Industrial pneumatic systems are central to automated production but often exhibit low efficiency and high energy costs due to pressure drops, leaks, and related issues. Air leaks increase operational expenses and degrade actuator performance. This study introduces a reliable classification-based approach for subsystem-level localization of single leaks. Each operating cycle is mapped to one of four actionable states using a Dynamic Time Warping (DTW) algorithm applied to time-series data from a single inlet flowmeter, distributor switching signals, and actuator end-position sensors. The method distinguishes whether a leak occurs in the supply line, actuator circuit, or dynamically during the extension stroke. Unlike conventional metrics such as Euclidean, Canberra, and Pearson distances, DTW does not require equal-length signals and compensates for leak-induced temporal shifts, achieving complete class separation. Experimental validation included 200 cycles: 50 under normal conditions and 150 across three fault states (inlet leak, actuator leak, and dynamic leak), sampled at 10 Hz. DTW distances showed no overlap between class distributions. A Leave-One-Out 1-NN classifier achieved 100% accuracy across all classes. The approach is low-cost, automated, and suitable for real-time implementation with minimal sensors, supporting integration into machine learning frameworks and enhancing energy efficiency in pneumatic systems.
Titova et al. (Thu,) studied this question.