CN112364762B - Mechanical transmission fault detection method based on step error frequency spectrum characteristics - Google Patents
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- 238000001228 spectrum Methods 0.000 title claims abstract description 26
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- 238000012544 monitoring process Methods 0.000 claims abstract description 7
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- 238000010183 spectrum analysis Methods 0.000 claims abstract description 4
- 230000005540 biological transmission Effects 0.000 claims description 10
- 238000013145 classification model Methods 0.000 claims description 9
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- 238000010248 power generation Methods 0.000 description 2
- 208000032369 Primary transmission Diseases 0.000 description 1
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Abstract
The invention discloses a mechanical transmission fault detection method based on a step error frequency spectrum characteristic, which comprises a data acquisition step, a step error characteristic extraction step, a machine learning model training step and a new data model testing step; firstly, collecting time sequence signals of the operation of a part through a sensor at monitoring points such as a gear, a bearing and the like; then, carrying out transformation processing on the signals by a frequency spectrum analysis method, and extracting the step error frequency spectrum characteristics; and finally, training a machine learning model and testing a new data model through the extracted features. According to the invention, the fault signal can be effectively captured through the extracted step error characteristics, so that the performance and efficiency of the machine learning model are greatly improved, the deployment is easy to realize, and the applicability is strong.
Description
Technical Field
The invention relates to a mechanical transmission fault detection method based on a step error frequency spectrum characteristic, and belongs to the field of transmission mechanical fault detection.
Background
Wind power generation has received a great deal of attention from a worldwide perspective due to its advantages of cleanliness and reproducibility. However, fans often operate in harsh environments and are extremely labor intensive. On one hand, the damage from the fan blade, the tower to the power generation box, even the damage of the bearing and the gear can bring great safety threat and economic loss to the production process; on the other hand, the manual judgment of whether a fan is out of order by the fault diagnosis expert has high time and labor costs. These are all difficulties in fan fault detection and repair, and therefore, a way of detecting and analyzing fan faults using machine learning techniques has emerged.
Machine learning techniques require a large amount of data as training support, especially deep learning techniques. In addition, the noise tolerance of the model for data and marker information is low, and small changes in the data can lead to large changes in model predictions. However, on one hand, training data in the fan fault detection field is relatively less and has a lot of noise (such as improper sensor installation position); on the other hand, fan fault detection is a time sequence signal, the input dimension is high, the capacity requirement on the model is high, and the model is easy to be over-fitted. The prior machine learning technology needs to use the characteristics of expert design and assist in shallow model when processing mechanical transmission time sequence signals, and the method encounters performance bottleneck, so that an intelligent solution is needed.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides the mechanical transmission fault detection method based on the step error frequency spectrum characteristics, which can effectively capture fault signals through the extracted step error characteristics, greatly improves the performance and efficiency of a machine learning model, is easy to realize deployment and has strong applicability.
The technical scheme adopted by the invention is as follows:
a mechanical transmission fault detection method based on step error frequency spectrum characteristics comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting step error frequency spectrum characteristics: extracting step error spectrum characteristics through spectrum analysis;
training a machine learning model;
and step four, testing a new data model.
Preferably, the data collection in the first step includes the following steps:
step 100: determining a fault monitoring point of the transmission machinery;
step 101: deploying a signal collection sensor;
step 102: collecting a transmission mechanical rotating speed time sequence signal;
step 103: the collected data are formed into a plurality of groups of data in the forms of 'time sequence signal has faults' and 'time sequence signal has no faults'.
Further preferably, the step two step error spectrum feature extraction includes the steps of:
step 200: converting the original transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range;
step 201: traversing different window sizes W1, W2, …, WN in proportion;
step 202: for a given window size Wi, traversing each window in the spectrogram in turn to take the maximum value, and removing the phenomenon of 'burrs' beside;
step 203: taking the obtained index value of the peak;
step 204: calculating first-order, second-order and third-order differences of the index values, and sequencing all the difference values to obtain a peak index value difference curve C;
step 205: performing linear fitting on the peak index value difference curve C, and taking the Error value as a step Error characteristic, namely a Stage Error and a se characteristic;
step 206: repeating steps 202-205 for all window sizes W1, W2, …, WN, extracting step error features se1, se2, …, seN for each window;
step 207: and calculating the mean value, variance, maximum value and minimum value of all the step error features, and putting all the step error features together to serve as the step error features finally extracted.
