With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components. This seriously impedes the practical application of data-driven diagnosis. In comparison, the full-labeled machine signals from test rigs or online datasets can be achieved easily, which is helpful for the diagnosis of real equipment. Thus, we introduced an improved Wasserstein distance-based transfer learning method (WDA), which learns transferable features between labeled and unlabeled signals from different forms of equipment. In WDA, Wasserstein distance with cosine similarity is applied to narrow the gap between signals collected from different machines. Meanwhile, we use the Kuhn-Munkres algorithm to calculate the Wasserstein distance. In order to further verify the proposed method, we developed a set of case studies, including two different mechanical parts, five transfer scenarios, and eight transfer learning fault diagnosis experiments. WDA reached an average accuracy of 93.72% in bearing fault diagnosis and 84.84% in ball screw fault diagnosis, which greatly surpasses state-of-the-art transfer learning fault diagnosis methods. In addition, comprehensive analysis and feature visualization are also presented.
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http://dx.doi.org/10.3390/s21134394 | DOI Listing |
ISA Trans
December 2024
Faculty of Mechanical and Civil Engineering, Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, Kraljevo 36000, Serbia. Electronic address:
When the fault diagnosis datasets contains noise disturbances, small samples, compound faults, and mixed conditions, the feature extraction capability of the neural network will face significant challenges. This paper proposes an end-to-end multi-scale residual network with parallel attention mechanism to address the above complex problems. Firstly, the adaptive mixing pooling method is employed to facilitate the model's ability to retain effective feature information present within the timing signal.
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December 2024
Department of Computer Engineering, Inha University, Incheon 22212, Republic of Korea.
The degradation of clamping force in the core support barrel, which forms the internal structure of a nuclear power plant, has the potential to significantly impact the plant's safety and reliability. Previous studies have concentrated on the detection of clamping force degradation but have been constrained in their ability to identify the precise size and position. This study proposes a novel methodology for diagnosing the size and position of clamping force degradation in core support barrels, combining deep-learning techniques and dynamic time warping (DTW) algorithms.
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December 2024
Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chia-Yi 621301, Taiwan.
This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load.
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November 2024
Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China.
Railway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper introduces a novel sensorless monitoring scheme that leverages stator current to detect fault-related characteristics, eliminating the need for additional sensors. This approach employs a hybrid signal preprocessing algorithm that integrates adaptive notch filtering (ANF) with envelope spectrum analysis (ESA) to effectively sparse the stator current and extract relevant fault features.
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November 2024
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG).
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