An intelligent Context Based Multi-layered Bayesian Inferential predictive analytic framework for classifying machine states

摘要

Proactive fault diagnosis is a burning issue in condition monitoring of machines. Intelligent methods prove to be promising solutions for designing predictive analytic frameworks for fault diagnosis and machine state classification. The competency of machine learning algorithms in handling large voluminous data has marked them as a natural solution for developing intelligent framework that proactively classifies the machine states. The paper proposes a novel Context Based Multi-layered Bayesian Inferential (CBMBI) predictive analytic framework, which is motivated by MisMatch Negativity (MMN) and Predictive Coding. The CBMBI framework is augmented with a new hyperparameter (context) that greatly reduces the misclassification rate. The performance of the framework is analysed with Case Western Reserve University 6205-2RS JEM SKF dataset. The profound results reveal that the proposed framework shows 97% accuracy and 94% F1-score which is relatively higher than the state of art technique.