What is the most appropriate technique for performing Predictive Maintenance on bearings, considering that Naive-Bayes does not seem to yield satisfactory results?

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There are several other techniques that can be considered for performing predictive maintenance on bearings, such as:

1. Decision trees: This technique works by creating a model that represents possible decisions and their anticipated consequences. It can be beneficial in predicting the likelihood of malfunction in bearings based on historical data.

2. Random forests: This technique is a modification of decision trees that uses multiple trees and combines their predictions to increase accuracy.

3. Support vector machines: This technique works by finding a hyperplane that separates data of one class from another, which can be useful for predicting whether bearings will malfunction or not.

4. Deep learning: This technique involves training neural networks to identify patterns in large amounts of data. It is useful for predicting when bearings are likely to fail based on a wide range of inputs.

Ultimately, the most appropriate technique will depend on the specific parameters of the system and the data available. It is recommended to consult an expert in predictive maintenance to determine the best technique for the particular situation.