For rolling mill machines, the operation status of bearing has a close relationship with process safety and production effectiveness. Therefore, reliable fault diagnosis and classification are indispensable. Traditional methods always characterize fault feature using a single fault vector, which may fail to reveal whole fault influences caused by complex process disturbances. Besides, it may also lead to poor fault classification accuracy. To solve the above-mentioned problems, a fault extraction method is put forward to extract multiple feature vectors and then a classification model is developed. First, to collect sufficient data, a data acquisition system based on wireless sensor network is constructed to replace the traditional wired system which may bring dangers during production. Second, the measured signal is filtered by a morphological average filtering algorithm to remove process noise and then the empirical mode decomposition method is applied to extract the intrinsic mode function (IMF) which contains the fault information. On the basis of the IMFs, a time domain index (energy) and a frequency index (singular values) are proposed through Hilbert envelope analysis. From the above analysis, the energy index and the singular value matrix are used for fault classification modeling based on the enhanced extreme learning machine (ELM), which is optimized by the bat algorithm to adjust the input weights and threshold of hidden layer node. In comparison with the fault classification methods based on SVM and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability.
As a key part of rolling mill, bearing operates in the environment of high temperature, high humidity, and heavy dust. Besides, bearing bears the largest impact force and load during production and it easily goes wrong under this circumstance. Thus, monitoring of bearing and timely classifying the faults into correct types are of great significance.
Recently, data-driven fault monitoring and classification methods have attracted more and more attention [
After collection of data, for fault classification of bearing, several crucial points should be discussed: (1) how to extract the fault features from the collected signal; (2) how to improve the classification accuracy of the fault identification model. Liu and Pan [
As for the fault classification, intelligent algorithms have been gradually applied to bearing, such as artificial neural network [
To solve the above-mentioned problems, a comprehensive and effective fault feature extraction and classification algorithm is proposed in this paper. First, a WSN is constructed in gearbox to collect the vibration signal. Second, to overcome the influence of disturbances, morphological average filtering algorithm is given to filter the collected signals and then the intrinsic mode function (IMF) is obtained through empirical mode decomposition (EMD) [ A data acquisition system based on wireless sensor network is constructed to replace the traditional wired system. A multiple fault features decomposition method is proposed to explain the fault influences using two indices with physical significance. A bat algorithm optimized ELM algorithm is proposed to determine the parameters to achieve better classification accuracy.
The remainder of this article is organized as follows. In Section
Rolling bearing has been widely used in industry, which is mainly composed of four parts: inner ring, outer ring, rolling body, and the holder. Figure
Physical structure of rolling bearing.
To collect data from rolling bearing, wireless sensor network (WSN) is constructed. WSN usually consists of a number of sensor nodes, cluster head nodes, and sink nodes. Besides, it forms a multihop ad hoc network system through the wireless communication, which can be used to receive, send, and process the information of monitoring objects within the covered area [
Figure
Network topology of wireless sensor network.
In order to effectively extract plenty of information under different status of rolling bearing, the signals collected by WSN are denoised by morphological averaged filter. After denosing, Figure
Flow chart of the extraction of fault feature vectors.
To remove the noise contained in the data collected from WSN system, mathematical morphology (MM) and average filtering algorithm [
Opening operator
The linear combination of (
In this way, positive and negative impulses of the signal are eliminated. Besides, it can smooth the signal and reduce the signal noise.
Due to harsh operation conditions of rolling bearing, its vibration signal always contains process disturbances, including the resonance and external noise. Therefore, after denoising using (
Note the denoised signal still as
In fact, different IMFs have different significances in comparison with the original signal
A large value of the coefficient means that the corresponding IMF is relevant to the original signal. In this way, it eliminates the interference component and obtains the intrinsic component mode component that contains the most information of the original signal.
The values of
After that, an energy eigenvector
The IMFs
Combined with (
Finally, the envelope spectrum of each IMF constructs a matrix
By processing each group of the signal under different status according to the above steps, we obtain the IMF Hilbert envelope spectrum singular value matrix and combine these singular value matrixes and energy features as multiple feature vectors to classify fault of rolling bearing. And multiple feature vectors are employed to train classification model of rolling bear based on bat algorithm (BA) optimized ELM, which will be given in the following section.
The accuracy of fault classification depends on the intelligent model used in the process of machine learning methods. In comparison with the BP method and the SVM method, ELM only needs to determine the number of nodes of hidden layer during the training of the network. Besides, it has the advantages of high efficiency, fast learning speed, and the unique solution. However, two structure parameters of ELM, that is, input weights and hidden layer threshold, are randomly given, which may result in poor accuracy. Having the advantages of dynamic control of global and local search conversion and avoiding falling into local optimum, BA is employed to optimize the two structure parameters of ELM. Thus, BA optimized ELM is proposed in the developed rolling bearing fault classification model to improve the precision and generalization ability.
In this part, the fault classification model is developed based on ELM. Figure
Flow chart of intelligent fault classification.
