Diesel Engine Faults Features Dataset (DEFault)

The objective of this dataset is the fault diagnosis in diesel engines to assist the predictive maintenance, through the analysis of the variation of the pressure curves inside the cylinders and the torsional vibration response of the crankshaft. Hence a fault simulation model based on a zero-dimensional thermodynamic model was developed. The adopted feature vectors were chosen from the thermodynamic model and obtained from processing signals as pressure and temperature inside the cylinder, as well as, torsional vibration of the engine’s flywheel. These vectors are used as input of the machine learning technique in order to discriminate among several machine conditions. The database is expected to emulate all operating scenarios under study. In our case, all possible diesel machine faults and system conditions variations, which correspond to severities levels containing enough information to characterize and discriminate the faults. The developed database covered the following operating conditions: Normal (without faults), Pressure reduction in the intake manifold, Compression ratio reduction in the cylinders and Reduction of amount of fuel injected into the cylinders. In all scenarios, the motor rotation frequency was set at 2500 RPM. The rotation of 2500 RPM was used, since it presented the lowest joint error rate in the estimation of the mean and maximum pressures of the burning cycle, between the experimental data (according to data supplied by the manufacturer) and the simulated data, during the validation stage of the thermodynamic and dynamic models. The entire database comprises a total of 3500 different fault scenarios for 4 distinct operational conditions. 250 of which from the normal class, 250 from ``pressure reduction in the intake manifold" class, 1500 from ``compression ratio reduction in the cylinders" class and 1500 from the ``reduction of amount of fuel injected into the cylinders" class. This database is named 3500-DEFault database.

本数据集旨在通过对气缸内部压力曲线及其曲轴扭转振动响应的分析，实现柴油发动机故障诊断，以辅助预测性维护。为此，基于零维热力模型构建了故障模拟模型。所选特征向量源自热力模型，并通过对气缸内部的压力和温度以及发动机飞轮的扭转振动信号进行处理而获得。这些向量被用作机器学习技术的输入，以区分多种机器状态。数据库旨在模拟所有研究中的运行场景。在本例中，涵盖了所有可能的柴油发动机故障和系统条件变化，这些变化对应于包含足够信息以表征和区分故障的严重程度等级。构建的数据库涵盖了以下运行条件：正常（无故障）、进气歧管压力降低、气缸压缩比降低以及气缸注入燃料量减少。在所有场景中，设定电机转速为2500 RPM。采用2500 RPM的原因在于，在热力模型和动力学模型验证阶段，该转速在实验数据（根据制造商提供的数据）与模拟数据之间，对燃烧循环的平均压力和最大压力的估计中表现出最低的联合误差率。整个数据库包含3500种不同的故障场景，对应于4种不同的操作条件。其中，正常类250种，进气歧管压力降低类250种，气缸压缩比降低类1500种，以及气缸注入燃料量减少类1500种。该数据库命名为3500-DEFault数据库。