nasa-cmapss-rul

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
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+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+Damage[[:space:]]Propagation[[:space:]]Modeling.pdf filter=lfs diff=lfs merge=lfs -text

Damage Propagation Modeling.pdf ADDED Viewed

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+size 434158

README.md ADDED Viewed

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+---
+ language:
+  - "en"
+  - "id"
+ pretty_name: "Modified CMAPSS (Turbofan Engine Degradation)"
+ tags:
+  - timeseries
+  - prognostics
+  - predictive-maintenance
+  - turbofan
+  - RUL
+  - datathon
+ license: cc0-1.0
+ task_categories:
+  - time-series-forecasting
+  - tabular-regression
+ size_categories:
+  - 100K<n<1M
+---
+# Modified CMAPSS Dataset (Turbofan Engine Degradation)
+## 📘 Description
+This dataset is a **modified version** of the NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) turbofan engine degradation simulation dataset. The modification was created by our team as part of a submission for **RISTEK UI Datathon 2025**, in conjunction with the predictive modeling work we developed.
+Each entry in this dataset corresponds to one engine's operating cycle. Engines begin with varying degrees of wear and manufacturing variation, operate normally, and eventually experience degrading performance leading to system failure. The task is to predict **Remaining Useful Life (RUL)** based on sensor and operating condition readings.
+## 📂 Dataset Subsets
+The dataset is divided into four subsets, consistent with the original C-MAPSS structure:
+| Subset | Train Units | Test Units | Operating Conditions | Fault Modes                      |
+|--------|-------------|------------|----------------------|----------------------------------|
+| FD001  | 100         | 100        | 1 (Sea Level)        | 1 (High Pressure Compressor)     |
+| FD002  | 260         | 259        | 6                    | 1 (High Pressure Compressor)     |
+| FD003  | 100         | 100        | 1 (Sea Level)        | 2 (HPC + Fan Degradation)        |
+| FD004  | 248         | 249        | 6                    | 2 (HPC + Fan Degradation)        |
+## 🛠️ Modifications
+The original dataset has been preprocessed and extended for easier machine learning use:
+- **Added RUL column**: Remaining Useful Life (RUL) has been precomputed for each cycle in the training set.
+- **Added headers**: All columns are clearly labeled for usability.
+- **Converted format**: Original space-delimited files are now provided as `.csv` files.
+## 🧾 Data Fields
+Each row corresponds to a single time step (cycle) for one engine unit. It includes 27 columns:
+unit_number, time_in_cycles, op_setting_1, op_setting_2, op_setting_3, sensor_1, sensor_2, ..., sensor_21, RUL
+## 📈 Intended Use
+- Benchmarking and training models for Remaining Useful Life (RUL) prediction
+- Educational and research use in predictive maintenance and health monitoring
+## ⚠️ Limitations and Considerations
+- This dataset is simulated and may not fully reflect real-world engine behavior.
+- Only includes two types of fault modes.
+- Best used as a benchmark for research rather than direct deployment.
+## 📚 Citation
+If you use this dataset, please cite the original NASA dataset as well as our submission if relevant:
+@inproceedings{saxena2008damage,
+title={Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation},
+author={Saxena, A. and Goebel, K. and Simon, D. and Eklund, N.},
+booktitle={Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08)},
+year={2008},
+address={Denver, CO}
+}
+# Dataset CMAPSS yang Dimodifikasi (Degradasi Mesin Turbofan)
+## 📘 Deskripsi
+Dataset ini adalah **versi modifikasi** dari dataset simulasi degradasi mesin jet turbofan NASA C-MAPSS. Modifikasi ini dibuat oleh tim kami sebagai bagian dari **submisi untuk RISTEK UI Datathon 2025**, seiring dengan model prediktif yang kami kembangkan.
+Setiap entri merepresentasikan siklus operasional dari satu mesin. Mesin dimulai dalam kondisi normal (dengan variasi keausan awal), kemudian mengalami degradasi hingga akhirnya gagal. Tugas utama dari dataset ini adalah memprediksi **Remaining Useful Life (RUL)** berdasarkan pembacaan sensor dan kondisi operasi.
+## 📂 Subset Dataset
+Dataset dibagi ke dalam empat subset seperti pada struktur CMAPSS asli:
+| Subset | Unit Latih | Unit Uji | Kondisi Operasi     | Mode Kegagalan                  |
+|--------|------------|----------|----------------------|---------------------------------|
+| FD001  | 100        | 100      | 1 (Permukaan Laut)   | 1 (High Pressure Compressor)    |
+| FD002  | 260        | 259      | 6                    | 1 (High Pressure Compressor)    |
+| FD003  | 100        | 100      | 1 (Permukaan Laut)   | 2 (HPC + Degradasi Fan)         |
+| FD004  | 248        | 249      | 6                    | 2 (HPC + Degradasi Fan)         |
+## 🛠️ Modifikasi
+Dataset asli dimodifikasi untuk memudahkan pemrosesan dan pemodelan:
+- **Penambahan kolom RUL**: Sisa siklus hidup (RUL) dihitung dan ditambahkan.
+- **Header kolom ditambahkan**: Semua kolom diberi nama deskriptif.
+- **Format diubah**: File teks dipisahkan spasi diubah menjadi `.csv`.
+## 🧾 Struktur Data
+Setiap baris merepresentasikan satu siklus operasional untuk satu unit mesin dengan total 27 kolom:
+unit_number, time_in_cycles, op_setting_1, op_setting_2, op_setting_3,
+sensor_1, sensor_2, ..., sensor_21, RUL
+## 📈 Penggunaan yang Disarankan
+- Melatih dan menguji model prediksi Remaining Useful Life (RUL)
+- Penelitian dan edukasi dalam konteks predictive maintenance
+## ⚠️ Keterbatasan
+- Ini adalah data hasil simulasi, bukan dari mesin nyata
+- Hanya mencakup dua jenis mode kegagalan
+- Sebaiknya digunakan sebagai benchmark eksperimen, bukan untuk deployment langsung
+## 📚 Sitasi
+Jika Anda menggunakan dataset ini, mohon sitasi dataset asli NASA serta submisi kami jika sesuai:
+@inproceedings{saxena2008damage,
+title={Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation},
+author={Saxena, A. and Goebel, K. and Simon, D. and Eklund, N.},
+booktitle={Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08)},
+year={2008},
+address={Denver, CO}
+}