Further preferably, the machine learning model training in the third step includes the steps of:
step 300: the step error characteristics extracted from each section of time sequence data are organized into a vector Vi;
step 301: the component corresponding to the time sequence data has no fault, and Yi=0 is set; otherwise, the component corresponding to the time sequence data has faults, and Yi=1 is set;
step 302: the classification model M is trained on the training data "Vi, yi".
Further preferably, the new data model test in the fourth step includes the steps of:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: the predicted value is less than or equal to 0.5, and no fault is output;
step 404: and if the predicted value is more than 0.5, outputting the fault.
The invention has the beneficial effects that:
the method can effectively remove the burrs, adopts a plurality of sliding windows to ensure the robustness of the model, can effectively capture fault signals through extracted step error characteristics, greatly improves the performance and efficiency of the machine learning model, is easy to realize deployment and has strong applicability.
Detailed Description
The present invention will be specifically described with reference to examples.
Example 1: the embodiment is a mechanical transmission fault detection method based on step error frequency spectrum characteristics, which comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting step error frequency spectrum characteristics: extracting step error spectrum characteristics through spectrum analysis;
training a machine learning model;
and step four, testing a new data model.
Wherein, the data collection in the first step comprises the following steps:
step 100: determining a fault monitoring point of the transmission machinery;
step 101: deploying a signal collection sensor;
step 102: collecting a transmission mechanical rotating speed time sequence signal;
step 103: the collected data are formed into a plurality of groups of data in the forms of 'time sequence signal has faults' and 'time sequence signal has no faults'.
Secondly, the step error spectrum characteristic extraction in the step two comprises the following steps:
step 200: converting the original transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range;
step 201: traversing different window sizes W1, W2, …, WN in proportion;
step 202: for a given window size Wi, traversing each window in the spectrogram in turn to take the maximum value, and removing the phenomenon of 'burrs' beside;
step 203: taking the obtained index value of the peak;
step 204: calculating first-order, second-order and third-order differences of the index values, and sequencing all the difference values to obtain a peak index value difference curve C;
step 205: performing linear fitting on the peak index value difference curve C, and taking the Error value as a step Error characteristic, namely a Stage Error and a se characteristic;
step 206: repeating steps 202-205 for all window sizes W1, W2, …, WN, extracting step error features se1, se2, …, seN for each window;
step 207: and calculating the mean value, variance, maximum value and minimum value of all the step error features, and putting all the step error features together to serve as the step error features finally extracted.
Then, the machine learning model training in the third step includes the steps of:
step 300: the step error characteristics extracted from each section of time sequence data are organized into a vector Vi;
step 301: the component corresponding to the time sequence data has no fault, and Yi=0 is set; otherwise, the component corresponding to the time sequence data has faults, and Yi=1 is set;
step 302: the classification model M is trained on the training data "Vi, yi".
Then, the new data model test in the fourth step includes the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: the predicted value is less than or equal to 0.5, and no fault is output;
step 404: and if the predicted value is more than 0.5, outputting the fault.
In practical application, the data collection steps are as follows: the bearing is selected as a main measuring point, a rotation speed sensor is deployed, the low-frequency vibration is measured in three directions of horizontal, vertical and axial (step 100, step 101), rotation speed time sequence signals corresponding to fans with faults and without faults are collected (step 102), each time sequence signal is collected for 4s-30s, data are organized into a time sequence signal, whether the time sequence signal has faults or not is stored, namely, the time sequence signal has faults and the time sequence signal has no faults are stored (step 103).
