Model-Aided Diagnosis of Mechanical Systems

The word “system” is used here for a technical construction (object) in its operational environment from, for instance, civil, mechanical, automotive, coastal, ship, or aerospace engineering. The forces acting on the system during its utilization affect the system’s properties. An example is the stiffness degradation through a crack due to cyclic forcing, and the hardening effect of high static forces at the beginning of a fatigue test. If the system property changes reduce safety, lifetime or other aspects of performance, and comfort, then failures (defects, faults, damage) can occur. Because the safety effects of failures can be catastrophic, early failure detection, localization and assessment are necessary during the system’s life.

Symptom-based diagnosis is described in [22–24]. Symptoms are sensitive quantities with respect to a fault, and they are used for fault detection and also for diagnostic purposes. Classical diagnostic methods are signal-based and signature-supported. Simple signature analysis employs data-reduction methods, for example counting the number of peaks of a spectral function. In general, signature analysis involves scalar information. Multiple features are applied independently. Extended signature analysis uses vectorial information and patterns. A set of features will be evaluated and used for decision and assessment.

The examples discussed here will illustrate some of the approaches that have been presented in the book only in principle. We do not intend to demonstrate the model-based procedure completely or in detail

Signal and signature supported diagnosis are well known and have already been introduced for the monitoring and diagnosis of (vibrating) systems in operation. Higher demands with respect to the safety and lifetimes of systems require efficient and automatically elapsing tools for assessment and decision. From the economic point of view, the costs of development, and during operation/service of a system, can be lowered if the faults of a system are detected early and their evolution is known. Assessment of such faults then leads to action in time, and avoids subsequent costs. The costs of monitoring itself can also be lowered if the monitoring is performed state-dependent and event-dependent, and not periodically. With regard to machines the importance of monitoring and diagnostics is obvious, because condition monitoring was introduced in this field of application much earlier than in other technical fields. For civil engineering systems it is obvious that the deteriorating infrastructure and environment both pose challenging problems for engineering and diagnostics as well. It would be a great contribution to society if engineers could save even a fraction of the percentage of the cost by improving the present method of maintaining the infrastructure [204] and by designing in a new, more service-orientated manner. Efficient diagnosis procedures are the basis for economic renewal engineering