In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

With the development of wind power generation, there will be more and more large-capacity large-scale units. Due to the geographical distribution of wind energy, wind turbines generally work in relatively harsh environments [

Wind turbines are composed of blades, gearboxes, generators, frequency conversion systems, primary control, etc. Among them, the most prone to faults and the most affected parts of the fault are mostly in critical parts such as gearboxes and generators. However, the gearbox is the part with the highest failure rate. Common failures of gearboxes generally occur in gears and rolling bearings, and the failure rate of rolling bearings is very high.

Even if the rolling bearing is not much different from the factory conditions, some bearings fail before reaching the theoretical service life, but some bearings have exceeded the theoretical service life but are still intact. If the rolling bearings are regularly repaired, this will result in the inability to use the rolling bearings better. Therefore, the detection, diagnosis, and analysis of the bearing state are beneficial to accurately understand the working condition of the rolling bearing and avoid unnecessary losses caused by unknown accidents.

Rolling bearing detection and diagnosis technology have experienced more than sixty years of development [

This paper takes the rolling bearings as the research object and analyzes the states of rolling bearings. Make full use of signal processing methods such as EMD to research feature extraction and failure analysis. After the signal is obtained for preprocessing, the kurtosis criterion is used to select Intrinsic Mode Functions (IMF), and then envelope spectrum analysis and feature extraction are performed to classify faults. Finally, a rolling bearing fault analysis method based on EMD K-Nearest Neighbor (EMD-KNN) is given.

The factors affecting the vibration of rolling bearings are internal and external. The vibration of rolling bearings can be divided into three categories: (1) Natural vibration; (2) Forced vibration caused by errors in the processing and assembly of parts of each part; (3) The outer and inner ring grooves or the surface of the ball have impact vibration caused by damages such as wear, scratches, pitting and spalling. Generally, the vibration of a rolling bearing is a superposition of the above three types of vibration, so its actual structure is very complicated [

Here, the SKF6205 deep groove ball bearing is utilized. Acceleration sensors measure the vibration signals of rolling bearings in different states. This paper uses a drive end bearing with a diameter of 0.17781 mm and a speed of 1797 r/min; the frequency of bearing rotation under this condition is 29.95 Hz; the sampling frequency is 12 kHz.

In normal working mode, the natural frequency of rolling bearing is only related to the material and structure of the bearing itself. So the natural frequency of each part will be calculated by the following formula [

The natural frequencies of the outer and the inner rings are:

The natural frequency of the rolling element is:

It should be noted that using

The rotation speed of the inner ring is:

The rotation speed of the outer ring is:

The rotation speed of the cage is:

From

The rotation frequency of the cage relative to the inner ring and the rotation frequency relative to the outer ring are, respectively:

When the inner ring of the rolling bearing fails, the calculation formula of the fault characteristic frequency is:

When the outer ring of the rolling bearing fails, the calculation formula of the fault characteristic frequency is:

When the rolling element of a rolling bearing fails, the calculation formula of the fault characteristic frequency is:

Failure analysis analyzes the failure mechanism, type and impact, frequency of occurrence, and development and change laws of the researched object. Only on the basis of fault analysis can the appropriate diagnosis method be determined to carry out effective fault diagnosis. Therefore, fault analysis and fault diagnosis are generally developed at the same time.

With the progress of human society and economic development, the equipment used by people has become more sophisticated and complex [

The specific diagnosis process has four steps: (1) Signal acquisition: According to the structure and operating characteristics of the equipment, use suitable sensors and suitable measuring point positions to measure the signal of the working state of the rolling bearing. Finally, display and store it; (2) Signal preprocessing: Because the signal collected by the sensor will have certain errors, such as noise and interference, it is not easy to directly obtain the fault characteristics. Therefore, the signal must be preprocessed first. The preprocessing method in this paper is mainly to perform re-sampling; (3) Signal feature extraction: Extract the fault features through signal analysis methods on the preprocessed signal. In this paper, the EMD method is used for signal feature extraction. This way can further judge the working conditions of rolling bearings and enhance the fault characteristics; (4) State detection and decision-making: make decision analysis by analyzing the signal after feature extraction.

Decomposing the data used in this article into EMD will get several IMF components and then use the kurtosis principle to select the appropriate IMF components. First, the original signal of bearing vibration is decomposed by EMD to obtain several IMFs [

EMD can decompose complex, nonlinear, and non-stationary signals to obtain multiple IMF through its characteristic time scale [

As a method of processing signals, Hilbert transform is to convolve a given signal with

After

From this find the instantaneous frequency:

After processing

The kurtosis

In this paper, the kurtosis criterion is used to screen IMF, and IMF components with

Analyze the bearing in a healthy state. The decomposed IMF components are used to calculate the kurtosis value using MATLAB software. According to the criterion that the kurtosis value is greater than 3, the 9 IMF components after decomposition are screened, so IMF2, IMF3, IMF6, IMF8 are selected. The screened IMF component diagram and envelope spectrum diagram are drawn. The IMF image is shown in

The envelope spectrum is shown in

In

Analyze the signal of the bearing inner ring failure. First, according to the criterion that the kurtosis value is greater than 3, the decomposed 10 IMF components are screened. Therefore, the selected components are IMF1, IMF2, IMF4, IMF5, IMF6, IMF8. Second, draw the filtered IMF component diagram and envelope spectrum diagram. The IMF image is shown in

