Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. Note to Practitioners - This paper proposes a fast, robust classification method for online sensor data classification. The proposed method is designed to cope with high levels of sensor outliers. The robustness of the method and its computational efficiency make it particularly appealing for online sensor data classification in challenging field conditions in which the presence of sensor outliers causes practical difficulties for most existing classification algorithms.

中文翻译：

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使用在线传感器数据进行基于鲁棒稀疏表示的分类来监控手动物料搬运任务


基于传感器的在线过程监控具有广泛的应用，例如在制造和服务行业。然而，在现实环境中，传感器数据经常受到噪声污染，这给准确的数据分析带来了严峻的挑战。在现有文献中，噪声通常被建模为高斯分布，以分析各种应用的传感器数据，例如故障检测和诊断。然而，在某些应用中，例如由于具有挑战性的现场条件，传感器数据可能会受到高水平异常值的干扰，使得传感器噪声的高斯假设不充分，从而导致较大的估计误差。本文重点关注在线分类应用。提出了一种鲁棒的稀疏表示分类方法，该方法考虑了非高斯噪声，因此可以有效地分析具有较高异常值水平的传感器数据。基于数值模拟传感器数据和来自职业手动物料搬运过程监控的实际可穿戴传感器数据，完成了案例研究。所提出的分类方法可以有效地分析具有非高斯噪声的传感器数据，并且优于文献中常用的方法。因此，这种新方法可能有利于解决具有挑战性的现场条件下的分类问题，以解决高水平传感器异常值的困难。从业者注意事项 - 本文提出了一种快速、稳健的在线传感器数据分类方法。所提出的方法旨在应对高水平的传感器异常值。 该方法的鲁棒性及其计算效率使其对于具有挑战性的现场条件下的在线传感器数据分类特别有吸引力，在这种条件下，传感器异常值的存在会给大多数现有分类算法带来实际困难。