Predictive maintenance with a minimum of sensors using pneumatic clamps as an example

In standard pneumatics, the available signals for data analytics are very limited. As a rule, no continuous status information is available. Usually only the reaching of the end position is indicated by means of a digital signal of a proximity sensor. This paper examines whether these limited data can be used to derive usable and useful information for predictive maintenance. Pneumatic clamps in body-in-white construction were chosen as application example. The paper describes a continuous run to investigate the basic feasibility of predictibility. In the following chapters, possibilities for error classification are discussed. Finally, the implementation of the findings in a field test is described.