Assets Predictive Maintenance Using Convolutional Neural Networks

Predictive Maintenance (PdM) performs maintenance based on the asset's health status indicators. Sensors can measure an unusual pattern of these indicators, such as an increased motor's vibration level or higher energy consumption, and, in most cases, failures are preceded by an unusual pattern of these measurements. Convolutional Neural Network (CNN) is a Machine Learning technique capable of extracting data representation. This paper presents a CNN framework to tackle assets predictive maintenance problem and a method to transform 1-dimensional (1-D) data into an image-like representation (2-D). A data transformation step is very important to make the use of CNN feasible. To evaluate the proposed framework two datasets were obtained from fans, with distinct electrical pattern, from a building at Western University. The data was preprocessed, transformed in a image-like representation and fed to a tuned classifier. The results presented by the CNN-PdM framework showed that the combination of CNN with the proposed data transformation method outperformed traditional machine learning techniques (Random Forest, Support Vector Machine, and Multi-Layer Perceptron). The model created by the CNN-PdM framework achieved accuracy rates as high as 98% for one of the datasets and 95% for the other.