In the pharmaceutical industry, the maintenance of production machines must be audited by the regulator. In this context, the problem of predictive maintenance is not when to maintain a machine, but what parts to maintain at a given point in time. The focus shifts from the entire machine to its component parts and prediction becomes a classification problem. In this paper, we focus on rolling-elements bearings and we propose a framework for predicting their degradation stages automatically. Our main contribution is a k-means bearing lifetime segmentation method based on high-frequency bearing vibration signal embedded in a latent low-dimensional subspace using an AutoEncoder. Given high-frequency vibration data, our framework generates a labeled dataset that is used to train a supervised model for bearing degradation stage detection. Our experimental results, based on the FEMTO Bearing dataset, show that our framework is scalable and that it provides reliable and actionable predictions for a range of different bearings.

本文提出了一种基于高频轴承振动信号的k均值轴承寿命分段方法，并使用自编码器将其嵌入潜在低维子空间，生成一个用于训练监督模型的带标签数据集，用于预测轴承的降级阶段，实验结果表明该框架具有可扩展性和可靠性。

制药行业预测性维护中轴承磨损阶段预测