Datasets:

premswan
/

engine-predictive-maintenance-data

Engine_RPM int64	Lub_Oil_Pressure float64	Fuel_Pressure float64	Coolant_Pressure float64	Lub_Oil_Temperature float64	Coolant_Temperature float64	Engine_Condition int64
496	4.70624	5.249898	3.803385	75.033607	70.542095	0
836	3.960027	4.928546	3.705357	74.395016	84.098307	1
623	3.055244	9.494612	2.084725	76.865701	72.927688	0
675	2.785797	5.463947	1.649817	75.397279	82.907453	1
1,412	2.816022	4.00141	1.921522	83.246013	71.176927	1
414	3.928896	4.468993	1.740373	74.461489	80.171776	1
1,041	2.618464	6.031472	3.444127	75.87409	74.213489	1
975	5.964635	4.00145	2.460979	80.727668	80.231235	0
1,158	2.653552	4.956097	1.126615	76.703935	82.821523	0
1,357	4.045931	7.2471	1.827783	78.382214	87.120363	1
767	3.675062	6.603299	2.240416	77.226557	71.871107	0
1,179	3.308127	6.162965	0.736781	75.678397	71.430854	0
956	3.720926	5.050159	4.541596	78.294517	75.350544	1
1,057	2.23783	5.631612	2.505175	74.529496	76.760059	0
854	5.325312	3.992183	5.119024	81.89433	82.209094	0
855	3.087465	5.170975	2.49316	86.235597	81.372706	0
1,096	2.480399	5.149975	0.635378	84.090834	78.015959	1
434	4.442973	7.090542	1.524269	75.620907	83.188313	1
822	3.310684	5.627634	2.058694	77.853008	78.117307	1
569	3.736063	5.729134	1.870194	76.742341	79.451863	1
952	2.40655	7.418635	1.44315	75.180661	78.452812	1
973	2.349488	5.715477	3.340146	78.821659	77.506734	1
554	3.396276	10.290297	1.999733	76.546559	83.106267	1
811	4.851543	3.622094	0.758107	76.042221	82.374601	1
540	3.081387	4.375281	0.982797	76.415207	79.320499	1
778	3.538907	5.208443	1.734624	73.849828	73.385308	1
563	2.217112	6.034855	2.623956	78.603892	77.731044	1
1,033	2.036132	4.932128	2.235217	75.931896	69.710272	1
804	4.57266	5.992453	1.535159	77.051917	79.80514	1
369	2.746007	6.989355	1.830474	77.18143	75.697478	1
798	5.789154	6.727879	1.819378	77.601968	79.906577	1
437	3.900054	6.734776	2.790181	78.008376	78.002771	0
1,221	4.42086	5.888119	5.204647	76.787069	90.006726	1
946	2.33791	5.878699	6.107326	78.055623	81.372531	0
654	2.126811	6.048617	2.932469	75.452488	85.561782	0
800	3.40304	5.070474	2.423549	75.857648	75.29601	1
1,388	2.991451	4.277201	1.463619	77.886941	82.120037	1
576	4.796219	8.31517	3.235007	83.490921	90.11711	1
1,307	3.771457	2.955204	1.844966	77.864105	81.623514	0
663	4.798853	7.292509	2.363309	75.429563	70.386828	1
582	1.305755	5.211494	2.633097	75.93492	75.580952	1
1,145	4.436745	5.686701	2.965899	74.062249	71.002669	0
673	1.826226	8.79934	1.239882	76.365291	83.423228	0
469	2.244983	7.020501	0.246693	76.915875	86.219173	1
739	2.464748	8.192446	2.752652	74.697141	87.950844	0
1,240	4.557669	4.769486	1.481279	77.441172	75.294501	1
529	2.591552	7.383817	2.334104	76.628042	76.083761	1
835	3.516408	3.704002	1.359745	89.275897	73.337822	1
848	2.474463	5.649183	3.259924	76.354736	77.660874	0
754	2.931881	5.210595	1.070077	78.219563	81.21399	1
510	4.64128	3.945978	1.855171	78.017094	74.074583	0
450	0.699888	4.111952	0.949993	74.86905	72.889809	1
658	3.201742	6.108218	1.567632	75.665033	69.755396	1
529	3.215984	5.469481	2.196793	78.294028	75.39119	1
1,799	2.776676	7.228798	1.834901	87.578571	78.951378	1
