Applied Sciences

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18 pages, 2901 KiB

Open AccessArticle

ResnetCPS for Power Equipment and Defect Detection

by Xingyu Yan, Lixin Jia, Xiao Liao, Wei Cui, Shuangsi Xue, Dapeng Yan and Hui Cao

Appl. Sci. 2024, 14(22), 10578; https://doi.org/10.3390/app142210578 - 16 Nov 2024

Viewed by 360

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation [...] Read more.

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model’s overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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22 pages, 5844 KiB

Open AccessArticle

A Fault Diagnosis Method for a Missile Air Data System Based on Unscented Kalman Filter and Inception V3 Methods

by Ziyue Wang, Yuehua Cheng, Bin Jiang, Kun Guo and Hengsong Hu

Appl. Sci. 2024, 14(14), 6309; https://doi.org/10.3390/app14146309 - 19 Jul 2024

Viewed by 834

Abstract

Due to the complexity of the missile air data system (ADS) and the harshness of the environment in which its sensors operate, the effectiveness of traditional fault diagnosis methods is significantly reduced. To this end, this paper proposes a method fusing the model [...] Read more.

Due to the complexity of the missile air data system (ADS) and the harshness of the environment in which its sensors operate, the effectiveness of traditional fault diagnosis methods is significantly reduced. To this end, this paper proposes a method fusing the model and neural network based on unscented Kalman filter (UKF) and Inception V3 to enhance fault diagnosis performance. Initially, the unscented Kalman filter model is established based on an atmospheric system model to accurately estimate normal states. Subsequently, in order to solve the difficulties such as threshold setting in existing fault diagnosis methods based on residual observers, the UKF model is combined with a neural network, where innovation and residual sequences of the UKF model are extracted as inputs for the neural network model to amplify fault characteristics. Then, multi-scale features are extracted by the Inception V3 network, combined with the efficient channel attention (ECA) mechanism to improve diagnostic results. Finally, the proposed algorithm is validated on a missile simulation platform. The results show that, compared to traditional methods, the proposed method achieves higher accuracy and maintains its lightweight nature simultaneously, which demonstrates its efficiency and potential of fault diagnosis in missile air data systems. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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16 pages, 6424 KiB

Open AccessArticle

A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus

by Yangtao Li, Haiquan Liang and Xiaodong Tan

Appl. Sci. 2023, 13(24), 13128; https://doi.org/10.3390/app132413128 - 9 Dec 2023

Viewed by 1087

Abstract

The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of the MVB [...] Read more.

The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of the MVB physical layer can reflect the electrical characteristics and fault characteristics of a network in real time and has the value of assisting the diagnosis of MVB network faults. Based on the characteristics of the MVB physical-layer voltage waveform, this paper studies the optimal sampling rate and feature-extraction algorithm of the MVB voltage waveform and the combination of the data of the data-link layer to assign accurate timestamps to waveform data, and finally presents a design for an MVB dual-mode data-acquisition platform with multiple sampling rates/test conditions. Experimental results show that the proposed algorithm can accurately extract waveform feature information under the optimal sampling rate of 62.5 MHz, which makes the collection of MVB voltage waveform data more reliable and practical. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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20 pages, 3807 KiB

Open AccessArticle

Dynamic Reliability Assessment Method for a Pantograph System Based on a Multistate T-S Fault Tree, Dynamic Bayesian

by Yafeng Chen, Jing Wen, Yingjie Tian, Shubin Zheng, Qianwen Zhong and Xiaodong Chai

Appl. Sci. 2023, 13(19), 10711; https://doi.org/10.3390/app131910711 - 26 Sep 2023

Cited by 5 | Viewed by 1216

Abstract

The operational reliability of rail vehicle pantograph systems is evaluated by transforming T-S multistate fault trees into dynamic Bayesian networks (DBNs), which take into account system multistability, long-lasting operation, dynamic failure, and maintenance recovery. The T-S multistate fault tree structure is constructed by [...] Read more.

The operational reliability of rail vehicle pantograph systems is evaluated by transforming T-S multistate fault trees into dynamic Bayesian networks (DBNs), which take into account system multistability, long-lasting operation, dynamic failure, and maintenance recovery. The T-S multistate fault tree structure is constructed by the content validity ratio and content validity index; the T-S gate rule expressing causal uncertainty is constructed by using fuzzy theory and dependent uncertain ordered weighted averaging expert scoring, and finally, the pantograph T-S multistate fault tree is transformed into a DBN model characterizing the dynamic interaction and time dependence of the system. The dynamic evolution laws of reliability of a pantograph system in maintenance and maintenance-free states over time are inferred, compared and analyzed. The results show that the system availability of a pantograph system decreases continuously during 720 days of operation. The system availability without maintenance decreases to 0.881, and the system availability with maintenance is 0.952. The reliability of a pantograph system can be effectively ensured with maintenance during the operation period; the sensitivity analysis is performed by changing the failure rate of the equipment to 120% or 80%; the fall indicator, the electrical control box, and the elevating bow motor are the weak links in the system, and the impact of fault escalation on the reliability of a pantograph system is analyzed. It is then verified that the system reliability can be further improved by using a preventive maintenance strategy, and the steady-state reliability can be gradually reached, which is about 0.9968, providing a reference for the maintenance of a pantograph system. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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21 pages, 4639 KiB

