Diesel Engine Faults Features Dataset

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.

本数据集旨在开展柴油机故障诊断，以辅助预测性维护，具体通过分析气缸内压力曲线变化与曲轴扭转振动响应实现。为此，研究人员构建了基于零维热力学模型（zero-dimensional thermodynamic model）的故障仿真模型。所选取的特征向量（feature vectors）源自该热力学模型，通过处理气缸内压力、温度以及发动机飞轮的扭转振动信号得到。这些向量被用作机器学习技术（machine learning technique）的输入，以区分多种柴油机运行状态。本数据库旨在复现本次研究覆盖的全部运行场景，就本研究而言，数据库涵盖所有可能的柴油机故障与系统状态变化，这些场景对应着具备足够信息以表征并区分各类故障的严重程度等级。本次构建的数据库覆盖以下运行工况：正常无故障工况、进气歧管压力降低工况、气缸压缩比降低工况，以及气缸喷油量降低工况。所有场景下，发动机转速均设定为2500转每分钟（RPM）。选择2500转每分钟作为设定转速，是因为在热力学与动力学模型的验证阶段，对比制造商提供的实验数据与仿真数据时，该转速下燃烧循环平均压力与最大压力的估算联合误差率最低。整个数据库共包含4种不同运行工况下的3500组故障场景：正常工况类250组、"进气歧管压力降低"工况类250组、"气缸压缩比降低"工况类1500组，以及"气缸喷油量降低"工况类1500组。该数据库被命名为3500-DEFault数据库。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于柴油发动机故障诊断和预测性维护的机器学习数据集，通过分析气缸内压力、温度及飞轮扭转振动信号提取特征，模拟正常和三种常见故障状态（进气歧管压力降低、气缸压缩比降低、燃油喷射量减少），共包含3500个不同故障场景，所有数据在2500 RPM转速下生成，旨在支持机器学习模型区分发动机工作条件。