficulty in obtaining speed information, complicated and inefficient operation steps, high sensitivity to noise, high requirements on input data quality and large consumption of computing resources, etc. by utilizing the phase characteristics in the current signal to replace the speed signal. The present invention can achieve accurate detection of motor faults under variable speed conditions with simple steps and small calculation amount.

[0038] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in the technical field without departing from the purpose of the present invention.

Claims

1. A motor variable speed fault detection method based on current phase information, characterized in that: The method comprises the following specific steps: Step 1: estimating the instantaneous angle of the current signal by Hilbert transform; Step 2: Divide the signal into equal-angle (equal motor angle) segments of (-Π, Π) according to the instantaneous angle of the current signal, and remove the first and last incomplete intervals; Step 3: Resample these segments by interpolation so that they have the same number of data points in the motor angle segments, thereby completing the angular domain resampling; Step 4: Reconnect each interval; Step 5: Use order analysis to extract the fault order.

2. The motor variable speed fault detection method based on current phase information according to claim 1 is characterized in that: The step three: resampling the segments using resampling technology so that each segment with equal motor rotation angle has the same number of data points. This step helps to eliminate the problem of uneven distribution of data points caused by changes in motor speed.

3. The motor variable speed fault detection method based on current phase information according to claim 1 is characterized in that: The fourth step: after completing the resampling, reconnect each interval to restore the continuity of the signal.

4. The motor variable speed fault detection method based on current phase information according to claim 1 is characterized in that: The step five: using order analysis technology to extract the fault order and identify the specific frequency components related to the fault, namely the fault order. Through this step, the fault source can be accurately located, providing strong support for subsequent repair and maintenance.

5. The motor variable speed fault detection method based on current phase information according to claim 1 is characterized in that: The signal in each interval is interpolated and resampled, and after reconnecting the intervals, the angular domain signal is converted into an order spectrum using Fourier transform.

6. The motor variable speed fault detection method based on current phase information according to claim 1 is characterized in that: The motor stator current signal can usually be considered to be sinusoidal, and the motor speed is proportional to the current fundamental frequency. This method estimates the instantaneous angle of the current signal through Hilbert transform, and divides the signal into equal-angle (equal motor angle) segments according to the instantaneous angle of the current signal. These segments are resampled through interpolation so that the equal motor angle segments have the same number of data points, thereby completing angular domain resampling, and then using the order envelope spectrum to detect motor faults.

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