Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
针对民机机械部件故障样本缺乏且类不平衡以及故障信号复杂多样导致的故障诊断精度低,识别不稳定的问题,提出基于增强元学习与通道注意力机制(learn to reweight with SE-1DleNet,LRS)的故障诊断方法。利用小样本平衡验证集指导了不平...针对民机机械部件故障样本缺乏且类不平衡以及故障信号复杂多样导致的故障诊断精度低,识别不稳定的问题,提出基于增强元学习与通道注意力机制(learn to reweight with SE-1DleNet,LRS)的故障诊断方法。利用小样本平衡验证集指导了不平衡训练集的损失权重更新以改善原始不均衡样本分布,提出元梯度增强的梯度裁剪策略;在1D-LeNet的基础上引入SE注意力机制对多维度故障特征通道自适应加权。结果表明:以民机大梁和机械轴承故障作为仿真试验数据集,与当前主流的故障诊断算法ProtoNet、DNCNN、GAN-CNN等相比,该方法诊断效果最优,在样本极端不平衡时准确率达95%以上,能够进行准确故障诊断。展开更多
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
文摘针对民机机械部件故障样本缺乏且类不平衡以及故障信号复杂多样导致的故障诊断精度低,识别不稳定的问题,提出基于增强元学习与通道注意力机制(learn to reweight with SE-1DleNet,LRS)的故障诊断方法。利用小样本平衡验证集指导了不平衡训练集的损失权重更新以改善原始不均衡样本分布,提出元梯度增强的梯度裁剪策略;在1D-LeNet的基础上引入SE注意力机制对多维度故障特征通道自适应加权。结果表明:以民机大梁和机械轴承故障作为仿真试验数据集,与当前主流的故障诊断算法ProtoNet、DNCNN、GAN-CNN等相比,该方法诊断效果最优,在样本极端不平衡时准确率达95%以上,能够进行准确故障诊断。
