Abstract
The ascent of Industry 4.0 and smart manufacturing has emphasized the use of intelligent manufacturing techniques, tools, and methods such as predictive maintenance. The predictive maintenance function facilitates the early detection of faults and errors in machinery before they reach critical stages. This study suggests the design of an experimental predictive maintenance framework, for conveyor motors, that efficiently detects a conveyor system's impairments and considerably reduces the risk of incorrect faults diagnosis in the plant; We achieve this remarkable task by developing a machine learning model that classifies whether the abnormalities observed are production-threatening or not. We build a classification model using a combination of time-series imaging and convolutional neural network (CNN) for better accuracy. In this research, time-series represent different observations recorded from the machine over time. Our framework is designed to accommodate both univariate and multivariate time-series as inputs of the model, offering more flexibility to prepare for an Industry 4.0 environment. Because multivariate time-series are challenging to manipulate and visualize, we apply a feature extraction approach called principal component analysis (PCA) to reduce their dimensions to a maximum of two channels. The time-series are encoded into images via the Gramian Angular Field (GAF) method and used as inputs to a CNN model. We added a parameterized rectifier linear unit (PReLU) activation function option to the CNN model to improve the performance of more extensive networks. All the features listed added together contribute to the creation of a robust future proof predictive maintenance framework. The experimental results achieved in this study show the advantages of our predictive maintenance framework over traditional classification approaches.
Highlights
The recent explosion of smart manufacturing applications, the Internet of things (IoT), and big data has considerably increased the amount of data collected and analyzed in different areas such as health care, transportation, power energy, food and beverage, multimedia, environment, finance, and logistics
We focus on the Gramian Angular Field (GAF) method for image encoding since it preserves the temporal correlation of time series data inputs which is needed for our predictive maintenance framework
Experimental results obtained show the relevance of using deep learning algorithms such as Convolutional neural network (CNN) to improve the accuracy of classification models
Summary
The recent explosion of smart manufacturing applications, the Internet of things (IoT), and big data has considerably increased the amount of data collected and analyzed in different areas such as health care, transportation, power energy, food and beverage, multimedia, environment, finance, and logistics. In 2015, [11] initiated an inventive approach that improved classification and imputation by encoding univariate time-series (UTS) data to images and using them as inputs to CNN models. The method was named RPMCNN and was used to perform the classification by transforming 2D images from time-series data received as inputs An approach was introduced by [14] using multivariate time-series (MTS) data as input to a classification of Tool wear for a CNN model. Our research takes a step further on previous work done in the manufacturing sector on this innovative concept by developing an experimental framework that: 1) Generates accurate predictive maintenance flags for conveyor motors by classifying whether observed system parameters inputs are threats or not.
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