84	3.830491	4.907142	1.061624	75.626682	77.407978	1
798	1.988798	2.982692	1.362544	77.303689	76.034316	0
1,083	4.86708	4.984865	2.4964	81.90805	81.064302	0
633	2.211679	7.715703	2.24428	76.325323	81.733805	0
918	2.981941	11.245539	2.724376	76.193842	92.014862	1
668	2.54182	5.14548	3.114428	75.49389	70.381397	1
488	0.325005	8.190476	1.503244	76.150753	77.308195	1
813	4.485257	8.605066	1.668094	76.21798	77.237554	1
413	3.492244	6.194312	1.769396	82.126134	78.464299	1
1,185	3.76483	10.673992	2.249566	78.487524	75.174314	0
1,057	2.054571	6.884652	1.598794	76.617464	87.627446	1
643	2.625858	8.451585	2.90954	83.808091	68.859504	0
659	4.495829	5.978984	5.866578	76.959741	83.497599	1
429	3.378734	3.557734	2.692528	77.956845	93.980689	0
1,087	4.294721	3.272855	2.08666	85.178231	75.427291	0
671	4.183711	7.136869	2.776894	77.187541	79.178336	1
743	2.983045	11.830769	1.705551	76.268239	85.180514	1
744	2.740729	4.944986	1.294337	77.417051	90.163324	1
809	4.050245	7.785442	2.343225	77.791209	75.283887	1
902	4.200947	14.528383	2.169704	76.169902	78.116536	0
555	2.756869	2.941945	2.084822	78.193826	83.436028	1
706	3.322614	6.614217	2.1754	75.46537	69.857252	1
634	4.144614	8.87925	1.95402	74.04727	80.937101	1
665	3.020798	7.931052	2.793915	76.949302	76.395635	1
913	2.577537	5.168561	2.085453	83.063455	86.055655	0
1,212	1.723152	5.266437	4.289763	80.382307	82.462033	0
1,300	2.21935	8.433284	1.580678	80.84323	74.473407	0
621	3.251042	5.331136	2.615028	78.185455	71.337673	0
808	3.077382	3.582213	5.852737	83.184829	74.732234	1
1,258	3.265263	11.247399	1.487561	76.842186	71.705021	1
1,267	4.119743	8.259543	2.09562	74.636635	76.781918	0
868	2.525346	4.285353	1.596997	76.14099	82.910354	1
1,024	2.194611	4.866378	0.512182	77.774181	83.93496	0
1,036	2.161426	6.125224	2.157851	75.996252	77.318644	0
656	4.151634	6.293229	3.177781	75.572258	68.317647	1
823	2.377631	2.604273	1.97795	73.113172	76.898773	1
524	1.441212	6.81795	1.786358	77.267706	73.978998	0
699	2.106315	2.486776	1.0972	86.787515	90.570099	0
981	3.235836	7.454698	4.277397	73.299392	80.962784	1
833	3.758835	6.281994	1.660214	76.560451	70.938112	0
919	2.726379	14.715238	2.298589	75.610175	87.730004	1
801	3.707931	8.233514	2.483217	75.775911	81.361103	1
696	4.103563	6.618358	1.740116	77.589843	67.575875	1
1,266	5.377554	5.537528	1.029143	77.857636	85.885943	1
1,409	2.781202	7.002047	2.945533	84.636721	81.159766	1

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Engine Predictive Maintenance Dataset

This dataset contains engine sensor readings used for predictive maintenance classification.

Objective

Predict whether an engine is operating normally or requires maintenance using sensor parameters.

Columns

Engine rpm
Lub oil pressure
Fuel pressure
Coolant pressure
lub oil temp
Coolant temp
Engine Condition

Label Assumption

Engine Condition = 1 is treated as the maintenance/faulty class and Engine Condition = 0 is treated as the normal/healthy class.

Files

'engine_data.csv': Raw dataset
'train.csv': Cleaned training split
'test.csv': Cleaned testing split

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