Abstract: The advent of Industry 4.0 introduced new methods for maintenance that enable faster and reliable use of data for classification. The so-called “Convolutional Neural network” can be applied for classification and regression. Concerning classification, the use of images can be applied to train and test a model to classify similar images such as equipment failure during production, wrong assembly, equipment failure or degradation during operation or even detect critical degradation pattern based on Remaining Useful Life (RUL) and/or State of Health (SoH) graphs. In order to apply the CNN methodology, is necessary to collect a set of pictures that aims to classify in the future application based on CNN structures. The “Convolutional Neural Network (CNN)” is like neural network in principle but differs in concept. Since the CNN aims to classify images, the structure of the CNN is quite complex with different types of layer as shows the figure below. The elements of CNN are the following:

• Input Image
• Convolution Layer
• Pooling Layer
• ReLu
• Fully Connected
• Softmax
• Output Classification

Abstract: 

The K-Means clustering method is an Unsupervised Machine Learning method for data clustering. The K-Means objective is to organize a set of data point in k different clusters considering the k different centroids and group the data closest to each k centroids. In order to practice the K-Means concepts, we can group the equipment of a different classes in different class based on the criticality or even define the time to perform preventive interventions based on Prognostic Health Management prediction from a group of equipment with different Remaining useful life.

Concerning the PHM, the K means cluster can be a very good solution for this type of problem, where a group of equipment need to be planned for preventive intervention based on the result of the RUL prediction as a result of PHM assessment in several equipment.