Semi-Supervised Multiscale Permutation Entropy-Enhanced Contrastive Learning for Fault Diagnosis of Rotating Machinery

Zhou, Yuqing; Wang, Hongche; Wang, G. L.; Kumar, Anil; Sun, Weifang; Xiang, Jiawei

doi:10.1109/tim.2023.3301051

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…The type of applications where the MSE can be used are as vast as the applications of each single-scale method, such as the analysis of time series [106,107]; biological signals, such as heartbeats and encephalographics [108][109][110]; image processing [111]; and hydrologic applications [112]. There are some interesting works on improvements around the MSE, as presented in [113], where the authors successfully diagnosed gearbox and milling tool faults. The method utilizes a novel technique that combines MPE with contrastive learning (LE), yielding results that improve the accuracy of traditional entropy-based methods.…”

Section: Multi-scale Entropymentioning

confidence: 99%

Entropy-Based Methods for Motor Fault Detection: A Review

Aguayo-Tapia,

Avalos-Almazan,

Rangel-Magdaleno

2024

Entropy

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In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

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Section: Multi-scale Entropymentioning

confidence: 99%

Entropy-Based Methods for Motor Fault Detection: A Review

Aguayo-Tapia,

Avalos-Almazan,

Rangel-Magdaleno

2024

Entropy

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“…Lau and Ngan reported that failures in bearings contribute to 40%-50% of total motor failures [1], and many such failures leading to safety threats. Therefore, effective methods for identifying bearing failure are of immense importance [2].…”

Section: Introductionmentioning

confidence: 99%

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Hei,

Shi,

Fan

et al. 2024

Meas. Sci. Technol.

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Transfer learning (TL) has demonstrated effective application in diagnosing bearing faults under varying operating conditions. The current TL methods achieve domain alignment by minimizing the variation in the marginal distribution of data between the source and target domains in the feature space. However, this approach yields incomplete similarity, leading to domain shift and a decrease in diagnostic performance. To overcome this problem, this paper proposes a new distance-guided domain adaptation (DGDA) method that consists of two modules: Deep domain adaptive correlation alignment (Deep CORAL) combined with Joint Maximum Mean Discrepancy (JMMD) for guided domain adaptation. Deep CORAL employs nonlinear transformations to synchronize second-order statistical correlations across source and target domains, thus ensuring feature-level alignment between these domains. JMMD is utilized to align the joint distribution of input features and output labels within the activation layer in the deep network, thereby bolstering domain alignment. Building on this, we propose a network structure that merges ResNet and BiLSTM, powered by wavelet kernels, serving as a feature extractor. This structure is designed to learn domain-invariant features and incorporates attention mechanisms to amplify important information while diminishing the impact of redundant data. An analysis of bearing experiments is used to demonstrate the effectiveness of this method, and the proposed method significantly outperforms several popular methods in diagnostic performance.

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“…Common sensors are divided by the type of measurement: (a) mechanical quantities like vibration (a popular choice due to its sensitivity to faults) [1][2][3][4][5], displacement [6], torque [7,8], and angular velocity/position [9,10]; (b) electrical quantities like current [11,12] and voltage [13,14], can reveal issues related to power delivery and motor health; and (c) other signals like temperature (inner/outer) [15,16], sound [17][18][19], and even chemical analysis [20,21] can be valuable for specific fault types. Beyond traditional sensors, recent research explores image-based diagnostics using cameras [22][23][24][25] and signals converted into virtual images [12,[26][27][28][29][30]. This versatility in sensor selection allows for a comprehensive approach to machine health monitoring and fault detection.…”

Section: Introductionmentioning

confidence: 99%

“…This approach was validated on various datasets, including one with three classes (an inner ring fault, an outer ring fault, and a normal condition). Yuqing Zhou et al [29] investigated the diagnosis of rotating machinery using a three-channel RGB image formed by merging the permutation entropy from sensor data. This approach aimed to recognize one of five classes of tool wear (initial wear, slight wear, stable wear, serious wear, and failure).…”

Section: Introductionmentioning

confidence: 99%

Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks

Łuczak

2024

Energies

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Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are proposed. The IMU6DoF-Time2GrayscaleGrid-CNN method converts the time series sensor data into a single grayscale image, leveraging the efficiency of a grayscale representation and the power of convolutional neural networks (CNNs) for feature extraction. The IMU6DoF-Time2RGBbyType-CNN method utilizes RGB images. The IMU6DoF-Time2RGBbyAxis-CNN method employs an RGB image where each channel corresponds to a specific axis (X, Y, Z) of the sensor data. This axis-aligned representation potentially allows the CNN to learn the relationships between movements along different axes. The performance of all three methods is evaluated through extensive training and testing on a dataset containing various operational states (idle, normal, fault). All methods achieve high accuracy in classifying these states. While the grayscale method offers the fastest training convergence, the RGB-based methods might provide additional insights. The interpretability of the models is also explored using Grad-CAM visualizations. This research demonstrates the potential of image-based approaches with CNNs for robust and interpretable machine fault diagnosis using sensor data.

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Semi-Supervised Multiscale Permutation Entropy-Enhanced Contrastive Learning for Fault Diagnosis of Rotating Machinery

Cited by 17 publications

References 21 publications

Entropy-Based Methods for Motor Fault Detection: A Review

Entropy-Based Methods for Motor Fault Detection: A Review

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks

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