Research Main research topics and fields

In this section, we would like to present the areas of reliability engineering and PHM which are addressed by our Institute for Technical Reliability and Prognostics (IZP). We also highlight our current, main fields of research.

 

Description of the main research topics and fields

Technical reliability aims to describe the failure modes of components and systems. Reliability is understood as the probability that a product does not fail under given operating and environmental conditions during a defined period of time. The development of new products is increasingly characterised by growing complexity and greater functional scope, accompanied by ever shorter development times. In addition, high customer demands mean the demands being placed on the reliability and availability of the products are continuously increasing. Assuring the reliability required is thus of key importance for the success of a product.

Both quantitative and qualitative methods are available to assure the reliability over the whole product lifecycle. The quantitative methods encompass probabilistic reliability analyses, for example. The qualitative reliability methods include the generally established failure modes and effects analysis (FMEA). Methods used in reliability engineering thus allow weak points of a system which already exist during the development phase to be identified and eliminated at an early stage.

One aim of reliability engineering is to compile a quantitative description of the failure mode and to provide proof of reliability for technical systems. Universally applicable statements about the failure mode of a whole fleet can be made on the basis of an analysis of field data and the evaluation of service life experiments and breakdown statistics of individual systems. The data required for this purpose mean that statistical design of experiments (DoE) and accelerated testing are likewise important elements of technical reliability.

Prognostics and Health Management (PHM) is an interdisciplinary approach which combines the classical fields of reliability engineering and statistics with methods used in artificial intelligence and elements of Industry 4.0. PHM involves both an assessment of the condition as well as a prediction of the remaining useful lifetime (RUL).

Basically, a PHM application can be divided into four steps: Data, Diagnosis, Prognosis and Health Management. Information about the current condition of a technical system and an individual prognosis of the remaining useful life are the basis for the use of advanced maintenance strategies such as Predictive Maintenance. The information provided for the optimised planning of maintenance and logistics processes can be used as part of the Health Management.

The ability to predict the remaining useful life of a system as early and precisely as possible is of crucial importance for the successful implementation of a PHM application. This individual prognosis requires that the actual loads and operating conditions, and the existing uncertainties be taken into account. A range of different methods can be used for the prognosis. Fundamentally, a distinction is made between model-based, data-driven and hybrid diagnostic and prognostic methods.

The main fields of research relating to reliability engineering are:

  • Reliability under operational conditions
  • Modelling and simulation of the reliability and availability of complex systems
  • Reliability models and methods

 

The main fields of research relating to PHM are:

  • Data-driven and hybrid methods for condition diagnosis and prognosis
  • Extension of data-driven methods through knowledge of the degradation process
  • Reduction in the quantity of training data needed through the use of Similar System Data/ Transfer Learning
  • Development and utilisation of ensemble models
  • Consideration of the uncertainties with data-driven diagnostic and prognostic methods
  • Methodology for selecting prognostic methods taking account of typical industry-specific constraints
  • Compilation of a database for publicly accessible sets of degradation data
  • Analysis of high-frequency drive data of machine tools for condition diagnosis
  • Vibration analysis of drive components
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