Objective
The main goal of the project is the development of a new methodology for condition monitoring and predictive maintenance of machine tools.
A project was created to develop and validate a specific methodology integrating different procedures of modelling and simulation in order to:
shorten fault diagnosis of newly designed machine tools;
make predictive maintenance more applicable;
improve scheduled maintenance, thus avoiding further damage and loss of production. Steps were taken to:
gain a more comprehensive knowledge on the fault propagation of the machine, using a simulation model, which included information derived from design material (circuit diagrams and computer aided design drawings);
predict soft faults (ie faults in which the fault trends over a transition period to failure);
update the knowledge bases and data bases necessary for fault location;
reduce additional sensor instrumentation. The feasibility of the approach was tested on a prototype installation on a real machine tool. The project consisted of the following main modules: repository, sequencer, data acquisition, simulation, pattern recognition, machine learning and maintenance module.
Repository: the core of the system, it stores all the data required by the various modules as well as providing the means of message exchange between modules. The various communication protocols are supported in a generic form, but implemented in the appropriate modules.
Sequencer: synchronizes all processes in the system.
Data acquisition: the data acquisition system (DAS) was initially designed to collect field data and was then refined to provide data for the system producing the research DAS.
Simulation: the simulation models include both normal and faulty behaviour. Models have been developed for the following subsystems: central lubrication, spindle motor, pneumatic, coolant, hydraulic, axis drive motor.
Pattern recognition: a set of 3 different classification algorithms have been implemented in the classification module: the perceptron algorithm, a least mean square estimation algorithm and the increment correction algorithm.
Machine learning: the machine learning module has 6 main tasks: to increase classification confidence;
to detect new fault cases;
to perform normal parameter tracking;
to perform fault case parameter tracking;
to calculate time to criticality;
to select appropriate repair plans.
Maintenance module: elaborates a maintenance strategy for the machine tool.
Generally, the prototype system performed well.
This methodology will describe applicable techniques to predict and evaluate the evolution of failures and the degradation of machine tool components (eg. ballscrews, axis drives, coolant system, hydraulics), in order to find out the causes of malfunctions, to suggest corrective and preventive actions and to provide support in the operation mode. The proposed methodology is expected to help improve the availability, reliability and maintainability of machine tools as well as other types of machines or pcesses. The methods will be based on modelling of the machine and its subsystems with physical as well as qualitative models.
The results of the project will enhance fault diagnosis of newly designed machines, make predictive maintenance more applicable and improve scheduled maintenance.
In order to demonstrate the feasibility of this approach the machine management prototype will be fitted to and tested on a field machine tool. The basic ideas, however, which, will be detailed below, are thought to be general enough to be applied to other types of industrial equipment and processes.
A low cost system is targeted, and thus the requisite software will be developed for a 386 type computer.
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Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology mechanical engineering tribology lubrication
- natural sciences computer and information sciences software
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
- natural sciences computer and information sciences artificial intelligence pattern recognition
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Coordinator
78727 Oberndorf
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.