EIGeS-Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal.
CISE-Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal.
Sensors (Basel). 2023 Feb 21;23(5):2402. doi: 10.3390/s23052402.
Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.
基于传感器的状态监测维修（CBM）只有在用于提取信息的数据可靠的情况下才能可靠。工业计量学在确保传感器所采集数据的质量方面起着重要作用。为了保证传感器所采集的值可靠，有必要通过从更高标准到工厂中使用的传感器的连续校准来保证计量溯源性。为了保证数据的可靠性，必须制定校准策略。通常，传感器仅按定期进行校准；因此，在没有必要的情况下，它们经常会进行校准，或者不准确地收集数据。此外，由于经常检查传感器，增加了对人力的需求，并且当冗余传感器具有相同方向的漂移时，传感器错误经常被忽略。有必要根据传感器的状况获取校准策略。通过在线监测传感器的校准状态（OLM），只有在真正需要时才进行校准。为了达到这个目的，本文旨在提供一种策略，对生产设备和使用相同数据集的读数设备的健康状况进行分类。模拟了来自四个传感器的测量信号，使用了人工智能和无监督算法的机器学习。本文演示了如何通过相同的数据集获得不同的信息。由于这个原因，我们有一个非常重要的特征创建过程，然后是主成分分析（PCA）、K-均值聚类和基于隐马尔可夫模型（HMM）的分类。通过 HMM 的三个隐藏状态，代表生产设备的健康状态，我们将首先通过相关性检测其状态的特征。然后，使用 HMM 滤波器从原始信号中消除这些错误。接下来，对每个传感器单独使用相同的方法，并在时域中使用统计特征，我们可以通过 HMM 获得每个传感器的故障。
