Research projects

Project director: Prof. Dr.-Ing. Peter Zeiler

Project worker: Moritz Müllerschön, Luca Steinmann

Cooperation partners: Cross-faculty project at Esslingen University of Applied Sciences

Brief description of the research project:

A Smart Factory Grid is the vision for an innovative, service-based and dynamically distributed manufacturing system. It is characterized by high flexibility and networking and represents a central component of Industry 4.0. Modular production units, so-called micro manufacturing units, interact within a smart factory grid. The aim of the System Health research unit is to develop methods for the intelligent diagnosis and prediction of the status of micro manufacturing units and the smart factory grid, as well as the optimization of maintenance processes.

Research question 1: Adaptive condition diagnosis and prognosis under variable loads (Luca Steinmann)

Due to its high adaptability, flexibility and resilience, matrix production places high demands on condition diagnosis and prognosis. Varying and unknown load profiles make it difficult to develop suitable methods. Initial approaches exist for forecasting future load profiles based on historical and current data, but condition forecasting under variable conditions remains a key challenge. Limited data quality, incomplete or censored data and uncertainty about future loads add to the problem.

The research approach aims to better understand and precisely predict the relationships between stress and degradation. One focus is on analyzing and integrating varying load profiles and overcoming data-related challenges. Approaches such as data augmentation and data generation are intended to expand the database and increase the robustness of the models in order to develop a holistic methodology for prediction considering variable load.

Research question 2: Adaptive condition diagnosis and prognosis with heterogeneous information (Moritz Müllerschön)

The development of a smart factory grid poses specific challenges. In particular, the heterogeneity of information sources poses a central problem. The data is often highly dimensional, formatted differently and varies in terms of availability and quality. Examples of this are sensor and operating data, simulation results and physical models. This makes it considerably more difficult to combine and use this data for condition diagnosis and forecasting. Another obstacle is the lack of a comprehensive methodology for information fusion.

One research approach is to develop a hybrid model for information fusion that combines data-driven methods with physical and rule-based models. Methods are to be developed to compensate for the limited availability of data and to effectively integrate existing domain knowledge. By developing a standardized data framework for pre-processing and integrating heterogeneous information sources, the basis for a robust condition diagnosis and prognosis can be created.

Research question 3: Health management in a smart factory grid (Moritz Müllerschön)

Health management deals with the continuous monitoring, analysis and optimization of the condition of production plants and processes and aims to ensure the availability and reliability of the system. In the context of the flexible and modular environment of a smart factory grid, this requires a high degree of adaptability.

The research approach aims to address health management in a smart factory grid as a complex optimization problem, as the complexity of production no longer allows health management using conventional methods. Important metrics here are costs, sustainability, productivity and many more. Predictive maintenance is intended to enable the automated generation of maintenance proposals in order to make precise and efficient maintenance decisions.

Further information on the entire Smart Factory Grids | DFG Research Impulse project can be found here:

Smart Factory Grids | DFG Research Impulse

(Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 528745080 – FIP 68)

Selection and implementation methodology for prognostic methods to predict the remaining useful life of a technical system taking account of practically relevant and application-specific constraints

This is a doctoral project in the field of Prognostics and Health Managements (PHM) and is being undertaken as a collaboration between the Institute for Technical Reliability and Prognostics (IZP) at Esslingen University of Applied Sciences and the Institute of Industrial Manufacturing and Management (IFF) at the University of Stuttgart.

A variety of possible prognostic methods are available to predict the remaining useful life of a technical system. They can be divided into data-driven, model-based and hybrid approaches.

The aim of this research project is to investigate a selection and implementation methodology which takes account of practically relevant and application-specific data. The focus is particularly on linking fundamental research results with the actual practical requirements of industry when developing prognostic methods.

Collaborating university: University of Stuttgart
First supervisor: Prof. Dr.-Ing. Marco Huber
Second supervisor: Prof. Dr.-Ing. Peter Zeiler
Doctoral student: Fabian Mauthe

Use of knowledge of similar systems for data-driven condition diagnosis and prognosis in industrial environments

This doctoral project in the field of Prognostics and Health Management is being undertaken as a collaboration between the Institute for Technical Reliability and Prognostics (IZP) and the University of Stuttgart. More information on this doctoral project can be found in the description of the REDAT research project.

Collaborating university: University of Stuttgart
First supervisor: Prof. Dr.-Ing. Peter Zeiler
Second supervisor: Prof. Dr.-Ing. Marco Huber
Doctoral student: Marcel Braig

Information fusion for condition diagnostics and prognostics and health management in a smart factory grid

This doctoral project in the field of Prognostics and Health Management is being undertaken as a collaboration between the Institute for Technical Reliability and Prognostics (IZP) and the University of Stuttgart. It is embedded in the “Smart Factory Grids” funding project, which addresses a service-based, dynamically distributed production system with high flexibility, networking, and modular mobile production units.

As part of the doctoral program, methodologies will be developed to answer research questions on systematic information fusion in the diagnosis and prognosis of the condition of technical systems. Information fusion involves the integration of data of different formats, origins, and quality. This should enable diagnosis and prognosis even with limited data, increase the accuracy of algorithms, and reduce uncertainty.

