Application of Context-Based Meta-Learning Schemes for an Industrial Device

The paper presents the application of fault diagnosis schemes for an industrial device. Presented schemes were based on various machine learning algorithms using single classifiers and different types of ensemble-classification methods. Besides the well-known methods of ensemble-classification, the author also implemented and used context-based meta-learning method. Presented schemes were tested using artificial datasets generated by the benchmark of a longwall scraper conveyor. Model allows for testing various scenarios of operation modes of the conveyor, including the possibility of modelling operational faults. Signals generated by model were connected with velocity sensor of the conveyor and current sensors of motors. Collected data was divided into smaller parts dependent on the values of discrete contextual feature. The contextual feature cannot be used directly by a classifier, but can be useful when it is combined with other features. It does not have direct information about operational faults of the device. Speaker's sex, nationality and age are examples of contextual features for speech recognition problem. Operational states of the conveyor and a longwall shearer was used to determine contextual feature for case study described in the article. In the next step each part of data was used to calculate single value of the feature like average value, median, skewness, etc. New dataset containing only features of measured signals was used during training and verification process of classifiers using a cross-validation method. The author compared results of reasoning processes based on the proposed schemes and basic types of the classifiers. The achieved results confirm the effectiveness of the new proposed approach based on context and also show its limitations.