How do you validate and verify the accuracy and reliability of your vibration-based fault diagnosis methods?

2 Feature selection

The second step to validate and verify your vibration-based fault diagnosis methods is to select the most suitable features to represent the vibration signals. Features are the characteristics or attributes of the signals that can distinguish between different fault types and severities. There are many types of features that can be extracted from vibration signals, such as time-domain, frequency-domain, time-frequency-domain, and nonlinear features. However, not all features are equally relevant or informative for diagnosis purposes. Therefore, you should use feature selection techniques to identify and select the most relevant features that can improve the accuracy and reliability of your methods, and reduce the dimensionality and complexity of your data.

3 Model selection

The third step to validate and verify your vibration-based fault diagnosis methods is to select the most suitable model to perform the diagnosis. A model is a mathematical or logical representation of the relationship between the features and the fault types. There are many types of models that can be used for diagnosis, such as rule-based, statistical, neural network, fuzzy logic, and machine learning models. However, not all models are equally suitable or effective for diagnosis purposes. Therefore, you should use model selection techniques to compare and evaluate the performance and robustness of different models, and choose the best model that can achieve the highest accuracy and reliability of your methods.

4 Model validation

The fourth step to validate and verify your vibration-based fault diagnosis methods is to validate the selected model using independent data. Model validation is the process of testing how well the model can generalize and predict the fault types of new or unseen data. This is important to ensure that the model is not overfitting or underfitting the data, and that it can handle different operating conditions and scenarios. To validate the model, you should use cross-validation techniques, such as k-fold cross-validation, leave-one-out cross-validation, or bootstrap cross-validation, to split the data into training and testing sets, and measure the accuracy and reliability of the model using different metrics, such as confusion matrix, accuracy, precision, recall, F1-score, ROC curve, and AUC.

5 Model verification

The fifth step to validate and verify your vibration-based fault diagnosis methods is to verify the selected model using real-world data. Model verification is the process of testing how well the model can detect and identify the actual faults that occur in the machine in real time. This is important to ensure that the model is not only accurate and reliable in theory, but also in practice, and that it can cope with the uncertainties and variations of the real-world environment. To verify the model, you should use online monitoring techniques, such as continuous monitoring, periodic monitoring, or condition-based monitoring, to collect and analyze the real-time vibration data from the machine, and compare the model outputs with the actual fault conditions.

6 Model improvement

The sixth step to validate and verify your vibration-based fault diagnosis methods is to improve the selected model based on the feedback and results from the validation and verification processes. Model improvement is the process of modifying or updating the model to enhance its accuracy and reliability, and to adapt to the changing conditions and requirements of the machine. This is important to ensure that the model is not outdated or obsolete, and that it can maintain its performance and robustness over time. To improve the model, you should use model improvement techniques, such as parameter tuning, feature engineering, model ensemble, or transfer learning, to optimize or modify the model parameters, features, or structure, based on the data analysis and diagnosis results.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?