Open AccessArticle

A Multi-Adversarial Joint Distribution Adaptation Method for Bearing Fault Diagnosis under Variable Working Conditions

by Zhichao Cui, Hui Cao, Zeren Ai and Jihui Wang

Appl. Sci. 2023, 13(19), 10606; https://doi.org/10.3390/app131910606 - 23 Sep 2023

Cited by 2 | Viewed by 1233

Abstract

Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, [...] Read more.

Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, this paper designs a transfer learning model named the multi-adversarial joint distribution adaptation network (MAJDAN) to achieve effective fault diagnosis across operating conditions. MAJDAN uses a one-dimensional lightweight convolutional neural network (1DLCNN) to directly extract features from the original bearing vibration signal. Combining the distance-based domain-adaptive method, maximum mean difference (MMD), with the multi-adversarial network will simultaneously reduce the conditional and marginal distribution differences between the domains. As a result, MAJDAN can efficiently acquire domain-invariant feature information, addressing the challenge of cross-domain bearing fault diagnosis. The effectiveness of the model was verified based on two sets of different bearing vibration signals, and one-to-one and one-to-many working condition migration task experiments were carried out. Simultaneously, various levels of noise were introduced to the signal to enable analysis and comparison. The findings demonstrate that the suggested approach achieves exceptional diagnostic accuracy and exhibits robustness. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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18 pages, 6909 KiB

Open AccessArticle

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

by Yongchao Hui, Yuehua Cheng, Bin Jiang, Xiaodong Han and Lei Yang

Appl. Sci. 2023, 13(16), 9438; https://doi.org/10.3390/app13169438 - 21 Aug 2023

Cited by 2 | Viewed by 1136

Abstract

This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process [...] Read more.

This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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21 pages, 5979 KiB

Open AccessArticle

Instantaneous Square Current Signal Analysis for Motors Using Vision Transformer for the Fault Diagnosis of Rolling Bearings

by Fei Chen, Xin Zhou, Binbin Xu, Zheng Yang and Zege Qu

Appl. Sci. 2023, 13(16), 9349; https://doi.org/10.3390/app13169349 - 17 Aug 2023

Cited by 2 | Viewed by 1649

Abstract

Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received [...] Read more.

Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received widespread attention in recent years. Meanwhile, traditional fault diagnosis methods cannot meet the diagnostic accuracy requirements because of the low signal-to-noise ratio (SNR) of the MCS. Committed to achieving bearing fault diagnosis through MCS, a rolling bearing fault diagnosis method, ISCV-ViT, based on the MCS and the Vision Transformer (ViT) model, is proposed. In particular, a signal processing method based on the instantaneous square current value (ISCV) is proposed to process the MCS directly obtained through a frequency converter into time-domain images. Then, the ViT model is applied for bearing fault diagnosis. Finally, experimental verification is carried out based on the public bearing dataset of Paderborn University (PU) and the bearing dataset of Shenzhen Technology University (SZTU). The analysis of the experimental results demonstrates that the average accuracy of the ISCV-ViT for the two datasets is up to 96.60% and 94.87%, respectively. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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21 pages, 6299 KiB

Open AccessArticle

Weak Fault Diagnosis Method of Rolling Bearings Based on Variational Mode Decomposition and a Double-Coupled Duffing Oscillator

by Shijie Shan, Jianming Zheng, Kai Wang, Ting Chen and Yuhua Shi

Appl. Sci. 2023, 13(14), 8505; https://doi.org/10.3390/app13148505 - 23 Jul 2023

Cited by 2 | Viewed by 1175

Abstract

Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and [...] Read more.

Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and VMD. The influence rule of system initial value on the response characteristics of a double-coupled Duffing system is studied, and the basis for its determination is given. The frequency of the built-in power of the system is normalized, and a variance evaluation standard for the output value of the double-coupled Duffing system for weak fault signals detection is established. In order to solve the interference problem of fault monitoring signals, VMD is proposed to pre-process the fault monitoring signals. The weak fault signal detection method proposed in this paper is tested and verified by simulation signals and rolling bearing fault signals. The results show that the method proposed in this paper can detect the weak fault signal with the lowest signal-to-noise ratio reduced by 2.96 dB compared with the traditional Duffing detection system, and it can accurately detect the early weak fault signal of rolling bearings. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)

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