How to establish a model for fault diagnosis of asynchronous motors?

Regarding the establishment of a fault diagnosis model for asynchronous motors, you are considering using wavelet transform spectrum analysis method, which is a good idea.
Building a model requires the following five steps: 1. Firstly, you need to collect a large amount of data, including normal motor operation data and faulty motor operation data. These data should include information on various aspects such as current, voltage, speed, temperature, etc.
2. The collected raw data needs to undergo some preprocessing, such as filtering, denoising, normalization, etc., to ensure the accuracy and reliability of the data.
3. From wavelet transform spectrum analysis, you can extract some features that can reflect the operating status of the motor, such as frequency band distribution, energy distribution, frequency amplitude, etc. These features should be able to distinguish the operating status of normal motors from faulty motors.
4. Based on the extracted features, you can use some machine learning algorithms to build models, such as support vector machines (SVM), random forests, neural networks, etc. These algorithms can help you classify and predict the operating status of motors.
5. The established model may have some shortcomings that require some optimization. You can improve the accuracy and robustness of the model by adjusting its parameters, changing its structure, and other methods. In the process of building a model, you need to focus on the following four aspects:
Data quality: The quality of data directly affects the accuracy and reliability of the model, so it is necessary to collect high-quality data as much as possible.
Feature selection: The quality of feature selection directly affects the performance of the model, so it is necessary to select features that can effectively distinguish between normal motors and faulty motors.
Model selection: The selection of models also needs to be carefully considered, as different models are suitable for different data