Reciprocating compressor multi-fault classification using symbolic dynamics and complex correlation measure

Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set of features was obtained by applying the symbolic dynamics algorithm, which provides the histogram of a set of symbols. This set of symbols was obtained by subdividing a 2D Poincare plot into angular regions and counting the intersection of the phase trajectories on each of regions. The second type of features corresponds to the complex correlation measure which is calculated as the addition of the areas of triangles belonging to a Poincare plot. Additionally, a small set of classical statistical features was also used for comparing their classification abilities to the new set of proposed features. The three sets of features enable highly accurate classification of the set of faults when used with random forest classification models. Notably, the ensemble subspace k-Nearest Neighbors algorithm provides classification accuracies higher than 99%.