This paper presents a modification of the data-driven sensor-based fault detection and diagnosis (SFDD) algorithm for online robot monitoring. Our version of the algorithm uses a collection of generative models, in particular restricted Boltzmann machines, each of which represents the distribution of sliding window correlations between a pair of correlated measurements. We use such models in a residual generation scheme, where high residuals generate conflict sets that are then used in a subsequent diagnosis step. As a proof of concept, the framework is evaluated on a mobile logistics robot for the problem of recognising disconnected wheels, such that the evaluation demonstrates the feasibility of the framework (on the faulty data set, the models obtained 88.6% precision and 75.6% recall rates), but also shows that the monitoring results are influenced by the choice of distribution model and the model parameters as a whole.

翻译：本文提出了一种数据驱动的基于传感器的故障检测与诊断（SFDD）算法的改进版本，用于在线机器人监测。本算法采用一组生成模型，特别是受限玻尔兹曼机，每个模型表示一对相关测量值之间滑动窗口相关性的分布。我们将此类模型应用于残差生成方案中，其中高残差生成冲突集，随后用于诊断步骤。作为概念验证，该框架在一款移动物流机器人上进行了评估，以识别断连车轮问题。评估结果不仅证明了该框架的可行性（在故障数据集上，模型达到了88.6%的精确率和75.6%的召回率），同时还表明监测结果受分布模型选择及整体模型参数的影响。