组合收割机的结构故障检测和诊断:一个批判性审查
Haiyang Wang1, Liyun Lao2, Honglei Zhang1
1College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
机床准确的故障检测和诊断 (FDD) 对农业生产率至关重要. 本综述分析了数据驱动的FDD方法,包括传感器数据,信号处理和用于结构故障识别的AI模型.
科学领域:
- 农业工程 农业工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 机组收割机由于复杂的设计和恶劣的操作条件,经常面临结构缺陷.
- 这些故障降低了运营效率,导致停机时间,影响农业生产率,威胁到粮食安全.
研究的目的:
- 系统地审查和分析最近的数据驱动故障检测和诊断 (FDD) 方法的进展,以对结合机的结构故障进行分析.
- 为实施这些FDD方法提供见解,并确定当前的挑战和未来的研究方向.
主要方法:
- 审查数据驱动的FDD技术,包括传感器数据采集 (振动,声学,应变),信号预处理和特征提取 (时间,频率,时间频率域,模式分析).
- 分析机器学习和人工智能模型用于故障模式识别和诊断.
- 探索系统和技术支持,如车载诊断,远程监控和模拟建模.
主要成果:
- 鉴定机组收割机常见的结构缺陷及其典型位置.
- 详细概述数据驱动的FDD过程,从数据采集到诊断.
- 讨论FDD实际实施的系统要求和技术支持.
结论:
- 数据驱动的FDD方法在提高机可靠性和运营效率方面显示出显著的前景.
- 主要挑战包括数据采集困难,信号复杂性和模型稳定性,需要进一步研究.
- 未来的方向旨在开发农业机械的智能维护策略.
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