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Journal Details
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

Search

Volume 21 (2021): Issue 3 (June 2021)

Journal Details
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

Search

3 Articles
Open Access

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

Published Online: 24 Jun 2021
Page range: 67 - 75

Abstract

Abstract

The rolling bearing and gear fault features are generally shown as modulation characteristics of their vibration signals. The empirical envelope (EE) method is an accordingly common demodulation method. However, the EE method has the defects of over- and undershoot, which may lead to demodulation error. According to this, an envelope optimization algorithm -- empirical optimal envelope (EOE) is introduced into the EE method, and an improved empirical envelope (IEE) method is obtained to calculate the instantaneous amplitude and instantaneous frequency of mono-component modulation signal. Furthermore, aiming at the actual measured mechanical vibration signal has multi-component modulation feature, the IEE method is combined with an adaptive signal decomposition method -- local oscillatory characteristic decomposition (LOD) proposed by the author, thereby a new multi-component signal demodulation method based on LOD and IEE is proposed. The proposed method is compared with Hilbert transform (HT) and Teager energy operator (TEO) demodulation methods by the simulation signal and actual measured mechanical vibration signal. The results show that the demodulation effects including edge effects, negative frequency, over- and undershoot of the proposed method are significantly improved and can extract the rolling bearing and gear fault feature information clearly.

Keywords

  • Multi-component modulation signal
  • local oscillatory-characteristic decomposition
  • improved empirical envelope
  • mechanical vibration signal
  • demodulation analysis
  • fault feature extraction
Open Access

3D Printed Pressure Sensor Based on Surface Acoustic Wave Resonator

Published Online: 24 Jun 2021
Page range: 76 - 81

Abstract

Abstract

This paper reports a 3-dimentional (3D) pressure sensor based on surface acoustic wave (SAW) resonators. The SAW resonators were designed and fabricated on 128°Y-X LiNbO3 substrate using the MEMS technology. The pressure sensing structure was 3D-printed using polyactic acid plastic, and two SAW resonators were integrated in the 3D-printed chamber structure for both temperature and pressure sensing. The SAW-based gas pressure sensors demonstrate a sensitivity of 589 ppm/MPa at the pressure range of 100-600 kPa and temperature of 40 °C.

Keywords

  • Surface acoustic wave
  • 3D printing
  • gas pressure sensor
  • LiNbO3
Open Access

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Published Online: 24 Jun 2021
Page range: 82 - 92

Abstract

Abstract

Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

Keywords

  • Multi-scale ternary dynamic analysis(MTDA)
  • NSGA-II and SVM
  • Hydraulic pump
  • Degradation state identification
3 Articles
Open Access

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

Published Online: 24 Jun 2021
Page range: 67 - 75

Abstract

Abstract

The rolling bearing and gear fault features are generally shown as modulation characteristics of their vibration signals. The empirical envelope (EE) method is an accordingly common demodulation method. However, the EE method has the defects of over- and undershoot, which may lead to demodulation error. According to this, an envelope optimization algorithm -- empirical optimal envelope (EOE) is introduced into the EE method, and an improved empirical envelope (IEE) method is obtained to calculate the instantaneous amplitude and instantaneous frequency of mono-component modulation signal. Furthermore, aiming at the actual measured mechanical vibration signal has multi-component modulation feature, the IEE method is combined with an adaptive signal decomposition method -- local oscillatory characteristic decomposition (LOD) proposed by the author, thereby a new multi-component signal demodulation method based on LOD and IEE is proposed. The proposed method is compared with Hilbert transform (HT) and Teager energy operator (TEO) demodulation methods by the simulation signal and actual measured mechanical vibration signal. The results show that the demodulation effects including edge effects, negative frequency, over- and undershoot of the proposed method are significantly improved and can extract the rolling bearing and gear fault feature information clearly.

Keywords

  • Multi-component modulation signal
  • local oscillatory-characteristic decomposition
  • improved empirical envelope
  • mechanical vibration signal
  • demodulation analysis
  • fault feature extraction
Open Access

3D Printed Pressure Sensor Based on Surface Acoustic Wave Resonator

Published Online: 24 Jun 2021
Page range: 76 - 81

Abstract

Abstract

This paper reports a 3-dimentional (3D) pressure sensor based on surface acoustic wave (SAW) resonators. The SAW resonators were designed and fabricated on 128°Y-X LiNbO3 substrate using the MEMS technology. The pressure sensing structure was 3D-printed using polyactic acid plastic, and two SAW resonators were integrated in the 3D-printed chamber structure for both temperature and pressure sensing. The SAW-based gas pressure sensors demonstrate a sensitivity of 589 ppm/MPa at the pressure range of 100-600 kPa and temperature of 40 °C.

Keywords

  • Surface acoustic wave
  • 3D printing
  • gas pressure sensor
  • LiNbO3
Open Access

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Published Online: 24 Jun 2021
Page range: 82 - 92

Abstract

Abstract

Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

Keywords

  • Multi-scale ternary dynamic analysis(MTDA)
  • NSGA-II and SVM
  • Hydraulic pump
  • Degradation state identification

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