VP12-24: Motor Noise and Loss Reduction Dependence on Magnetostriction and Electromagnetic Power Variation Identify by AI Algorithm
Creators: Chang-Hung Hsu
Publisher: name: IEEE
Year: 2023

Sensors, High Frequency Devices, and Power Devices (Virtual)
The vibration measurement is mainly used natural frequency and resonance frequency to predict the magnetic device operation status [1,2]. This common feature is focused the electric motor device where is made by the electromagnetic silicon steel sheet. This study is used the magnetostrictive variation induced by the electromagnetic silicon steel sheet through the exciting power of the current and magnetic field which is to accelerate the vibration and noise variation. It is caused by the excitation of the core and coil. It can intercept the required characteristic signal, analyze the performance of dynamic magnetostrictive of ESS under DC bias and its complex dynamic anisotropy. The motors running under high-frequency conditions cause noise and vibration due to the interaction between magnetomotive forces (MMF). These MMFs are related to the fundamental wave current and switching harmonic current flowing into the motor. For experiment results and discussion, this article proposes the reference architecture of the IoT smart factory with neural network magnetic identification (NNMI). Regarding to the vibration frequency prediction as a multi-type classification problem, it uses the neural network features of deep learning to combine the input signal, as shown in Figure 1. That is classified into different categories [3, 4]. To use an optimized method with feedforward convolutional neural network, it can predict the frequency and analyze the AC and DC vibration frequency of the power motor and the non-fixed periodicity of the mechanical robot arm belong to different spectrum models. The sound pressure level is related to the sound intensity level (SIL). The conversion of voltage signal, current signal, and time domain and frequency domain FFT. As shown in Figure 2, to predict the vibration and sound module by using artificial intelligence method, it is verified the operating system through by measurement, numerical analysis and optimal calculation of NN method to identify the vibration and noise of the motor.References: [1] Eshaan Ghosh, Aida Mollaeian, Seog Kim, Jimi Tjong, Narayan C. Kar, DNN-Based Predictive Magnetic Flux Reference for Harmonic Compensation Control in Magnetically Unbalanced Induction Motor, IEEE Transactions on Magnetics, Volume: 53, Issue: 11, 2017. [2] S. Nandi, H.A. Toliyat, X. Li, Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review, IEEE Transactions on Energy Conversion, Volume: 20, Issue: 4, P. Paper, 1360, 2005. [3] Siliang Lu, Gang Qian;Qingbo He, Fang Liu, Yongbin Liu, Qunjing Wang, In Situ Motor Fault Diagnosis Using Enhanced Convolutional Neural Network in an Embedded System, IEEE Sensors Journal, Volume: 20, Issue: 15, 2020. [4] Jingsong Xie, Zhaoyang Li, Zitong Zhou, Scarlett Liu, A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features, IEEE Transactions on Instrumentation and Measurement, Volume: 70, 2021.