CN112364762A - Mechanical transmission fault detection method based on step error frequency spectrum characteristics - Google Patents
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Abstract
本发明公开了一种基于阶梯误差频谱特征的机械传动故障检测方法，包括数据采集步骤、阶梯误差特征提取步骤、机器学习模型训练步骤和新数据模型测试步骤；首先通过在齿轮、轴承等监控点的传感器收集部件运转的时序信号；然后通过频谱分析方法对信号进行变换处理，并提取阶梯误差频谱特征；最后通过提取的特征进行机器学习模型训练和新数据模型测试。本发明通过提取的阶梯误差特征能有效地捕获故障信号，大幅度提升了机器学习模型的性能和效率，易实现部署且适用性强。The invention discloses a mechanical transmission fault detection method based on step error spectral features, including data acquisition steps, step error feature extraction steps, machine learning model training steps and new data model testing steps; The sensor collects the time series signal of the operation of the component; then the signal is transformed by the spectrum analysis method, and the step error spectrum feature is extracted; finally, the machine learning model is trained and the new data model is tested by the extracted features. The present invention can effectively capture the fault signal through the extracted step error feature, greatly improve the performance and efficiency of the machine learning model, and is easy to implement and deploy and has strong applicability.
Description
Technical Field
The invention relates to a mechanical transmission fault detection method based on step error frequency spectrum characteristics, and belongs to the field of transmission mechanical fault detection.
Background
Wind power generation has received a great deal of attention worldwide due to its clean and renewable advantages. However, wind turbines often work in harsh environments and are extremely labor intensive. On one hand, the damage of the fan blades, the tower frame, the power generation box, even the bearings and the gears can bring huge safety threat and economic loss to the production process; on the other hand, the manual judgment of whether a fan fails by a failure diagnosis expert has high time and labor costs. These are the difficulties of fan fault detection and maintenance, so a way of detecting and analyzing fan faults by using a machine learning technology appears.
Machine learning techniques require a large amount of data as training support, especially deep learning techniques. In addition, the noise tolerance of the model to the data and the label information is low, and when the data changes slightly, the model prediction changes greatly. On one hand, however, the training data in the field of fan fault detection is relatively small and has much noise (such as improper installation position of a sensor); on the other hand, the fan fault detection is a time sequence signal, the input dimensionality is high, the requirement on the capacity of the model is high, and overfitting of the model is easily caused. The prior machine learning technology needs to pass through characteristics designed by experts and assist shallow models when processing mechanical transmission time sequence signals, and the method meets the performance bottleneck, so an intelligent solution is needed.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the mechanical transmission fault detection method based on the step error frequency spectrum characteristics, the fault signal can be effectively captured through the extracted step error characteristics, the performance and the efficiency of the machine learning model are greatly improved, the deployment is easy to realize, and the applicability is strong.
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 the step error frequency spectrum characteristics: extracting step error frequency spectrum characteristics through frequency spectrum analysis;
step three, training a machine learning model;
and step four, testing a new data model.
Preferably, the data collection in the first step comprises the following steps:
step 100: determining a transmission mechanical fault monitoring point;
step 101: deploying a signal collection sensor;
step 102: collecting a time sequence signal of the rotating speed of the transmission machinery;
step 103: the collected data are combined into a plurality of groups of data in the forms of 'timing signal failure' and 'timing signal failure-free'.
Further preferably, the step two step error spectrum feature extraction includes the following steps:
step 200: converting the primary 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, sequentially traversing each window in the spectrogram to obtain a maximum value, and removing a side 'burr';
step 203: taking the index value of the obtained peak;
step 204: calculating first, second and third differences of the index values, and sequencing all the difference values to obtain a high-peak index value difference curve C;
step 205: performing linear fitting on the high-peak index value difference curve C, and taking an Error value of the curve C as a step Error characteristic, namely a Stage Error, se characteristic;
step 206: repeating the steps 202 to 205 for all window sizes W1, W2, … and WN, and extracting step error features se1, se2, … and seN under each window;
step 207: and calculating the mean value, the variance, the maximum value and the minimum value of all the step error characteristics, and putting all the step error characteristics together to serve as the finally extracted step error characteristics.
