Prediction of Motor Failure Time Using An Artificial Neural Network

Abstract

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

“…A methodology was proposed by Sampaio, G.S. et al [47] to treat and convert the collected data of vibration measurements from a vibration system that simulated a motor and to build a dataset in order to train and test an ANN model capable of predicting the future condition of the equipment, predicting when a failure can happen. The methodology involves the use of frequency and amplitude data by classifying the dataset and defining a way of calculating the vibrating system's failure time.…”

“…They have been successfully applied in several field of PdM applications. Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique.…”

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

“…The downside is that it is a black box, meaning low interpretability. Sampaio et al [31] found that the MLP architecture worked effectively on non-linear and complex systems, furthermore it had great generalisation. On the downside, the convergence was found to be somewhat slow and that the model had a tendency to overfit.…”

Decision Support System for the Production of Miscanthus and Willow Briquettes

“…The term artificial neural network refers to a computational and machine learning technique [40][41][42][43]. One type of neural networks are multilayer perceptron neural networks (MLPs).…”

“…Many ANN applications are related to renewable energy sources (different uses of ANN models for better energy production predictions). Research addresses for example the creation and use of ANNs to forecast solar radiation (the main problem for the best use of photovoltaic systems) and wind power forecasting [41,45,[57][58][59]. ANNs are applied for forecasting building energy usage and demand [42].…”