Determination of the machine energy consumption profiles in the mass-customised manufacturing

Abstract

This paper presents an original methodology and related algorithms that are dedicated to monitoring energy efficiency in discrete production stations. The learning phase of the algorithm is executed during the regular production process and is based on observations of the behaviour of the production station and energy consumption measurements. The collected information is then processed using data-mining procedures in order to find the clusters that reflect the energy consumption profiles that are specific for different variants of production. The profiles are used to monitor energy efficiency and detect anomalies. The main benefit of the proposed approach is its flexibility. No additional calibrating operations or technological knowledge are required. Although the presented proof by research results are focussed on pneumatic air installations, the proposed methodology can also be used for other media. The output of the proposed solution is a very precise estimation of energy consumption in reference to a given variant of production. It allows for the accurate detection of compressed air consumption anomalies. Such anomalies can be caused by technical (machine faults) and technological (human errors) problems. The proposed methodology can be applied for the optimisation of energy consumption and for the detection of machine maintenance problems that are visible through abnormal compressed air consumption.

Keywords:

• predictive maintenance
• performance analysis
• energy efficiency
• data mining
• clustering

Acknowledgements

This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project ‘Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model’: [Grant Number 612207] and research work financed from funds for science for years 2016–2017 allocated to an international co-financed project: [Grant Number 3491/7.PR/15/2016/2].

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project ‘Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model’: [Grant Number 612207] and research work financed from funds for science for years 2016–2017 allocated to an international co-financed project: [Grant Number 3491/7.PR/15/2016/2].