Further preferably, the training of the machine learning model in the third step comprises the following steps:
step 300: organizing the step error features extracted from each section of time sequence data into a vector Vi;
step 301: a component corresponding to the time series data has no fault, and Yi =0 is set; otherwise, if the component corresponding to the time series data is faulty, setting Yi = 1;
step 302: the classification model M is trained on the training data "Vi, Yi".
Further preferably, the new data model test in step four includes the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error frequency spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: if the predicted value is less than or equal to 0.5, outputting no fault;
step 404: if the predicted value is greater than 0.5, the output is faulty.
The invention has the beneficial effects that:
the method has the advantages that the burr phenomenon can be effectively removed, the robustness of the model is guaranteed by adopting the sliding windows, the fault signal can be effectively captured through the extracted step error characteristics, the performance and efficiency of the machine learning model are greatly improved, the deployment is easy to realize, and the applicability is strong.
Detailed Description
The present invention will be described in detail with reference to the following 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 the step error frequency spectrum characteristics: extracting step error frequency spectrum characteristics through frequency spectrum analysis;
step three, 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 transmission mechanical fault monitoring point;
step 101: deploying a signal collection sensor;
step 102: collecting a time sequence signal of the rotating speed of the transmission machinery;
step 103: the collected data are combined into a plurality of groups of data in the forms of 'timing signal failure' and 'timing signal failure-free'.
Secondly, the step-two step error frequency spectrum feature extraction comprises the following steps:
step 200: converting the primary 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, sequentially traversing each window in the spectrogram to obtain a maximum value, and removing a side 'burr';
step 203: taking the index value of the obtained peak;
step 204: calculating first, second and third differences of the index values, and sequencing all the difference values to obtain a high-peak index value difference curve C;
step 205: performing linear fitting on the high-peak index value difference curve C, and taking an Error value of the curve C as a step Error characteristic, namely a Stage Error, se characteristic;
step 206: repeating the steps 202 to 205 for all window sizes W1, W2, … and WN, and extracting step error features se1, se2, … and seN under each window;
step 207: and calculating the mean value, the variance, the maximum value and the minimum value of all the step error characteristics, and putting all the step error characteristics together to serve as the finally extracted step error characteristics.
Then, the training of the machine learning model in the third step comprises the following steps:
step 300: organizing the step error features extracted from each section of time sequence data into a vector Vi;
step 301: a component corresponding to the time series data has no fault, and Yi =0 is set; otherwise, if the component corresponding to the time series data is faulty, setting Yi = 1;
step 302: the classification model M is trained on the training data "Vi, Yi".
Then, the new data model test in the fourth step comprises the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error frequency spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: if the predicted value is less than or equal to 0.5, outputting no fault;
step 404: if the predicted value is greater than 0.5, the output is faulty.
In practical application, the data collection steps are as follows in sequence: selecting a bearing as a main measuring point to deploy a rotating speed sensor, measuring low-frequency vibration in the horizontal direction, the vertical direction and the axial direction (step 100, step 101), collecting rotating speed time sequence signals corresponding to fans with faults and without faults (step 102), collecting 4s-30s of each time sequence signal, and organizing data into a time sequence signal, judging whether the time sequence signal has faults or not, and storing the time sequence signal in a form of 'time sequence signal fault' and 'time sequence signal no fault' (step 103).
