A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods
Abstract
:1. Introduction
2. Basic Principles of Modified Ensemble Empirical Mode Decomposition (MEEMD) and AutoRegressive (AR) Spectrum Method
2.1. Basic Principles of MEEMD Method
2.2. Permutation Entropy
2.3. AR Spectrum Method
3. Wavelet Kernel Extreme Learning Machine
3.1. Extreme Learning Machine Theory
3.2. Theory of Kernel Extreme Learning Machine
3.3. Wavelet Kernel Extreme Learning Machine Theory
- (1)
- The number of input neurons and output neurons are determined, according to the number of features and categories, and the WKELM classifier model is established;
- (2)
- Set the value of the penalty factor C and the parameter of the wavelet kernel a;
- (3)
- The input characteristics of the training samples and their corresponding output categories are used to train the WKELM;
- (4)
- The features of the test sample are used as the input for the WKELM and, then, the trained WKELM classifier is used for the category decision.
4. Fault Feature Extraction Method of Axial Piston Pump Based on MEEMD and AR Spectrum
4.1. Test System and Data Acquisition
4.2. Simulation sSignal Analysis of MEEMD Method
4.3. Fault Feature Extraction Process
- (1)
- The signal is decomposed by the MEEMD method, from which several IMF components are obtained.
- (2)
- As there may still be pseudo-components in the IMF components obtained through MEEMD decomposition, it is necessary to eliminate such components. In this paper, the pseudo-components are eliminated by the Pearson correlation coefficient method. First, the Pearson correlation coefficient between all IMF components and the original signal is calculated. Then, IMF components with large correlation coefficients are taken as the effective components. The Pearson correlation coefficient is calculated as follows:
- (3)
- AR spectrum estimation of the t IMF components is carried out and the AR spectrum energies of the t IMF components are calculated. After normalization, the characteristic matrix, , of the signal is composed as follows:
4.4. Analysis of Acquired Hydraulic Pump Signals
4.5. Fault Feature Extraction of Hydraulic Pump Based on MEEMD-AR Spectrum
5. Fault Diagnosis Method of Hydraulic Pump Based on WKELM
5.1. Hydraulic Pump Fault Diagnosis Based on WKELM
5.2. Comparison and Analysis of Different Hydraulic Pump Fault Diagnosis Methods
6. Conclusions
- (1)
- Compared with the EEMD method, the MEEMD method can better suppress the phenomenon of mode mixing in hydraulic pump vibration signal decomposition. At the same time, the MEEMD method has better orthogonality and fewer pseudo-components;
- (2)
- The hydraulic pump fault feature extraction method based on the MEEMD and AR spectrum methods proposed in this paper can effectively solve the problem of low efficiency inherent to traditional feature extraction methods. This method can effectively extract the fault features from the vibration signals of a hydraulic pump;
- (3)
- The WKELM method was improved and introduced for the fault diagnosis of a hydraulic pump, and a full hydraulic pump fault diagnosis method was proposed based on MEEMD–AR–WKELM integration. The diagnosis accuracy of different hydraulic pump fault states using this method was 100%. Compared with the BP, SVM, and ELM methods, the fault diagnosis method proposed in this paper was shown to have higher fault identification accuracy and faster identification speed.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Num. | Name | Model | Performance Parameters |
---|---|---|---|
1 | Motor | Y132M-4 | Rated speed: 1480 rpm |
2 | Axial piston pump | MCY14-1B | Theoretical displacement: 10 mL/r rated pressure: 31.5 MPa; 7 pistons rated speed: 1500 rpm |
3 | Data acquisition card | USB-6221 | Maximum sampling rate: 250 kS/s |
4 | Vibration sensor | YD-72D | Frequency range: 0.3 Hz–18 kHz Charge sensitivity: 0.35 pC/(m/s2) Linear range of amplitude: 1000 m/s2 |
5 | Charge amplifier | DHF-10 | Maximum output voltage: ±10 V Gain: 0.1m V/pC–1 V/pC; Precision: < 1.5% |
6 | Pressure Sensor | SYB-351 | Measuring range: 0–25 MPa Precision: 0.2%; Output range: 0–5 V |
7 | Rotational speed measurer | LT-XSMP | Measuring range: 6–45000 rpm |
Num. | States | Fault Setting Method |
---|---|---|
1 | Normal working | --- |
2 | Single slipper wear fault | Grind off a rounded corner of the slipper |
3 | Single slipper loosing fault | Replace the normal components with a slipper loosing fault components |
4 | Center spring worn fault | Grind off the center spring by 1.2 mm |
Method | IO | Number of IMF Components |
---|---|---|
EEMD | 0.2069 | 9 |
MEEMD | 0.1131 | 6 |
State | IMF1 | IMF2 | IMF3 | IMF4 |
---|---|---|---|---|
Normal working | 0.2102 | 0.0712 | 0.0374 | 0.0210 |
Single slipper wear fault | 0.1156 | 0.0316 | 0.0151 | 0.0106 |
Single slipper loosing fault | 4.7201 | 1.6651 | 1.3350 | 0.9085 |
Center spring worn fault | 8.9050 | 3.5852 | 2.8181 | 1.6852 |
Condition of Hydraulic Pump | Normal Working | Single Slipper Wear Fault | Single Slipper Loosing Fault | Center Spring Worn Fault | Total | |
---|---|---|---|---|---|---|
MEEMD–BP | Number of samples correctly diagnosed | 20 | 20 | 20 | 19 | 79 |
Recognition accuracy | 100% | 100% | 100% | 95% | 98.75% | |
MEEMD–SVM | Number of samples correctly diagnosed | 20 | 19 | 19 | 20 | 78 |
Recognition accuracy | 100% | 95% | 95% | 100% | 97.5% | |
MEEMD–ELM | Number of samples correctly diagnosed | 20 | 19 | 20 | 20 | 79 |
Recognition accuracy | 100% | 95% | 100% | 100% | 98.75% | |
MEEMD–WKELM | Number of samples correctly diagnosed | 20 | 20 | 20 | 20 | 80 |
Recognition accuracy | 100% | 100% | 100% | 100% | 100% |
Fault Diagnosis Method | MEEMD–WKELM | MEEMD–ELM | MEEMD–SVM | MEEMD–BP |
---|---|---|---|---|
Training time (s) | 0.0020 | 0.0054 | 0.058 | 0.749 |
Testing time (s) | 0.0011 | 0.0019 | 0.0067 | 0.0042 |
Condition of Rolling Bearing | Normal Working | Inner Race Fault | Rolling Ball Fault | Outer Race Fault | Total |
---|---|---|---|---|---|
Number of samples correctly diagnosed | 20 | 20 | 20 | 20 | 80 |
Recognition accuracy | 100% | 100% | 100% | 100% | 100% |
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Li, Z.; Jiang, W.; Zhang, S.; Sun, Y.; Zhang, S. A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors 2021, 21, 2599. https://doi.org/10.3390/s21082599
Li Z, Jiang W, Zhang S, Sun Y, Zhang S. A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors. 2021; 21(8):2599. https://doi.org/10.3390/s21082599
Chicago/Turabian StyleLi, Zhenbao, Wanlu Jiang, Sheng Zhang, Yu Sun, and Shuqing Zhang. 2021. "A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods" Sensors 21, no. 8: 2599. https://doi.org/10.3390/s21082599
APA StyleLi, Z., Jiang, W., Zhang, S., Sun, Y., & Zhang, S. (2021). A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors, 21(8), 2599. https://doi.org/10.3390/s21082599