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Artificial-Intelligence-Enhanced Fault Diagnosis and PHM

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 4567

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Special Issue Editors

Prof. Dr. Baoping Cai

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Guest Editor

College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao 266580, China
Interests: offshore oil and gas equipment technology; reliability theory and method; intelligent fault diagnosis theory and technology
Special Issues, Collections and Topics in MDPI journals

Dr. Haidong Shao

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Guest Editor

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Dr. Dongming Fan

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Guest Editor

School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: smart maintenance; reliability modeling and simulation; prognostic and health management

Special Issue Information

Dear Colleagues,

In recent years, condition monitoring, diagnosis, and prognostics and health management (PHM) based on artificial intelligence (AI) has become a special research interest. It is a dynamic and valuable research goal to diagnose, predict, and maintain the faults in equipment by using an AI-enhanced algorithm combined with monitoring information, which has broader application prospects. We invite you to submit your contributions to the upcoming Special Issue, which covers all aspects of AI-enhanced diagnosis and PHM. Full-length papers, communications, and reviews are welcome.

This Special Issue aims to collect the progress in basic research, technological development, and innovative application of diagnosis and PHM combined with AI, including sensor information monitoring and collection, fault diagnosis, fault prediction, maintenance decision making, etc. The reviews must provide a key overview of the latest technologies related to the technology and application of diagnosis and PHM.

The topics of interest include, but are not limited to, the following:

Optimization of sensor network layout;
AI-enhanced state information monitoring;
Micro-fault identification using neural network;
Fault diagnosis algorithm with high sensitivity;
Fault prediction of long time series;
AI-enhanced remaining useful life prediction;
Update and optimization of maintenance decision.

Prof. Dr. Baoping Cai
Dr. Haidong Shao
Dr. Dongming Fan
Guest Editors

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Keywords

optimization of sensor network layout
AI-enhanced state information monitoring
micro-fault identification using neural network
fault diagnosis algorithm with high sensitivity
fault prediction of long time series
AI-enhanced remaining useful life prediction
update and optimization of maintenance decision

Published Papers (5 papers)

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17 pages, 6029 KiB

Open AccessArticle

Industrial Product Surface Anomaly Detection with Realistic Synthetic Anomalies Based on Defect Map Prediction

by Tao Peng, Yu Zheng, Lin Zhao and Enrang Zheng

Sensors 2024, 24(1), 264; https://doi.org/10.3390/s24010264 - 02 Jan 2024

Viewed by 1013

Abstract

The occurrence of anomalies on the surface of industrial products can lead to issues such as decreased product quality, reduced production efficiency, and safety hazards. Early detection and resolution of these problems are crucial for ensuring the quality and efficiency of production. The key challenge in applying deep learning to surface defect detection of industrial products is the scarcity of defect samples, which will make supervised learning methods unsuitable for surface defect detection problems. Therefore, it is a reasonable solution to use anomaly detection methods to deal with surface defect detection. Among image-based anomaly detection, reconstruction-based methods are the most commonly used. However, reconstruction-based approaches lack the involvement of defect samples in the training process, posing the risk of a perfect reconstruction of defects by the reconstruction network. In this paper, we propose a reconstruction-based defect detection algorithm that addresses these challenges by utilizing more realistic synthetic anomalies for training. Our model focuses on creating authentic synthetic defects and introduces an auto-encoder image reconstruction network with deep feature consistency constraints, as well as a defect separation network with a large receptive field. We conducted experiments on the challenging MVTec anomaly detection dataset and our trained model achieved an AUROC score of 99.70% and an average precision (AP) score of 99.87%. Our method surpasses recently proposed defect detection algorithms, thereby enhancing the accuracy of surface defect detection in industrial products. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Fault Diagnosis and PHM)

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13 pages, 4464 KiB

Open AccessArticle

by Wenbo Wu, Tianji Zou, Lu Zhang, Ke Wang and Xuzhi Li

Sensors 2023, 23(23), 9535; https://doi.org/10.3390/s23239535 - 30 Nov 2023

Cited by 1 | Viewed by 593

Abstract

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Fault Diagnosis and PHM)

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16 pages, 26560 KiB

Open AccessArticle

Multi-UAV Redeployment Optimization Based on Multi-Agent Deep Reinforcement Learning Oriented to Swarm Performance Restoration

by Qilong Wu, Zitao Geng, Yi Ren, Qiang Feng and Jilong Zhong

Sensors 2023, 23(23), 9484; https://doi.org/10.3390/s23239484 - 28 Nov 2023

Cited by 1 | Viewed by 754

Abstract

Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of vehicle destruction. This paper presents a multi-agent deep reinforcement learning-based DR strategy (DRS) that optimizes the multi-UAV group redeployment in terms of swarm performance. To generate a two-layer DRS between multiple groups and a single group, a multi-agent deep reinforcement learning framework is developed in which a QMIX network determines the swarm redeployment, and each deep Q-network determines the single-group redeployment. The proposed method is simulated using Python and a case study demonstrates its effectiveness as a high-quality DRS for large-scale scenarios. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Fault Diagnosis and PHM)

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22 pages, 7583 KiB

Open AccessArticle

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning

by Niamat Ullah, Zahoor Ahmad, Muhammad Farooq Siddique, Kichang Im, Dong-Koo Shon, Tae-Hyun Yoon, Dae-Seung Yoo and Jong-Myon Kim

Sensors 2023, 23(21), 8850; https://doi.org/10.3390/s23218850 - 31 Oct 2023

Cited by 5 | Viewed by 945

Abstract

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Fault Diagnosis and PHM)

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22 pages, 7235 KiB

Open AccessArticle

A Transfer-Based Convolutional Neural Network Model with Multi-Signal Fusion and Hyperparameter Optimization for Pump Fault Diagnosis

by Zhigang Zhang, Aimin Tang and Tao Zhang

Sensors 2023, 23(19), 8207; https://doi.org/10.3390/s23198207 - 30 Sep 2023

Viewed by 714

Abstract

Pumps are one of the core components of drilling equipment, and their fault diagnosis is of great significance. The data-driven approach has made remarkable achievements in the field of pump fault diagnosis; however, most of them are easily affected by complex background conditions and usually suffer from data scarcity problems in real-industrial scenarios, which limit their application in practical engineering. To overcome the above shortcoming, a novel framework for a model named Hyperparameter Optimization Multiple-Signal Fusion Transfer Convolution Neural Network is proposed in this paper. A convolutional neural network model based on transfer learning is built to promote well-learned knowledge transfer over different background conditions, improve robustness, and generalize the model to cross-domain diagnosis tasks. The multi-signal fusion strategy is involved in capturing system state information for establishing the mapping relationship between the raw signal and fault pattern by integrating the multi-physical signal with the weight allocation protocol. The hyperparameter optimization method is explored in conjunction with the transfer-based model by integrating Grid Search with the Gradient Descent algorithm for further improvement of diagnosis performance. Results show that the proposed model can effectively realize the fault diagnosis of pumps under different background conditions, achieving 95% accuracy. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Fault Diagnosis and PHM)

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