The step error frequency spectrum feature extraction steps are as follows in sequence: converting the primary 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 200); traversing different window sizes W1, W2, … and WN according to proportion (step 201), sequentially traversing each window in a spectrogram for a given window size Wi to take a maximum value, removing a side 'burr' phenomenon (step 202) to obtain a peak index value (step 203), calculating first-order, second-order and third-order difference values of the index values, sequencing all the difference values to obtain a peak index value difference curve C (step 204), performing linear fitting on the peak index value difference curve C, and taking an Error value of the peak index value difference curve C as a step Error feature, namely a Stage Error, a se feature (step 205); the steps 202 to 205 are repeated for all the window sizes W1, W2, … and WN, step 1, se2, … and seN under each window are extracted (step 206), and then the mean, variance, maximum and minimum values of all the above step error features are calculated and put together to be the final extracted step error feature (step 207).
The training steps of the machine learning model are as follows: organizing the step error features extracted from each time series data into a vector Vi (step 300), if the component corresponding to the time series data has no fault, setting Yi =0, otherwise Yi =1 (step 301), and training a classification model M according to training data "Vi, Yi" (step 302), wherein in the embodiment, the classification model M adopts a support vector machine model; in practical application, the classification model M may also adopt a random forest model.
The new data model test sequentially comprises the following steps: collecting mechanical transmission time sequence data of a component to be predicted (step 400), extracting step error frequency spectrum characteristics (step 401), predicting by using a trained model M (step 402), outputting no fault if the predicted value is less than or equal to 0.5 (step 403), and outputting fault if the predicted value is not more than 0.5 (step 404).
The above description is only a preferred embodiment of the present patent, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the inventive concept, and these modifications and decorations should also be regarded as the protection scope of the present patent.
Claims (5)
1. A mechanical transmission fault detection method based on step error frequency spectrum features is characterized in that: the method comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting the step error frequency spectrum characteristics: extracting step error frequency spectrum characteristics through frequency spectrum analysis;
step three, training a machine learning model;
and step four, testing a new data model.
2. The method for detecting the mechanical transmission fault based on the step error frequency spectrum characteristic as claimed in claim 1, wherein the data collection in the first step comprises the following steps:
step 100: determining a transmission mechanical fault monitoring point;
step 101: deploying a signal collection sensor;
step 102: collecting a time sequence signal of the rotating speed of the transmission machinery;
step 103: the collected data are combined into a plurality of groups of data in the forms of 'timing signal failure' and 'timing signal failure-free'.
3. The method for detecting the mechanical transmission fault based on the step error frequency spectrum characteristic as claimed in claim 2, wherein the step two step error frequency spectrum characteristic extraction comprises the following steps:
step 200: converting the primary 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, sequentially traversing each window in the spectrogram to obtain a maximum value, and removing a side 'burr';
step 203: taking the index value of the obtained peak;
step 204: calculating first, second and third differences of the index values, and sequencing all the difference values to obtain a high-peak index value difference curve C;
step 205: performing linear fitting on the high-peak index value difference curve C, and taking an Error value of the curve C as a step Error characteristic, namely a Stage Error, se characteristic;
step 206: repeating the steps 202 to 205 for all window sizes W1, W2, … and WN, and extracting step error features se1, se2, … and seE under each window;
step 207: and calculating the mean value, the variance, the maximum value and the minimum value of all the step error characteristics, and putting all the step error characteristics together to serve as the finally extracted step error characteristics.
4. The method for detecting the mechanical transmission fault based on the step error frequency spectrum characteristic as claimed in claim 3, wherein the step three machine learning model training comprises the following steps:
step 300: organizing the step error features extracted from each section of time sequence data into a vector Vi;
step 301: a component corresponding to the time series data has no fault, and Yi =0 is set; otherwise, if the component corresponding to the time series data is faulty, setting Yi = 1;
step 302: the classification model M is trained on the training data "Vi, Yi".
5. The method for detecting mechanical transmission faults based on step error spectrum characteristics as claimed in claim 4, wherein the new data model test in the step four comprises the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error frequency spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: if the predicted value is less than or equal to 0.5, outputting no fault;
step 404: if the predicted value is greater than 0.5, the output is faulty.
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