Abstract
With the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning provides possibilities for industrial diagnostics to achieve improved performance and efficiency. These deep learning applications can be used to automatically extract features during training, eliminating time-consuming feature engineering and prior understanding of sophisticated (signal) processing techniques. This paper extends on previous work, introducing one-dimensional (1D) CNN architectures that utilize an adaptive wide-kernel layer to improve classification of multivariate signals, e.g., time series classification in fault detection and condition monitoring context. We used multiple prominent benchmark datasets for rolling bearing fault detection to determine the performance of the proposed wide-kernel CNN architectures in different settings. For example, distinctive experimental conditions were tested with deviating amounts of training data. We shed light on the performance of these models compared to traditional machine learning applications and explain different approaches to handle multivariate signals with deep learning. Our proposed models show promising results for classifying different fault conditions of rolling bearing elements and their respective machine condition, while using a fairly straightforward 1D CNN architecture with minimal data preprocessing. Thus, using a 1D CNN with an adaptive wide-kernel layer seems well-suited for fault detection and condition monitoring. In addition, this paper clearly indicates the high potential performance of deep learning compared to traditional machine learning, particularly in complex multivariate and multi-class classification tasks.
Highlights
In the current industrial era, manufacturers rely more and more on the use of sensors for data collection and analysis
This paper investigates the use of deep learning in the context of fault detection of rolling bearing elements and builds on our earlier research [11], exploring the usage of one-dimensional convolutional neural network (CNN) for classifying multivariate signals based on data derived from rotating machines
We look at the generalizability of these techniques, their performance with limited training data and compare them to traditional machine learning approaches such as knearest neighbors, random forests and support vector machines
Summary
In the current industrial era, manufacturers rely more and more on the use of sensors for data collection and analysis. These developments boost the industry towards newer standards as what is called Industry 4.0 [1,2]. These new approaches for improving performance and increasing production efficiency require scalable methods to process and explain the collected (often complex) data such as multivariate time series. Due to the improved availability of large datasets derived from sensors, these automated learning techniques, e.g., deep learning, provide strong performance in signal classification tasks. This work focuses on such approaches and provides specific enhancements for accurately distinguishing signals in fault detection and condition monitoring applications
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
