Abstract
Reliability and safety aspects are becoming much more important due to higher quality requirements, complicated and/or connected processes. The fault monitoring systems to be commonly used in machine- and rotordynamics are based on signal analysis methods. Furthermore, various kinds of fault detection and isolation (FDI)-schemes are already applied to a lot of technical applications of detecting and isolating sensor and actuator failures (Isermann, 1994; van Schrick, 1994) and also to fault detection in power plants (in general) or in manufacturing machines. An implicit assumption is that process or machine changes due to faults lead to changes in calculated parameters, which are unique and unambiguous. In the case of applying methods of signal analysis this means spectrums etc. the vibration behaviour will be monitored very well but have to be interpreted. On the other hand signal parameters usually only describe the system by analyzing output signals without use of known and unknown inner parameters and/or inputs. These parameters are available, and normally this knowledge is used by the operating staff interpreting the resulting signal parameters. In this way a decision-making problem appears so that questions about the physical character of faults, about the existence of special faults and also about the location of failures/faults has to be answered. In this way the experience and knowledge of the interpreting persons are very important. In this contribution the problems of the decision-making process are tried to defuse:
• The available knowledge about the unfaulty system parameters is used to built up beside a nominal system model an unambiguous fault-specific ratio. Inner states of the structure are estimated by an PI-observer.
• The developed robust PI-observer (Söffker et al., 1993a; Söffker et al., 1995a) estimates inner states and unknown inputs.
In (Söffker et al., 1993b) this new method is applied to the crack detection of a rotor, but not proved. In this paper the proof is given and a generalization is described. The advantages in contrast to usual signal based vibration monitoring systems and also modern FDI-schemes are shown.
Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy