A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis

The essence of big 'data-based intelligent industrial fault diagnosis lies in the process of machine learning and feature engineering. Deep learning methods can discover the complex relationship between data and potential faults, and outperform the traditional machine learning methods. The gcForest is able to generate a deep forest ensemble, which allows gcForest to do representation learning and fault classification. At each node of the random tree, gcForest selects the one with the best gini value from candidates for splitting. However, most of the data acquired from industrial scene is with continuous and unstructured attributes, accordingly the node-splitting procedure will be generally intractable. We present a novel approach with the combination of deep Boltzmann machine and multi-grained scanning forest ensemble, to effectively deal with industrial fault diagnosis based on big data. At first, we use deep Boltzmann machine to turn all features of data to be processed by forests into binary, and then utilize multi-grained scanning forest ensemble to process them in every layer of deep Boltzmann machine. By means of the collaborative method, we can address the aforementioned issues. The experimental results and analysis on industrial fault diagnosis under different experimental conditions, show that the fault classification accuracy of the proposed approach is competitive to other popular deep learning algorithms, but also takes much less time than gcForest.