The step error spectrum characteristic extraction steps are as follows: converting the primary transmission mechanical rotation speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range (step 200); traversing different window sizes W1, W2, … and WN according to proportion (step 201), taking the maximum value for each window in a spectrogram for a given window size Wi, removing the phenomenon of 'burrs' beside the window in sequence (step 202) to obtain a peak index value (step 203), calculating first-order, second-order and third-order differential values of the index value, sequencing all differential values to obtain a peak index value differential curve C (step 204), performing linear fitting on the peak index value differential curve C, and taking the Error value as a step Error characteristic, namely Stage Error and se characteristic (step 205); steps 202-205 are repeated for all window sizes W1, W2, …, WN, step error features se1, se2, …, seN under each window are extracted (step 206), and then the mean, variance, maximum, minimum values of all the above step error features are calculated and all the step error features are put together as the final extracted step error feature (step 207).
The training steps of the machine learning model are as follows: step error characteristics extracted from each time series data are organized into a vector Vi (step 300), if a component corresponding to the time series data has no fault, yi=0 is set, otherwise Yi=1 (step 301), a classification model M is trained according to training data Vi and Yi (step 302), and in the embodiment, the classification model M adopts a support vector machine model; in practical application, the classification model M may also be a random forest model.
The new data model test steps are as follows: mechanical transmission time sequence data of a part to be predicted is collected (step 400), step error spectrum characteristics are extracted (step 401), the trained model M is utilized for prediction (step 402), no fault is output (step 403) if the predicted value is less than or equal to 0.5, and otherwise, the fault is output (step 404).
The foregoing is merely illustrative of the preferred embodiments of this invention, and it will be appreciated by those skilled in the art that variations and modifications may be made without departing from the principles of this invention, and such variations and modifications are to be regarded as being within the scope of this invention.
Claims (4)
1. A mechanical transmission fault detection method based on step error frequency spectrum features is characterized by comprising the following steps: the method comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting step error frequency spectrum characteristics: extracting step error spectrum characteristics through spectrum analysis;
training a machine learning model;
step four, testing a new data model;
the step-error spectrum feature extraction in the step two comprises the following steps:
step 200: converting the original transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range;
step 201: traversing different window sizes W1, W2, …, WN in proportion;
step 202: for a given window size Wi, traversing each window in the spectrogram in turn to take the maximum value, and removing the phenomenon of 'burrs' beside;
step 203: taking the obtained index value of the peak;
step 204: calculating first-order, second-order and third-order differences of the index values, and sequencing all the difference values to obtain a peak index value difference curve C;
step 205: performing linear fitting on the peak index value difference curve C, and taking the Error value as a step Error characteristic, namely a Stage Error and a se characteristic;
step 206: repeating steps 202-205 for all window sizes W1, W2, …, WN, extracting step error features se1, se2, …, seN for each window;
step 207: and calculating the mean value, variance, maximum value and minimum value of all the step error features, and putting all the step error features together to serve as the step error features finally extracted.
2. The method for detecting mechanical transmission failure based on step error spectrum features according to claim 1, wherein the step one data collection comprises the steps of:
step 100: determining a fault monitoring point of the transmission machinery;
step 101: deploying a signal collection sensor;
step 102: collecting a transmission mechanical rotating speed time sequence signal;
step 103: the collected data are formed into a plurality of groups of data in the forms of 'time sequence signal has faults' and 'time sequence signal has no faults'.
3. The method for detecting mechanical transmission failure based on step error spectrum features according to claim 1, wherein the training of the machine learning model in the step three comprises the following steps:
step 300: the step error characteristics extracted from each section of time sequence data are organized into a vector Vi;
step 301: the component corresponding to the time sequence data has no fault, and Yi=0 is set; otherwise, the component corresponding to the time sequence data has faults, and Yi=1 is set;
step 302: the classification model M is trained on the training data "Vi, yi".
4. A mechanical transmission failure detection method based on step error spectrum features according to claim 3, wherein the new data model test in the fourth step comprises the steps of:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: the predicted value is less than or equal to 0.5, and no fault is output;
step 404: and if the predicted value is more than 0.5, outputting the fault.
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