In addition, the complex planning of maintenance measures is to be formulated as an optimization problem and results from information fusion are to be integrated. Metrics for this can include costs, orders, sustainability, productivity, and others.

Collaborating university: University of Stuttgart

First supervisor: Prof. Dr.-Ing. Peter Zeiler

Second supervisor: Prof. Dr.-Ing. Marco Huber

Doctoral student: Moritz Müllerschön

Smart Grind

Number of industrial cooperation partners: 1

Brief description of the research project:

The objective of this research project is to develop a universal retrofit system for the process optimisation of surface grinders. This will allow the path planning of the grinding process to be optimised by taking account of the actual material removal. Hence, the only surfaces which are subjected to grinding are those on which grinding work is actually necessary. This approach, which is based on the intelligent evaluation of high-frequency drive data of the NC control, has the potential to shorten the processing time considerably, and can thus significantly increase the economic efficiency of the surface grinding process. The project will also use machine learning methods to produce a condition diagnosis and prognosis for the grinding disc. Knowledge of the condition of the grinding disc helps companies to meet the high quality demands placed on the surface finish and the form and positional tolerances of the grinding process. Furthermore, this information allows maintenance intervals to be implemented as required for optimum exploitation of the tool life (resource efficiency). The on-site evaluation of the data to calculate the optimised path planning, and of the condition diagnosis and prognosis, takes place in an edge. The advantages lie not only in the fact that high security standards are maintained when dealing with sensitive production data, but also in the low latency of the data transmission.

Prognostics

Number of industrial cooperation partners: 3

Brief description of the research project:

The research project aims to enable users, especially SMEs, to realise the latest maintenance strategies, primarily predictive maintenance. To this end, it will develop a method of assessment and selection which takes account of application-specific and industry-related constraints. This categorisation will form the basis for the generation of sets of real degradation data, which will then be made freely available. This helps users with suitable projects to select appropriate methods, and also to assess the implementability and the risks. The method employed here can be data driven, e.g. an artificial neuronal network or Support Vector Machines. The prognostic method can also be model based and use a physical model of the degradation process, e.g. a crack growth equation or a wear model. The possibilities described are illustrated below

Data-driven to hybrid

Brief description of the research project:

Extension of data-driven diagnostic and prognostic methods used in Prognostics and Health Management to hybrid methods through the incorporation of knowledge about the degradation process

This research project explores approaches which can be used in the diagnosis and prognosis of the degradation state of technical systems. The various approaches here can be divided into model-based, hybrid and data-driven approaches. In the model-based methods, the degradation behaviour is simulated in a physical model. A widely used example of this physical modelling are crack progression models such as the Paris-Erdogan equation. They describe the fatigue cracking under cyclic load. However, the degradation process of many technical systems is highly complex, and hence a great deal of effort is required for the detailed implementation of the model-based approach. In contrast, the methods known as data-driven diagnostic and prognostic methods have the advantage that no or very little knowledge of the particular degradation behaviour is required to use them. These methods are categorised under the basic fields of statistics and machine learning. They simply replicate the mathematical relationship between the variables measured and the degradation. As the name suggests, the hybrid approach represents a mixture of the two aforementioned approaches.

ReDat

Number of industrial cooperation partners: 3

Brief description of the research project:

The diagnosis, prognosis and monitoring of the state of health and the remaining useful life of technical systems requires methods which can simulate the system behaviour, possible failure states and the degradation which occurs. These methods are usually categorised into those based on physical models, and data-driven methods. The former are based on a detailed understanding of the system being monitored and its degradation behaviour. The modelling is very involved, however, and can only be used to a limited extent for complex systems. Data-driven methods are thus an important alternative. They extract information about a system on the basis of data alone. The success of data-based approaches depends crucially on there being a sufficiently large quantity of training data, however. The problem is that generating and processing this data is very time consuming and expensive. Moreover, it is often the case that very little data on run time and run-to-failure is available for novel systems (e. g. a new generation of machines) and systems which are produced only in low numbers.

The research project adopts the approach of using information (data or models) from Similar Systems to train data-driven methods. The aim is to reduce the quantity of data required from the system under investigation itself. For example, knowledge of the degradation behaviour of an open deep groove ball bearing can be used for a closed version with similar dimensions. The approaches can be applied to individual components (e.g. bearings, axles, shafts), to machines (e. g. in production) or to products (e. g. gears, engines).

Extension of data-driven diagnostic and prognostic methods used in Prognostics and Health Management to hybrid methods through the incorporation of knowledge about the degradation process

This doctoral project in the field of Prognostics and Health Management is being undertaken as a collaboration between the Institute for Technical Reliability and Prognostics (IZP) and the Institute of Industrial Manufacturing and Management (IFF) at the University of Stuttgart. The doctoral student is a member of the PROMISE 4.0 Collaborative Doctoral Programme. More information on this doctoral project can be found in the description of the DATA-DRIVEN TO HYBRID research project.

Collaborating university: University of Stuttgart
First supervisor: Prof. Dr.-Ing. Thomas Bauernhansl
Second supervisor: Prof. Dr.-Ing. Peter Zeiler
Doctoral student: Simon Hagmeyer

Link to the work: https://elib.uni-stuttgart.de/items/fa4a7c2c-d4d3-4f6b-9e1d-c1972e8c8909