Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs

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.

Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

“…In summary, our proposed architecture of a GNN for time series regression contains the following main contributions compared to previous work, as we will detail below: 1. To obtain node features, we apply a 1D CNN for feature extraction on the individual nodes using a wide kernel [9,41] on the input data as in [16]. 2.…”

“…In the second block of our model, two 1D CNN layers act as feature extractors by using wide kernel sizes, small strides, increasing filters, regularization of λ = 10 −4 and a ReLU activation function, which has proven to be useful for 1D time series data [16,41]. The function of these CNN layers is to learn the temporal patterns of each station.…”

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

“…Therefore, an important core technology of smart machinery is how to improve equipment activation through intelligent sensing and fault diagnosis prediction, and how to reduce the risk of downtime due to equipment failure. There are many types of mechanical equipment failures, such as abrasion and damage of the processing machine's spindle cutter [1][2][3][4][5][6][7], the abnormal damage of the bearing [8][9][10][11][12] or gearbox of the rotary machinery due to the harsh environment, rotational instability caused by mechanical failures of the power generator, etc. Therefore, accurate prediction of mechanical failures will reduce production losses, a key factor, and condition for the efficient production of smart machinery.…”