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readme.txt ADDED Viewed

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+Data Set: FD001
+Train trjectories: 100
+Test trajectories: 100
+Conditions: ONE (Sea Level)
+Fault Modes: ONE (HPC Degradation)
+Data Set: FD002
+Train trjectories: 260
+Test trajectories: 259
+Conditions: SIX
+Fault Modes: ONE (HPC Degradation)
+Data Set: FD003
+Train trjectories: 100
+Test trajectories: 100
+Conditions: ONE (Sea Level)
+Fault Modes: TWO (HPC Degradation, Fan Degradation)
+Data Set: FD004
+Train trjectories: 248
+Test trajectories: 249
+Conditions: SIX
+Fault Modes: TWO (HPC Degradation, Fan Degradation)
+Experimental Scenario
+Data sets consists of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different engine � i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data is contaminated with sensor noise.
+The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. The objective of the competition is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. Also provided a vector of true Remaining Useful Life (RUL) values for the test data.
+The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle, each column is a different variable. The columns correspond to:
+1)	unit number
+2)	time, in cycles
+3)	operational setting 1
+4)	operational setting 2
+5)	operational setting 3
+6)	sensor measurement  1
+7)	sensor measurement  2
+...
+26)	sensor measurement  26
+Reference: A. Saxena, K. Goebel, D. Simon, and N. Eklund, �Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation�, in the Proceedings of the Ist International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.

synthetic_business_data_v2.csv ADDED Viewed

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+engine_id,is_critical_route,next_service_in_cycles,cost_of_failure_usd,proactive_repair_cost_usd,part_lead_time_days,standard_repair_time_days
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+8,False,34,2973548,106250,8,5
+9,False,145,4870442,122082,39,3
+10,False,8,3213475,84754,21,4
+11,True,117,6032850,145742,32,6
+12,False,183,2999238,61411,34,2
+13,False,5,4959034,52911,33,6
+14,True,154,5892651,117270,10,5
+15,True,124,3569983,133763,39,6
+16,True,87,6769000,58680,41,2
+17,False,35,2043585,134896,28,5
+18,False,67,3419945,133879,33,6
+19,False,65,4161196,121295,35,5
+20,True,16,6809172,138614,39,3
+21,False,52,2109556,61111,37,3
+22,True,161,5363335,87504,25,6
+23,True,70,6091624,51802,36,5
+24,False,14,4864747,58155,27,2
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synthetic_orders_data_v2.csv ADDED Viewed

