Leakage Fault Diagnosis of Two Parallel Cylinders in Pneumatic System with a Minimal Number of Sensors

Abstract

The low investment cost is one of the core competitiveness advantages of pneumatic power systems. With increasingly pressing intelligent manufacturing, it is meaningful to investigate the feasibility of implementing fault diagnoses of pneumatic systems with a minimal number of low-cost sensors. In this study, a typical pneumatic circuit with two parallel-installed cylinders is taken as an example. The pressure, flow rate, and exergy data collected from upstream sensors are used for diagnosing the leakage faults in two downstream cylinders with the help of different machine learning methods. The features of data are extracted with stacked auto-encoders. Gaussian process classifier, support vector machine, and k-nearest neighbor are used for classifying faults. The results show that it is feasible to detect and diagnose downstream multi-faults with one or two upstream sensors. In terms of the working conditions presented in this study, the average accuracy of diagnosis with exergy data is the highest, followed by flow-rate data and pressure data. The support vector machine performs the best among the three machine learning methods.