MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array
2.1. Microphone Array
3.1. MVDR Algorithm Simulation
3.2. Microphone Array Formation Selection
4.1. LSTM Model Parameter Settings
4.2. Experimental Setup
4.3. Experimental Process
- Step 1—The microphone array was used to collect the idler fault audio signal.
- Step 2—MVDR processing was performed on the collected signal to generate idler fault distance data.
- Step 3—We processed the data again to build the dataset.
- Step 4—We divided the data into the training set (90%) and testing set (10%).
- Step 5—We trained and tested the model to draw conclusions.
4.4. Background Noise Interference Experiment
4.5. Impact Noise Interference Experiment
4.6. Evaluation Criteria and Comparison of Models
4.6.1. Data Comparison for Diagonal Double Rectangular Microphone Array
4.6.2. Data Comparison for Circular Microphone Array
- Due to the special structure of an idler, the human diagnosis steps are cumbersome and time is wasted. Based on deep learning, our model removed the tedious step of manually extracting idler fault features, which could greatly shorten the time required and improve the efficiency of idler fault diagnosis.
- Through simulation experiments, we found that the diagonal dual rectangular microphone array had higher resolution and noise resistance compared to the other microphone arrays and could be beneficial to the estimation of roller fault distance.
- After adjusting the model structure and parameters, we trained the generated idler fault distance samples. The accuracy of the proposed model was 100%, with better performance than the other models and results closer to the true values.
- Five fault locations during idler operation were analyzed, and the experimental results showed that the proposed model had the ability to estimate the fault distance and provide better robustness. It could be used as a standard for judging the fault distance of idlers and provide ideas for combining beamforming algorithms and deep learning with idler fault diagnosis.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Parameter Name||Parameter Value|
|Number of arrays||18|
|Desired signal angle||10|
|Interference signal angle||−30, 30|
|Number of stories||100|
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Zhang, X.; Wu, W.; Li, J.; Dong, F.; Wan, S. MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array. Sensors 2023, 23, 5094. https://doi.org/10.3390/s23115094
Zhang X, Wu W, Li J, Dong F, Wan S. MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array. Sensors. 2023; 23(11):5094. https://doi.org/10.3390/s23115094Chicago/Turabian Style
Zhang, Xiong, Wenbo Wu, Jialu Li, Fan Dong, and Shuting Wan. 2023. "MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array" Sensors 23, no. 11: 5094. https://doi.org/10.3390/s23115094