	@@ -0,0 +1,84 @@

+order_id,client_name,order_value_usd,due_in_cycles,penalty_if_late_usd
+ORD-2824,Globex Inc.,900573,81,108068
+ORD-1409,ACME Corp,1715856,52,137268
+ORD-5506,ACME Corp,1137144,32,90971
+ORD-5012,Stark Industries,1398320,69,209748
+ORD-4657,ACME Corp,1468021,42,117441
+ORD-3286,ACME Corp,678352,33,54268
+ORD-2679,Stark Industries,966152,68,144922
+ORD-9935,Stark Industries,1607739,63,241160
+ORD-2424,Cyberdyne Systems,925523,48,92552
+ORD-7912,Globex Inc.,1687983,54,202557
+ORD-1520,Stark Industries,720184,70,108027
+ORD-1488,ACME Corp,822710,45,65816
+ORD-2535,ACME Corp,2156940,50,172555
+ORD-4582,Globex Inc.,2346182,63,281541
+ORD-4811,Globex Inc.,1033636,84,124036
+ORD-9279,ACME Corp,1913354,40,153068
+ORD-1434,Stark Industries,2276203,58,341430
+ORD-4257,ACME Corp,731915,46,58553
+ORD-9928,Stark Industries,1025830,62,153874
+ORD-7873,Stark Industries,1218216,76,182732
+ORD-4611,Cyberdyne Systems,1763108,58,176310
+ORD-8359,Stark Industries,1837682,57,275652
+ORD-5557,Globex Inc.,657504,71,78900
+ORD-1106,ACME Corp,1192440,42,95395
+ORD-3615,ACME Corp,1715557,57,137244
+ORD-7924,Stark Industries,1751772,88,262765
+ORD-6574,Globex Inc.,948345,68,113801
+ORD-5552,Stark Industries,2379490,62,356923
+ORD-3547,Globex Inc.,1290141,69,154816
+ORD-4527,Stark Industries,1954908,88,293236
+ORD-6514,Cyberdyne Systems,2050284,57,205028
+ORD-2674,Wayne Enterprises,2041310,31,102065
+ORD-2519,Stark Industries,713920,87,107088
+ORD-7224,Globex Inc.,2009433,81,241131
+ORD-2584,Globex Inc.,2307150,70,276858
+ORD-6881,Stark Industries,1904116,60,285617
+ORD-6635,Wayne Enterprises,759747,17,37987
+ORD-5333,Cyberdyne Systems,1527512,47,152751
+ORD-1711,Stark Industries,552528,63,82879
+ORD-8527,Stark Industries,1470240,52,220536
+ORD-9785,Wayne Enterprises,655576,25,32778
+ORD-3045,Stark Industries,1428479,65,214271
+ORD-7201,ACME Corp,888043,53,71043
+ORD-2291,Cyberdyne Systems,660775,55,66077
+ORD-5803,Stark Industries,1872249,57,280837
+ORD-6925,Globex Inc.,1187281,77,142473
+ORD-4150,Stark Industries,1675592,85,251338
+ORD-2139,Wayne Enterprises,2251541,31,112577
+ORD-1750,Stark Industries,817428,69,122614
+ORD-4733,Cyberdyne Systems,2432015,58,243201
+ORD-5741,Stark Industries,622402,55,93360
+ORD-2307,Wayne Enterprises,1073695,19,53684
+ORD-4814,Cyberdyne Systems,1678049,39,167804
+ORD-2654,Wayne Enterprises,1965585,27,98279
+ORD-7227,Stark Industries,1025303,82,153795
+ORD-5554,ACME Corp,2173361,44,173868
+ORD-8428,Stark Industries,2135156,75,320273
+ORD-6977,Globex Inc.,1306741,76,156808
+ORD-3664,Globex Inc.,651456,83,78174
+ORD-7065,Stark Industries,770846,50,115626
+ORD-6820,Stark Industries,1203286,55,180492
+ORD-4432,Cyberdyne Systems,1215460,34,121546
+ORD-5374,Wayne Enterprises,1599188,37,79959
+ORD-2169,ACME Corp,2345924,39,187673
+ORD-3803,ACME Corp,1693104,50,135448
+ORD-9751,Globex Inc.,1587341,50,190480
+ORD-5010,Wayne Enterprises,1434604,18,71730
+ORD-3677,Stark Industries,1848464,89,277269
+ORD-8573,Cyberdyne Systems,1584763,37,158476
+ORD-7216,Stark Industries,1008153,83,151222
+ORD-5422,Globex Inc.,1860642,61,223277
+ORD-4598,ACME Corp,1360395,34,108831
+ORD-6313,Stark Industries,1848346,66,277251
+ORD-1916,Stark Industries,2485838,72,372875
+ORD-4752,ACME Corp,1326196,43,106095
+ORD-1525,Cyberdyne Systems,815826,36,81582
+ORD-6168,Stark Industries,2151451,83,322717
+ORD-7572,Globex Inc.,817537,67,98104
+ORD-5386,Globex Inc.,2489466,83,298735
+ORD-2084,Globex Inc.,1864133,82,223695
+ORD-4456,ACME Corp,750875,59,60070
+ORD-6155,Cyberdyne Systems,753490,46,75349
+ORD-4483,Wayne Enterprises,933027,24,46651

test_FD001.csv ADDED Viewed