The motor bearing is the key component in the motor, and its performance and life directly affect the reliability and maintenance cost of the motor. Accurate prediction of the life of motor bearings helps to take measures in advance for maintenance or replacement and avoid downtime accidents caused by bearing damage. Based on the basic structure and working principle of motor bearings, this paper introduces the research status of life prediction and discusses the method and practice of life prediction.
The motor bearing is mainly used to support the rotor, reduce the friction between the rotor and the stator, and ensure the normal operation of the motor. Common motor bearings are rolling bearings and plain bearings. The rolling bearing is mainly composed of an inner ring, an outer ring, a rolling body and a cage, while the plain bearing is mainly composed of a bearing bush and a journal. When the motor is running, the bearing will be subjected to radial and axial loads, resulting in friction and wear.
The life prediction method of motor bearing mainly includes the prediction method based on theoretical model and the prediction method based on data drive.
Prediction method based on theoretical model: This method establishes mathematical model to predict bearing life by analyzing bearing failure mechanism. The common failure mechanisms include fatigue failure, wear failure and corrosion failure. The advantage of the theoretical model prediction method is that it can explain the bearing failure process from the mechanism, but it often requires a deep understanding of the bearing material and manufacturing process.
Data-driven prediction method: This method collects and analyzes a large number of bearing operation data, and establishes a statistical model or machine learning model to predict the bearing life. Common statistical models include Weibull distribution model, exponential distribution model, etc. Machine learning models include neural networks, support vector machines, etc. The advantage of data-driven forecasting method is that it can make full use of the existing operational data, but it has high requirements on the quality and quantity of data.
The following is a case of data-driven motor bearing life prediction:
A wind farm has a group of wind turbines of the same model, each equipped with the same bearing. In order to predict the life of these bearings, the staff collected the operating data of each generator bearing, including operating time, temperature, vibration and other parameters. By cleaning and preprocessing these data, the staff selected the appropriate characteristic parameters as the input to the model.
Next, the staff used support vector machine (SVM) as a prediction model to predict the life of the bearing. First, they use a portion of the data to train the model, and then validate and test it with another portion. In the process of model training, grid search and cross-validation methods were used to optimize the parameters of the model to improve the prediction accuracy of the model.
Finally, the staff compared the predicted results with the actual operating conditions, and found that the predicted results of the model were in good agreement with the actual life. Through this case, we can see the application value of data-driven life prediction method in practice.
This paper introduces the basic structure and working principle of motor bearings, as well as the research status and practical cases of life prediction. It can be seen from practice that the data-driven life prediction method has high application value and can accurately predict the life of motor bearings. However, this method has high requirements on the quality and quantity of data, so it is necessary to pay attention to the collection and processing of data in practical applications. In addition, the prediction method based on theoretical model also has its unique advantages, which can explain the bearing failure process from the mechanism. Future studies can further explore the combination and optimization of these two methods to improve the accuracy and reliability of motor bearing life prediction.
