Diesel Engine Faults Features Dataset (3500-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.

本数据集旨在通过分析气缸内压力曲线变化与曲轴扭转振动响应，实现柴油机故障诊断，辅助预测性维护。据此，本研究开发了基于零维热力学模型（zero-dimensional thermodynamic model）的故障仿真模型。所采用的特征向量选自该热力学模型，通过处理气缸内压力、温度以及发动机飞轮扭转振动等信号得到。这些向量被用作机器学习技术的输入，以区分多种柴油机运行工况。该数据库旨在复现研究范围内的所有运行场景，涵盖所有可能的柴油机故障与系统工况变化，对应包含足够信息以表征和区分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：正常工况（无故障）、进气歧管压力降低、气缸压缩比降低以及气缸喷油量减少。所有场景下，发动机转速均设置为2500转每分钟。选择该转速的原因在于，在热力学与动力学模型的验证阶段，相较于制造商提供的实验数据，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组不同故障场景：正常工况类250组、"进气歧管压力降低"类250组、"气缸压缩比降低"类1500组，以及"气缸喷油量减少"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的模拟数据集，旨在辅助预测性维护。数据集包含3500个样本，覆盖四种操作条件：正常、进气歧管压力降低、气缸压缩比降低和燃油喷射量减少，所有场景均在2500 RPM转速下模拟。特征向量基于热力学模型和信号处理提取，维度为84，并提供了不同白噪声水平的数据文件，适用于机器学习分类任务。