The envelope spectrum is shown in

In

Analyze the original signal of the bearing rolling element failure. First, according to the criterion that the kurtosis value is greater than 3, the decomposed 10 IMF components are screened. Therefore, the selected components are IMF2, IMF3. Second, draw the filtered IMF component diagram and envelope spectrum diagram. The IMF image is shown in

The envelope spectrum is shown in

In

Analyze the original signal of the bearing outer ring failure. First, according to the criterion that the kurtosis value is greater than 3, the decomposed 10 IMF components are screened. Therefore, the selected components are IMF1∼IMF9. Second, draw the filtered IMF component diagram and envelope spectrum diagram. The IMF image is shown in

The envelope spectrum is shown in

In

The work of the previous chapter mainly focused on feature extraction, fault analysis, and diagnosis for a single signal. After EMD processing the data, this paper uses the envelope spectrum to analyze the failure of rolling bearings. Although the analysis results of the health status and the failure status of the outer ring are very obvious, the analysis results of the failure status of the rolling elements are disturbed. In the work of this chapter, the input signal is a complex mixed signal. The neural network classifier is added to find the frequency characteristics of each state from the complex signal. In this way, the correctness of the fault analysis and diagnosis can be ensured.

First, preprocess the original data and perform the same preliminary work as the previous chapter to obtain the IMF components selected by the kurtosis criterion. Second, superimpose the IMF components to calculate the time-frequency domain parameters of the superimposed signal. Third, normalize the computed data. Fourth, select four classifiers to train and classify the data. Finally, after comparison, a suitable classifier can be selected.

Since the previous work is similar to the previous work of Chapter 3, it will not be repeated here. The superimposed signal of the IMF component is obtained directly for calculation.

Peak difference (

Standard deviation (

Kurtosis value (

Average (

Mean square frequency (

Root mean square frequency (

Frequency variance (

Frequency standard deviation (

The calculated data is the superposition of the IMF components selected by the kurtosis criterion in Chapter 3. First, select one hundred thousand of the data points. Second, call the reshape function. Third, turn the hundred thousand data into a 5000 × 20 matrix. The matrix represents each state divided into 20 sets, and each set of data has 5000 data points. When repeating the above steps, four 5000 × 20 matrices of four states are obtained. According to

Since each feature parameter has its dimension, it is not convenient to directly join the neural network. Therefore, this article uses the method of data normalization to normalize the data. The data are normalized as shown in

Naive Bayes (NB) [

Bearing status | Peak difference | Standard deviation | Kurtosis | mean |

Health | 0.041158 | 0.062256 | 0.36854 | 0.095909 |

Outer ring failure | 0.972764 | 0.970917 | 0.980174 | 0.417068 |

Inner ring failure | 0.400276 | 0.403035 | 0.699864 | −0.64849 |

Rolling element failure | 0.027969 | 0.03545 | 0.490336 | 0.186732 |

K-Nearest Neighbor (KNN) [

Discriminant Analysis Classifier (DAC) [

This paper will divide the raw signal data of each of the four vibration states of rolling bearings into 20 groups with 5000 data in each group, a total of 80 groups of data. Among the 20 sets of data in each state, 15 sets are used to train the classifiers, a total of 60 sets; the remaining 5 sets of data in each state are tested to determine the fault diagnosis accuracy and effects of several classifiers.

Training and testing are implemented in MATLAB software. Use the tic toc function for timing and accuracy calculations. The four classifiers are operated separately. Finally, the classification accuracy of the KNN neural network is 100%, which takes 0.449198 s and the Random Forest classifier (RF) neural network takes 2.161731 s. The characteristic of the K-nearest neighbor classifier is to follow the data to judge completely, and there is no specific fixed mathematical model.

In this way, using the other three classifier operations, the final result is shown in

Neural networks | Accuracy (%) | Time (s) |

NB | 100 | 0.630496 |

KNN | 100 | 0.449198 |

RF | 100 | 2.161731 |

DAC | 100 | 0.574576 |

Since the test data is less and the characteristic parameters are more obvious, the accuracy of the four classifiers is 100%. However, it can be seen that the running time of each classifier is different. Among them, the longest time-consuming is RF, which takes 2.161731 s. The shortest time-consuming is KNN, which takes 0.449198 s.

On the premise of this sample of rolling bearing vibration signal data, the classification effect of the K nearest neighbor classifier is better than the other three classifiers. Therefore, the EMD-KNN method not only improves the accuracy of the typical fault diagnosis of rolling bearings but also only uses one operation to obtain the final result.

This paper takes the rolling bearing in the wind turbine generator as the research object, extracts the characteristics of its vibration signals in four different states, processes the signals based on EMD, and uses different methods for fault analysis. The research results of this paper are as follows: (1) The experimental results show that relative to directly observing the original signal, the signal characteristics after processing are more obvious, and it is easier to perform fault analysis and diagnosis. (2) Based on the EMD processed data, the time-frequency domain feature extraction is performed. Four neural network classifiers are added to obtain a more efficient and fast fault analysis method. It shows that fault analysis and diagnosis can be carried out more quickly and conveniently after joining the neural network.