| 1 | 1. Title: Miba Friction Plate Testing Data
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| 2 | 2. Sources:
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| 3 | (a) Miba frictec, Andreas Promberger, Peter Mitterbauer Str. 1,
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| 4 | A-4661 Roitham, AUSTRIA, +43 76139020, Andreas.promberger@miba.com
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| 5 | (b) FH Upper Austria, Gabriel Kronberger, Softwarepark 11,
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| 6 | A-4232 Hagenberg, AUSTRIA, +43 50804 22320,
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| 7 | gabriel.kronberger@heuristiclab.com
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| 8 | (c) April 2017
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| 9 |
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| 10 |
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| 11 | 3. Past Usage:
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| 12 | - G. Kronberger, M. Kommenda, E. Lughofer, S. Saminger-Platz,
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| 13 | A. Promberger, F. Nickel, S. Winkler, M. Affenzeller - Robust
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| 14 | Generalized Fuzzy Modeling and Enhanced Symbolic Regression for
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| 15 | Modeling Tribological Systems, submitted to Applied Soft
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| 16 | Computing, 2017
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| 17 |
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| 18 | - E. Lughofer, G. Kronberger, M. Kommenda, S. Saminger-Platz,
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| 19 | A. Promberger, F. Nickel, S. M. Winkler, M. Affenzeller - Robust
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| 20 | Fuzzy Modeling and Symbolic Regression for Establishing Accurate
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| 21 | and Interpretable Prediction Models in Supervising Tribological
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| 22 | Systems - Proceedings of the 8th International Joint Conference on
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| 23 | Computational Intelligence, Porto, Portugal, 2016, pp. 51-63
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| 24 |
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| 25 | 4. Relevant information:
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| 26 | A set of datasets for regression modelling of
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| 27 | friction characteristics of friction plate systems. The data stem
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| 28 | from tests of friction plates with commercial test benches for wet
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| 29 | friction plate systems. Friction characteristics such as the
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| 30 | coefficient of friction, wear, and temperatures are measured at
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| 31 | different loads. The goal is to predict these values given load
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| 32 | parameters. Data have been kindly provided by Miba frictec company.
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| 33 |
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| 34 | 5. Number of instances
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| 35 | A separate file is provided for each target variable. The values of
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| 36 | the target variable are given in the last column.
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| 37 |
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| 38 | - Cf1: 815 instances
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| 39 | - Cf2: 2921 instances
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| 40 | - Cf3: 657 instances
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| 41 | - Cf4: 649 instances
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| 42 | - NvhRating: 3943 instances
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| 43 | - Temp1: 656 instances
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| 44 | - Temp2: 648 instances
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| 45 | - Wear1: 904 instances
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| 46 | - Wear2: 902 instances
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| 47 |
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| 48 | 6. Number of Attributes: 28
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| 49 | (2 binary, 22 numeric & continuous, and four nominal) Depending on
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| 50 | the target variable (or file) some of the attributes might be
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| 51 | constant.
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| 52 |
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| 53 | 7. Attribute Information
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| 54 | The first column 'Partition' contains the partition assignment that
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| 55 | should be used for validation of algorithms. This attribute should
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| 56 | not be used for modelling. Rows must not be shuffled as in standard
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| 57 | cross-validation.
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| 58 |
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| 59 | - Source1: indicates that the row stems from data source one [binary]
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| 60 | - Source2: indicates that the row stems from data source two (
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| 61 | redundant given Source1) [binary]
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| 62 | - x1: an attribute of the testing procedure [numeric, integer]
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| 63 | - Material_Cat: represents the type of friction material [nominal]
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| 64 | - x2, ... ,x16: material attributes [numeric, continuous]
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| 65 | - Material: represents the friction material [nominal]
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| 66 | - Grooving: represents the grooving (surface structure) on the
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| 67 | friction plate [nominal]
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| 68 | - Oil: represents the oil type [nominal]
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| 69 | - x17, ... ,x22 load attributes [numeric, continuous]
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| 70 |
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| 71 |
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| 72 | If you use these data files please use the following reference:
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| 73 | G. Kronberger, M. Kommenda, E. Lughofer, S. Saminger-Platz,
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| 74 | A. Promberger, F. Nickel, S. Winkler, M. Affenzeller - Robust
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| 75 | Generalized Fuzzy Modeling and Enhanced Symbolic Regression for
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| 76 | Modeling Tribological Systems, submitted to Applied Soft Computing,
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| 77 | 2017
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| 78 |
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