基于数据包络分析的故障检测系统传感器选择新框架
A new framework of sensor selection for developing a fault detection system based on data-envelopment analysis
AI总结：
本文针对含噪声测量数据下机械故障检测的传感器选择问题，提出四个基于数据包络分析的线性优化模型，并在齿轮箱振动数据集上验证其故障检测精度优于现有方法。
摘要：
已有多种方法被提出，用于识别在不同工况下对机械设备进行故障发现与定位时最优的传感器集合。为了在保持可接受性能的同时最小化成本，识别可用的最有效传感器集合至关重要。然而，由于受噪声干扰污染的测量数据存在不确定性，某些传感器集合在故障检测中的表现可能不同。本文利用数据包络分析（DEA）开发了新的传感器选择模型，DEA已被证明是一种有效的管理方法，可用于确定故障诊断中最优的传感器数量、相关成本及传感器性能。我们提出了四个用于传感器选择的线性优化模型，以设计故障检测系统。利用从齿轮箱采集的振动数据集验证了所提模型的有效性。基于案例研究结果，所提方法在故障检测精度上显著优于其他对比传感器选择方法。
英文摘要：
Several methods have been proposed to identify which sensor sets are optimal for finding and localizing faults under different conditions for mechanical equipment. In order to preserve acceptable performance while minimizing costs, it is crucial to identify the most effective set of sensors available. Nevertheless, some sensor sets can behave differently in fault detection because of uncertainty in the measurement data contaminated by noise interference. This paper develops new sensor selection models using Data Envelopment Analysis (DEA), which has proven helpful as a management approach for determining an optimal number of sensors, associated costs, and sensor performance in the fault diagnosis. We propose four linear optimization models for sensor selection to design the fault detection system. The validity of the presented models is demonstrated using a vibration dataset collected from a gearbox. Based on the case study results, the proposed methods are effectively superior to the other comparison sensor selection methods in fault detection accuracy.
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