Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery

Wo Jae Lee (), Kevin Xia (), Nancy L. Denton (), Bruno Ribeiro () and John W. Sutherland ()
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Wo Jae Lee: Purdue University
Kevin Xia: Purdue University
Nancy L. Denton: Purdue University
Bruno Ribeiro: Purdue University
John W. Sutherland: Purdue University

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 2, No 5, 393-406

Abstract: Abstract The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real-time in the presence of changing conditions in manufacturing activities; this may advance manufacturing competitiveness and sustainability. As a factory becomes highly automated, physical asset management comes to be a critical part of an operational life-cycle. Maintenance is one area where the collection of technologies may be applied to enhance operational reliability using a machine condition monitoring system. Data-driven models have been extensively applied to machine condition data to build a fault detection system. Most existing studies on fault detection were developed under a fixed set of operating conditions and tested with data obtained from that set of conditions. Therefore, variability in a model’s performance from data obtained from different operating settings is not well reported. There have been limited studies considering changing operational conditions in a data-driven model. For practical applications, a model must identify a targeted fault under variable operational conditions. With this in mind, the goal of this paper is to study invariance of model to changing speed via a deep learning method, which can detect a mechanical imbalance, i.e., targeted fault, under varying speed settings. To study the speed invariance, experimental data obtained from a motor test-bed are processed, and time-series data and time–frequency data are applied to long short-term memory and convolutional neural network, respectively, to evaluate their performance.

Keywords: Maintenance; Long short-term memory; Convolutional neural network; Machine condition monitoring; Mechanical imbalance (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-020-01578-x

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