Assuming that the number of samples is
In (
Sequentially, parameters
And (
The goal of adjustment is to find a set of optimal parameters
The weights of input layer and thresholds of hidden layer might be zero, which may result in the functionless of some hidden layers. Thus, the number of hidden layer nodes has to be increased to achieve higher classification accuracy. However, it may lead to poor adaptability and low generalization capacity for testing data. To solve this problem, BA is employed to optimize the input weights and threshold of hidden layer of ELM. In this way, the classification accuracy and generalization ability will be improved. Figure
The flow chart of BA optimized ELM algorithm.
BA is a new heuristic algorithm proposed by Yang et al. [ Initialize the bat population location Update the bat pulse frequency, speed, and position according to ( where Generate uniformly distributed random number Generate uniformly distributed random number Sort the fitness value of all bats and find out the optimal solution. Repeat Steps (1)–(5) until a solution that meets the termination condition is found.
The application object of this article is a mill located in Baotou Iron and Steel Group, China. Figure
The gearbox of rolling mill.
Signal collected from WSN of (a) normal status, (b) rolling bearing fault, (c) inner ring fault, and (d) outer ring fault.
Morphological average filter is used to denoise the above signals. The linear structural element is selected, and each structural element value is 0, namely,
Waveforms after filtering (a) normal status, (b) rolling bearing fault, (c) inner ring fault, and (d) outer ring fault.
For each operation status, experiment was performed 30 times. Each experiment contains 2048 data points. Then, EMD is used to decompose the state sample under different status. According to the rule given in Section
The results of EMD for normal condition.
The correlation coefficient between the original signal and obtained IMF after decomposition of each state is evaluated. Table
Correlation coefficients between IMFs and the original signal in four cases.
IMF1 | IMF2 | IMF3 | IMF4 | |
---|---|---|---|---|
Normal | 0.4452 | 0.5507 | 0.5020 | 0.2117 |
0.0634 | 0.1692 | 0.3012 | 0.0501 | |
0.0419 | 0.1353 | 0.0801 | 0.0993 | |
| ||||
Fault 1 | 0.8769 | 0.4236 | 0.1818 | 0.0940 |
0.1529 | 0.0680 | 0.0355 | 0.0023 | |
0.0311 | 0.0598 | 0.0047 | 0.0110 | |
| ||||
Fault 2 | 0.9929 | 0.0867 | 0.0115 | 0.0114 |
0.0629 | 0.0045 | 0.0002 | 0.0042 | |
0.0446 | 0.0036 | 0.0015 | 0.0019 | |
| ||||
Fault 3 | 0.9529 | 0.1038 | 0.1520 | 0.1546 |
0.1569 | 0.0253 | 0.1300 | 0.0736 | |
0.0410 | 0.0206 | 0.0751 | 0.0352 |
Hilbert envelope demodulation spectrum for (a) normal status, (b) rolling bearing fault, (c) inner ring fault, and (d) outer ring fault.
Two indices, one from time domain and one from frequency domain, are calculated using the first four IMFs through the Hilbert envelope demodulation. Figures
Time domain index (energy) for four cases.
Frequency domain index (Hilbert envelope singular value) under four cases.
For the proposed fault classification model, initial values of parameter of BA optimized ELM are as follows: the population number is 20; the range of pulse frequency is from
Comparisons of SVM, ELM, and BA-ELM.
Algorithm | Accuracy (%) | |||
---|---|---|---|---|
Normal | Fault 1 | Fault 2 | Fault 3 | |
SVM | 90 | 100 | 100 | 45 |
ELM | 90 | 95 | 100 | 80 |
BA-ELM | 100 | 100 | 95 | 95 |
Classification of testing data based on BA-ELM.
Classification of testing data based on SVM.
Classification of testing data based on ELM.
To solve the problems of data acquisition and fault classification for rolling bearing, several crucial points are solved in this paper. First, a data acquisition system based on wireless sensor network is constructed to replace the traditional wired system to collect sufficient data. Because rolling bearing works under a complex environment, the collected vibration signal is always polluted by noise. To effectively remove noise, a morphological average filtering algorithm is proposed. Then the empirical mode decomposition method is performed on the filtered data to obtain multiple feature vectors, including a frequency domain index and a time domain index. Then, these two indices are used as inputs for fault modeling. Finally, the fault classification model is developed based on enhanced extreme learning machine, which is optimized by bat algorithm to adjust the input weights and threshold of hidden layer node. In comparison with fault classification methods based on support vector machine and traditional extreme learning machine, the experimental results show that the proposed method has higher classification accuracy and better generalization ability.
The authors declare that they have no conflicts of interest.
This research is supported by the National Natural Science Foundation of China (no. 51565047), Natural Science Fund of Inner Mongolia (no. 2017MS0509), Innovation Fund of Inner Mongolia University of Science and Technology (no. 2015QDL12), and Innovation Fund of Inner Mongolia Postgraduate (no. S20171012708).