机电工程技术2026，Vol.55Issue(11)：40-45,6.DOI:10.3969/j.issn.1009-9492.2026.11.007
基于随机森林和长短期记忆网络的高速剑杆织机故障诊断技术
Fault Diagnosis Technology of High-speed Rapier Looms Based on Random Forest and Long Short-term Memory Network
摘要
Abstract
High-speed rapier looms often encounter faults such as abnormal tension and high motor operation temperatures,hindering their efficient and stable operation.Benefiting from the progress of data-driven methods,the accuracy of equipment fault diagnosis is improved.However,due to the temporal nature of heterogeneous data from multiple sources,traditional fault diagnosis methods fail to provide reliable predictive performance.Therefore,a fault diagnosis method based on random forest(RF)and long short-term memory(LSTM)networks is proposed in this study,and then applied to the high-speed rapier loom.First,an STM32-based core controller is employed to capture data from various types of sensors,which are then transmitted via a serial port to a diagnostic module implemented on a Raspberry Pi.Notably,both the RF and LSTM models are deployed on the diagnostic module,where the RF model is used to determine the type of equipment failure and the LSTM model is used to estimate the remaining useful life.Practical results confirm that the RF model achieves a 96.4%fault diagnosis accuracy,while the LSTM model achieves a life prediction accuracy with R2=0.947,demonstrating excellent robustness and generalization ability.关键词
高速剑杆织机/故障诊断/随机森林/长短期记忆网络/剩余运行寿命Key words
high-speed rapier loom/fault diagnosis/random forest/long short-term memory/remaining useful life分类
轻工纺织引用本文复制引用
贺诗羽,韩哲哲,钱泽文,余璨辰,吴海丰,汪木兰..基于随机森林和长短期记忆网络的高速剑杆织机故障诊断技术[J].机电工程技术,2026,55(11):40-45,6.基金项目
江苏省高校哲学社会科学研究一般项目(2023SJYB0432) （2023SJYB0432）
南京工程学院高等教育研究课题(2025GJZC20) （2025GJZC20）
