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.

Pneumatic systems are widely used in industrial manufacturing sectors. However, the energy efficiency of pneumatic systems is generally much lower than their hydraulic and electric counterparts. It is necessary to explore more elaborate theories and methods for achieving better energy performance in pneumatic systems. In this study, for investigating the interaction effects between pneumatic components and the accessible improvement potential of energy efficiency in a pre-existing pneumatic system, the advanced exergy analysis is conducted with a better understanding of exergy destruction. The conventional exergy analysis is also carried out for comparison. The results show that an exergy efficiency of 17.3% could be achieved under the real condition in the case of the investigated pneumatic system. However, under unavoidable conditions, the theoretical maximum exergy efficiency could reach 70.5%. This means there is a significant potential for improving the energy performance of the investigated system. Furthermore, both conventional and advanced exergy analyses indicate that the pneumatic cylinder has the greatest potential for improvement. The advanced exergy analysis reveals the complex and variable interactions between pneumatic components. It highlights that the exergy destruction of some components is caused by other components in the system, and thus, improving energy efficiency at the system level rather than at the component level is of great significance. Besides, a priority order of all pneumatic components is determined, thereby guiding the improvement of the energy efficiency of the pneumatic system.