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Reliability-Adaptive Systems

A definition of a Reliability-Adaptive System and some perspectives in application are given here. A Reliability-Adaptive System has to fulfil the two characteristic requirements and may integrate the properties of an additional aspect:

  • 1st Requirement — Reliability Observation
    The quantitative reliability properties of a system and its components are estimated also during operation, i.e. after the system is put into service, but before an item under consideration fails. For example, reliability characteristics (current values of failure rate functions, probability density functions, cumulative distribution functions, membership functions, belief and plausibility functions, etc.) are calculated for each instant of operation.
     
  • 2nd Requirement — System Influence
    The achieved results must influence the system operation; some examples are listed below.
     
  • Additional Aspect — Reliability Prognosis
    A prognosis based on the reliability characteristics progression is conducted. The result of this prognosis is a quantified forecast given by reliability measures.
Generally, Reliability-Adaptive Systems allow automation of decision-making within processes, e.g.:
  • Component Derating
    Derating of components during operation can be optimised within a given minimal operation time frame or a given system task frame. More generally speaking, the system can be transferred into a state, which would result in more reliable operation.
     
  • System Individualisation
    An individual system of a fleet can be operated depending on individual reliability properties.
     
  • System Control
    A system can be controlled by considering reliability aspects. In addition to conventional control strategies, information about reliability properties can be implemented into closed loop realisations.
     
  • System Reconfiguration
    Adaptive reconfiguration strategies for redundant systems can be realised.
     
  • System Maintenance Optimisation
    The time to preventive maintenance can be estimated more precisely for each individual system.
The main application fields are automation, control, machine tool engineering, robotics. Presently, the following issues can be seen as problem areas: validity of assumptions, timeliness of results found, and implications for the sensitivity of results.