Name
Abstract | ||
|---|---|---|
Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time-frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20236886 | SENSORS |
Keywords | DocType | Volume |
fault diagnosis, bearing fault, machine health monitoring, acoustic emission signals, signal decomposition, convolutional neural network, embedded systems | Journal | 20 |
Issue | ISSN | Citations |
23 | 1424-8220 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minh Tuan Pham | 1 | 1 | 0.35 |
| Jong Myon Kim | 2 | 144 | 32.36 |
| Cheol Hong Kim | 3 | 73 | 24.39 |