In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system performance or collect usage statistics of individual machines. In this work, we focus on estimating the power output of a Combined Heat and Power (CHP) machine of a medium-sized company facility by analyzing the total facility power consumption. We formulate the problem as a time-series classification problem where the class label represents the CHP power output. As the facility is fully instrumented and sensor measurements from the CHP are available, we generate the training labels in an automated fashion from the CHP sensor readings. However, sensor failures result in mislabeled training data samples which are hard to detect and remove from the dataset. Therefore, we propose a novel multi-task deep learning approach that jointly trains a classifier and an autoencoder with a shared embedding representation. The proposed approach targets to gradually correct the mislabelled data samples during training in a self-supervised fashion, without any prior assumption on the amount of label noise. We benchmark our approach on several time-series classification datasets and find it to be comparable and sometimes better than state-of-the-art methods. On the real-world use-case of predicting the CHP power output, we thoroughly evaluate the architectural design choices and show that the final architecture considerably increases the robustness of the learning process and consistently beats other recent state-of-the-art algorithms in the presence of unstructured as well as structured label noise.

中文翻译：

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在存在标签噪声的情况下，通过端到端时间序列分类来估算工业设备的电力输出

在复杂的工业环境中，通常的做法是监视机器的运行情况，以检测不良状态，调整维护计划，优化系统性能或收集单个机器的使用情况统计信息。在这项工作中，我们专注于通过分析设施的总功耗来估算中型公司设施的热电联产（CHP）机器的功率输出。我们将该问题表述为时间序列分类问题，其中类别标签表示CHP功率输出。由于该设施已完全配备了仪器，并且可以使用CHP的传感器进行测量，因此我们会根据CHP传感器的读数以自动化的方式生成培训标签。但是，传感器故障会导致标记错误的训练数据样本，这些样本很难检测到并从数据集中删除。所以，我们提出了一种新颖的多任务深度学习方法，该方法可以共同训练具有共享嵌入表示的分类器和自动编码器。所提出的方法的目标是在训练过程中以自我监督的方式逐渐纠正标签错误的数据样本，而无需事先对标签噪声量进行任何假设。我们在几个时间序列分类数据集上对我们的方法进行了基准测试，发现它具有可比性，有时甚至比最新方法更好。在预测CHP功率输出的实际用例中，我们彻底评估了架构设计的选择，并表明最终的架构大大提高了学习过程的鲁棒性，并且在性能上一直领先于其他最新技术。非结构化以及结构化标签噪声的存在。