Monitoring Pneumatic Actuators’ Behavior Using Real-World Data Set

Abstract

AbstractDeveloping a big data signal processing method is to monitor the behavior of a common component: a pneumatic actuator. The method is aimed at supporting condition-based maintenance activities: monitoring signals over an extended period, and identifying, classifying different machine states that may indicate abnormal behavior. Furthermore, preparing a balanced data set for training supervised machine learning models that represent the component’s all identified conditions. Peak detection, garbage removal and down-sampling by interpolation were applied for signal preprocessing. Undersampling the over-represented signals, Ward’s hierarchical clustering with multivariate Euclidean distance calculation and Kohonen self-organizing map (KSOM) methods were used for identifying and grouping similar signal patterns. The study demonstrated that the behavior of equipment displaying complex signals could be monitored with the method described. Both hierarchical clustering and KSOM are suitable methods for identifying and clustering signals of different machine states that may be overlooked if screened by humans. Using the proposed methods, signals could be screened thoroughly and over a long period of time that is critical when failures or abnormal behavior is rare. Visual display of the identified clusters over time could help analyzing the deterioration of machine conditions. The clustered signals could be used to create a balanced set of training data for developing supervised machine learning models to automatically identify previously recognized machine conditions that indicate abnormal behavior.