Data
NPSdecay

NPSdecay

in_preparation ARFF Publicly available Visibility: public Uploaded 17-01-2018 by francesca cipollini
1 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Andrea Coraddu, Francesca Cipollini, Luca Oneto, Davide Anguita Please cite: Cipollini, F. and Oneto, L. and Coraddu, Murphy, A.J. and Anguita, D., Condition-Based Maintenance of Naval Propulsion Systems: Data Analysis with Minimal Feedback, RESS (Under review), 2018. Dataset: Hull, Propeller and Gas Turbine efficiency decay: Data Analysis with Minimal Feedback Abstract: The proposed dataset is the result over experiments carried out by means of a numerical simulator of a naval vessel (Frigate), characterized by a COmbined Diesel ELectric And Gas (CODLAG) propulsion plant. The different blocks forming the complete simulator have been developed adopting Matlab and Simulink frameworks, and fine tuned over the years by means of real operational data of similar propulsion plants.These blocks describe the behaviour of the main components of a Naval Propulsion System (NPS), which are the gas turbine (GT), the gas turbine compressor (GTC), the hull (HLL), and the propeller (PRP). In view of these observations, the available data are in agreement with a possible real vessel. In this release of the simulator it is also possible to take into account the performance decay over time of the PRP Thrust, PRP Torque, the HLL, the GTC and GT, according to several vessel parameters. A series of measures (25 features) which indirectly represents of the state of the system subject to performance decay has been acquired and stored in the dataset over the parameter's space. For each record it is provided: * - A 25-feature vector containing the vessel relevant features: * Lever (lp) [ ]; * Vessel Speed [knots]; * Gas Turbine shaft torque (GTT) [kN m]; * Gas Turbine Speed (GT rpm) [rpm]; * Controllable Pitch Propeller Thrust stbd (CPP T stbd)[N]; * Controllable Pitch Propeller Thrust port (CPP T port)[N]; * Shaft Torque port (Q port) [kN]; * Shaft rpm port (rpm port)[rpm]; * Shaft Torque stbd (Q stdb) [kN]; * Shaft rpm stbd (rpm stbd) [rpm]; * HP Turbine exit temperature (T48) [C]; * Generator of Gas speed (GG rpm) [rpm]; * Fuel flow (mf) [kg/s]; * ABB Tic control signal (ABB Tic) []; * GT Compressor outlet air pressure (P2) [bar]; * GT Compressor outlet air temperature (T2) [C]; * External Pressure (Pext) [bar]; * HP Turbine exit pressure (P48) [bar]; * TCS tic control signal (TCS tic) []; * Thrust coefficient stbd (Kt stbd) []; * Propeller rps stbd (rps prop stbd) [rps]; * Thrust coefficient port (Kt port) []; * Propeller rps port (rps prop port) [rps]; * Propeller Torque port (Q prop port) [Nm]; * Propeller Torque stbd (Q prop stbd) [Nm]; * - Propeller Thrust decay state coefficient (Kkt); * - Propeller Torque decay state coefficient (Kkq); * - Hull decay state coefficient (Khull); * - GT Compressor decay state coefficient (KMcompr); * - GT Turbine decay state coefficient (KMturb). Notes: - Features are not normalized - Each feature vector is a row on the text file (30 elements in each row) For more information about this dataset please contact: cbm@smartlab.ws Check at www.cbm.smartlab.ws for updates on this dataset. License: Use of this dataset in publications must be acknowledged by referencing the following publication This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited. Other Related Publications: * [1] Cipollini, F., Oneto, L., Coraddu, A., Murphy, A. J., & Anguita, D. (2018). Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis. Ocean Engineering, 149, 268-278. * [2] Coraddu, A. and Oneto, L. and Ghio, A. and Savio, S. and Anguita, D. and Figari, M. - Journal: Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment - Number: 1 - Pages: 136-153 - Title: Machine learning approaches for improving condition-based maintenance of naval propulsion plants - Volume: 230 - Year: 2016.

30 features

lpnumeric9 unique values
0 missing
Vessel_Speednumeric9 unique values
0 missing
GTTnumeric90428 unique values
0 missing
GT_rpmnumeric2524 unique values
0 missing
CPP_T_stbdnumeric69272 unique values
0 missing
CPP_T_portnumeric69272 unique values
0 missing
Q_portnumeric62580 unique values
0 missing
rpm_portnumeric5622 unique values
0 missing
Q_stdbnumeric62580 unique values
0 missing
rpm_stbdnumeric5622 unique values
0 missing
T48numeric50735 unique values
0 missing
GG_rpmnumeric19208 unique values
0 missing
mfnumeric73208 unique values
0 missing
ABB_Ticnumeric77729 unique values
0 missing
P2numeric35774 unique values
0 missing
T2numeric21675 unique values
0 missing
Pextnumeric298 unique values
0 missing
P48numeric19231 unique values
0 missing
TCS_ticnumeric82597 unique values
0 missing
Kt_stbdnumeric37431 unique values
0 missing
rps_prop_stbdnumeric5985 unique values
0 missing
Kt_portnumeric37431 unique values
0 missing
rps_prop_portnumeric5985 unique values
0 missing
Q_prop_portnumeric65659 unique values
0 missing
Q_prop_stbdnumeric65659 unique values
0 missing
Kktnumeric15 unique values
0 missing
Kkqnumeric15 unique values
0 missing
Khullnumeric15 unique values
0 missing
KMcomprnumeric15 unique values
0 missing
KMturbnumeric15 unique values
0 missing

62 properties

455109
Number of instances (rows) of the dataset.
30
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
30
Number of numeric attributes.
0
Number of nominal attributes.
1096.71
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-1.05
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
0.74
Third quartile of skewness among attributes of the numeric type.
0.97
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
343.88
Third quartile of standard deviation of attributes of the numeric type.
277484.69
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-1.21
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
1.05
First quartile of means among attributes of the numeric type.
-0.85
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
45439.96
Mean of means among attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.06
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.92
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.38
Mean skewness among attributes of the numeric type.
25.1
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
37473.53
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.56
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.45
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
17.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
-0.14
Maximum kurtosis among attributes of the numeric type.
0.15
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.57
Third quartile of kurtosis among attributes of the numeric type.
343569.7
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

1 tasks

0 runs - estimation_procedure: 50 times Clustering
Define a new task